Business Problem Solving - College Writers

A 2500-word article.

The assignment must be completed after reading the assignment requirements (7027-as3) and referring to the course textbook.
The attached PPT (7027-AS1) is related to the previous assignment and must be referenced. The content and ideas of the two assignments must be related.
If the delivered assignment does not meet the requirements, I will require unlimited revisions.
If you cannot accept the above requirements, please do not quote, otherwise, I will request a refund.
The references in the paper must not overlap with the references provided in the PPT.
The paper should be written using both APA and Harvard referencing formats.

Section

Learning sources

1. The business client and their business problem (10%, 250 words):

· A brief summary and history of your client, including decision-maker who has commissioned this project.

· A short description of the situation that prevails for your client at the outset of problem solving (i.e., the state of affairs that are problematic).

· Provide clear evidence of the business problem, ideally quantifying the problem and illustrating it graphically.

· A set of observations or complications around the situation that creates the tension or dynamic that captures the problem (i.e., what changed or what went wrong that created the problem).

· In the form of an objective (e.g., To reduce Coca-Cola’s plastic waste by 50% by 2026 without sacrificing profit margin), define a specific, measurable and actionable problem.

Topic 1.2

Topic 1.3

Conn & McLean (2019) Chapter 1

Conn & McLean (2019) Chapter 2

2. Problem structure and components logic tree (20%, 500 words):

· Use an initial logic tree (i.e., factor/lever/component) to break the problem into component parts or issues (e.g., causes of the problem) to illustrate and define the basic structure of the problem.

· This should be evidence-based, using a combination of credible industry and academic literature, evidence and theory, covering the problem generally (based upon the academic literature) and the problem in the context of your client (based upon the industry literature).

· Provide a fully-referenced commentary of the logic tree.

· It is expected that this logic tree will have three layers – branches should expand at each layer.

Topic 2.1

Conn & McLean (2019) Chapter 3

3. Solution drivers and hypothesised solutions logic tree (30%, 750 words):

· Using the basic problem structure logic tree as a guide to locate further industry and academic literature, evidence and theory, produce a more complete logic tree (i.e., deductive logic, hypothesis or hybrid of the two) of:

a. solution drivers, which help us to see potential pathways to solve the problem,

b. concluding with your hypothesised solutions as the leaves of your logic tree.

· Provide a fully-referenced commentary of the logic tree.

· It is expected that this logic tree will have four layers – branches should expand at each layer, although not necessarily for the fourth layer of hypothesised solutions.

Topic 2.1

Conn & McLean (2019) Chapter 3

4. Prioritisation of hypothesised solutions (20%, 500 words):

· Using the prioritisation matrix, consider all of the hypothesised solutions from the leaves of your second logic tree to prioritise those that have the biggest impact on solving the problem and which you can most affect to find the critical path to solving your problem.

· Prune the tree to remove the ‘leaves’ that are not on the critical path to solving the problem, establishing the hypothesised solutions that will be taken forward to be workplanned.

· Provide a fully-referenced commentary of the prioritisation matrix.

Topic 2.2

Conn & McLean (2019) Chapter 3

5. Workplan (15%, 375 words):

· Starting from the prioritised hypothesised solutions established in the previous step, propose a workplan for how you will test your hypothesised solutions and inform their implementation via data collection and analyses, so to be able to reach a conclusion on the solution to the problem.

· For each prioritised hypothesised solution identify the following columns in a chunky workplan:

a. a research question that will guide data collection and analysis to test each hypothesised solution and inform their implementation,

b. the data required and how you will access or collect it,

c. the data analysis techniques you will use,

d. timing of this work and

e. the anticipated analysis end product (e.g., a graph).

· Using a Gantt chart, produce a lean project plan covering key activities and fixed milestones of your proposed project over a three month period of work.

Topic 2.3

Topic 3.1

Topic 3.2

Conn & McLean (2019) Chapter 4

Conn & McLean (2019) Chapter 5

Conn & McLean (2019) Chapter 6

6. One-day answer (5%, 125 words)

· Conclude your problem-solving project proposal with a one-day answer to convey what understandings are emerging, what unknowns still stand between you and the problem resolution and your best guess at a resolution.

Topic 2.3

Conn & McLean (2019) Chapter 4

Defining problems: Understanding the business problem and its context

Dr. Stephen Hills

Learning objectives

To be able to define a specific problem.

To be able to reframe a problem.

To be able to sharpen a problem statement.

Problem definition

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Pitfalls and common mistakes

Weak problem statements: Vague problem statements that are not specific in terms of:

Establishing what is required to make a decision on solving the problem

The action that will follow the problem being solved

Constraints of the problem-solving

Time frame and level of accuracy required for the problem to be solved

Step 1: Define the problem

Good problem definition is essential.

Specific

Measure success

Time bounded

Meets values of the decision maker

Problem Definition

Crystal clear definition of the problem you are solving is essential. A quote from Einstein:

“If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and five minutes thinking about solutions.”

Einstein believed the quality of the solution you generate is in direct proportion to your ability to identify the problem you hope to solve.

You need to be very clear about the boundaries of the problem

the criteria for success

the time frame

The level of accuracy required.

Problem statements

Characteristics of good problem statements

Outcomes focused: A clear statement solved, expressed in outcomes, not outputs.

Specific and measurable wherever possible.

Clearly time-bound.

Designed to explicitly address decision-maker values and boundaries, including the accuracy needed and the scale of aspirations.

Structured to allow sufficient scope for creativity and unexpected results— too narrowly scoped problems can artificially constrain solutions.

Solved at the highest level possible, meaning for the organisation as a whole, not just optimised for a part or a partial solution.

SMART – specific, measurable, action oriented, relevant, and timely.

SMART Goals – specific, measurable, achievable, relevant, and time-bound.

Constraints of a project: The iron triangle

All projects have certain constraints — normally these are cost, time and scope.

Project managers work within these three project constraints.

Any changes to one of these variables will impact the others.

For a project to be successful, these three factors need to be balanced.

Boundaries focused

The Project Manager

I need to finish on time and to budget and deliver the full scope.

Outcomes focused

The Iron Triangle

The Client

It’s all about the outcomes.

A tool for defining problems: The problem definition worksheet

Decision makers

Success criteria

Key forces acting on the decision makers

Time frame for resolution

Boundaries/constraints

Accuracy necessary

Case: Reversing the decline in Wild Pacific Salmon

Reversing the decline in Wild Pacific Salmon: The Client

Wild Atlantic Salmon had seen huge declines from mismanagement that caused large-scale ecosystem harm and community economic losses.

Wild Pacific Salmon now under pressure from human development in land use and fisheries management approaches that negatively affect salmon numbers and salmon habitats.

Importance: Wild Pacific Salmon are an apex species critical element in the northern rainforest ecosystems, a substantial biomass in their own rights, which have a massive impact of freshwater and marine ecosystems.

The client were a new foundation committed to a long-term model of philanthropy, focusing and funding a few initiatives fitting the following criteria:

Initiatives with measurable outcomes.

Initiative that are important and really matter.

Initiatives where the foundation’s unique contribution mattered.

Initiatives that over time would contribute to a portfolio effect – building off and supporting each other.

The project team could have up to 15 years to work on the problem with substantial financial resources.

Porter & Kramer’s Theory of Shared Value

Initiative that are important and really matter.

Initiatives where the foundation’s unique contribution mattered.

Reversing the decline in Wild Pacific Salmon: Problem Constraints

Quick results.

Measurable ecosystem-level outcome improvements over time.

Grassroots advocacy campaigns and large-scale direct policy efforts were undesirable and, therefore, off limit.

Problem Definition Worksheet Example

Counting fish: Evolution of the problem statement

The foundation was committed to initiatives with measurable outcomes, which it was initially felt fit with tackling the declining number of Wild Pacific Salmon.

However, there are five different species in several different regions and some species are doing well in some places, others not so well.

Overall numbers go up and down throughout the year due to ocean conditions.

It is highly challenging to measure the number of Wild Pacific Salmon and to determine the impact of an initiative to reverse the decline of Wild Pacific Salmon.

However, the functioning of the North Pacific Salmon ecosystem is more measurable – looking at their food availability and habitat.

As such, the problem statement evolved to reflect this.

Problem Statement Evolution

Reframing the problem

Define problems with sufficient scope and at the highest-level

Narrowly scoped projects make for fast problem solving, but provide limited space for creative and novel solutions, employing only conventional conceptions of a problem.

Breakthrough ideas are more challenging with old models and old framing of problems.

Target your problem solving efforts at the highest level at which you can work, rather than single business units because what makes sense for single business unit may not make sense for the company overall.

Case: Reducing HIV infections in India

The Avahan India AIDS Initiative

Growing concern about the spread of HIV in India in 2003 led to the Avahan India AIDS Initiative being funded by the Bill and Melinda Gates Foundation.

The traditional public health lens of supply and demand was initially used:

Supply: Using data from front line health workers, they determined how many condoms needed to be distributed.

Demand: Raising awareness of the need to use protection.

Research with sex workers established that there was a high correlation between violence and sexually transmitted infections – men demanding unprotected sex.

The problem was reframed to incorporate reducing sexual violence against female sex workers.

Solution: Rapid response teams of community workers, lawyers and newspaper reporters.

Empowered sex workers to insist on condom use for their clients.

An article published in the Lancet Global Health journal estimated the initiative prevented over 600,000 HIV infections.

Sharpening the problem statement

Problem statements keep on getting better when facts are brought to bear to sharpen the problem definition.

Set up a dialogue with the client or other stakeholders.

For example, the problem statement for underinvestment in capacity building by nonprofits was sharpened when it was understood that such underinvestment was particularly the case:

For small organisations (< 50 employees) with little discretionary funding for capacity building.

Where the service delivery was in question and systems complex.

The problem statement was sharpened to focus on small nonprofits and their need to update their delivery models for a complex systems environment.

Conclusions

Defining the problem well is the starting point for great problem solving because a well-defined problem is a problem half solved.

Problem definition requires understanding the boundaries of the problem, the timeframe for solution, the accuracy required and any other forces affecting the decision.

Take an opposing view to test the robustness of the problem statement.

Bring creativity into problem definition by reframing the problem.

Sharpen your problem statement as you learn more about the problem.

Homework: Think about and research potential clients and problems

Who makes a good client?

A high profile organisation for whom there is a lot of readily available information.

An existing or former employer for whom you know a lot about their business.

An organistaion with whom you have personal connections, such as family or friends in senior positions, who are willing to share information with you.

What makes a good business problem for MN7027 and MN7P13?

“Problem solving is decision making when there is complexity and uncertainty that rules out obvious answers, and where there are consequences that make the work to get good answers worth it.”

Conn & McLean (2018)

A problem for which there is complexity and uncertainty that rules out obvious answers.

A problem for which, if solved, there are consequences that make the work to get good answers worth it.

Where can I find ideas for a client and business problem?

BBC News Business or business sections of other credible news outlets.

Workshop: Problem definition worksheet

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Disaggregating problems: Breaking up the business problem into manageable parts

Dr. Stephen Hills

Learning objectives

To be able to break up problems into manageable parts.

To be able to break problems up to see the structure of the problem.

To be able to appropriately choose between different types of logic trees.

Disaggregating problems

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Step 2: Disaggregate the issues

Use a logic tree to disassemble the problems into parts

Use theoretical frameworks to understand drivers of the problem solution

Problem disaggregation

Any problem of real consequence is too complicated to solve without breaking it down into logical parts that help us understand the drivers or causes of the problem.

We need to take the problem apart in a way that helps us to see potential pathways to solve it.

Taking the problem apart to see all of its parts clearly also allows us to determine what not to work on:

The problem components or issues that are too difficult to change (i.e., that can be actively managed).

The problem components or issues that don’t impact the problem sufficiently.

Literature reviews and theoretical frameworks

A literature review (e.g. Google Scholar search) of the facets of the problem will provide insight into the different ways that a problem can be broken up.

Using knowledge from the literature you can develop a theoretical framework of the drivers or causes of the problem.

From this we can develop hypotheses of pathways to the solution – our evidence-based predictions of potential pathways to a solution that we can go on to test.

Logic trees

Slot pricing favouring larger planes

Light aircraft policy

Reduce further the times between take-offs and landings

Difficult to change because of noise

Example: Logic tree for increasing capacity at Sydney airport

Logic trees

Structures for seeing elements of a problem clearly.

Schemas that provide a visual mental map of the different levels of a problem.

Clear logic of relationships linking component parts of the problem to each other.

Different ways to disaggregate a problem

The task of planning to build a brick wall can be seen as either a process or as the sum of its components —both yield different insights and are helpful for visualising the task of building the wall.

Types of logic trees

Early in the process we start with factor/level/component trees to help us define basic problem structure.

Later in the process we move to hypothesis trees, deductive logic trees or decision trees, depending on the nature of the problem, so to drive analysis or action.

Factor/lever/component logic trees

At the start of this step, when you are able to state your problem clearly but don’t yet have a detailed understanding of it, you should employ the simplest kind of logic tree.

Start with the most obvious elements that make up a problem – components that can help focus data gathering.

A logical first disaggregation can usually be achieved with a small amount of Internet research.

Pacific Salmon Case: From initial component tree to refined hypothesis tree

Initial factor/lever/component logic tree

A rudimentary factor/lever/component logic tree was developed to get a hold of the problem – to get a grasp of all the elements and relationships that defined the problem space.

For several days (not weeks) undertook readings about the Salmon problem and talked to experts in salmon conservation.

Just enough initial research to generate a first-cut tree, which would then act as a guide to make further research more efficient.

First-cut logic tree: Factor/lever/component tree

The big levers that affect salmon and the secondary tertiary layers of the problem without judgment of importance or magnitude of levers or which could be actively managed (i.e., affected by the grant funding).

First-cut logic tree: Deficiencies

Does not show the importance or magnitude of any lever, or which ones grant funding could affect so does not support the development of hypotheses to guide data gathering, analysis and prioritisation.

Does not address the substantial regional differences in which factors are important so it doesn’t show the magnitude impact of each lever by region.

Creates a significant confusion in the government policy element, which is shown as a separate topic area, rather than as it truly plays out by affecting each lever from watershed protections to fisheries to artificial propagation.

This tree confuses or overlaps some of its branches, so it is not mutually exclusive and collectively exhaustive (MECE).

MECE: Logic trees should have branches that are…

Mutually Exclusive	The branches of the tree don’t overlap, or contain partial elements of the same factor or component. The core concept of each trunk or branch of the problem is self-contained, not spread across several branches.
Collectively Exhaustive	Taken as a whole, the logic tree contains all of the elements of the problem, not just some of them. Missing parts could lead to missing solutions to the problem.

Hypothesis logic trees

Second-cut logic tree: Hypothesis tree

After a literature review and other in-depth research, it is possible to refine a logic tree and transition from a simple factor/lever/component logic tree to a hypothesis tree – predictions of solutions that need to be tested.

Second-cut logic tree: Improvements

Better organized.

Mutually exclusive & collectively exhaustive.

Focuses analysis on both specific regions & intervention types.

Initial hypotheses to push for some early outcomes (i.e., achieve some traction).

Deductive logic trees

Appropriate for when you have a very clear idea of the problem structure, which is logically or mathematically coherent.

Use deductive reasoning (a.k.a. top-down reasoning) that argue from general rules or principles to conclusions via more specific data and assertions.

General statement: All LMU MBA students need a minimum of a 2.2 for an honours degree (or equivalent) to enter the programme.

Specific observation: Priyanka is a LMU MBA student.

Deductive conclusion: Priyanka has a minimum of a 2.2 for an honours degree (or equivalent).

Deductive logic trees are constructed similarly, with a problem statement that may sometimes be expressed in quantities, and branches that are typically logically or mathematically complete, so that the components add up to the desired objective of the problem statement.

You can use this kind of tree when you know a lot about the logical structure of a problem and especially when the cleaving frame is inherently mathematical.

Case: Improving nursing-related patient outcomes

Nurses provide at least 90% of patient care in hospitals.

Over 100k lives a year are lost in the USA from mistakes in patient care in hospitals.

There is a substantial shortage of nurses, resulting in more patients per nurse (or fewer nurses per patient).

For each patient added per nurse, mortality rates increase.

Deductive logic tree: Improving nursing outcomes

This problem is suited to a deductive logic tree because it is logically complete:

Increasing number of skilled new nurses

Improving skills and practices of current nurses

…adds up to the desired outcome.

Case: Improving nursing-related patient outcomes

Focuses attention on the key drivers of nursing numbers and skill levels, moving from general rules or principles to conclusions via more specific data and assertions.

Data and analysis were used to determine which levers were most powerful in improving patient outcomes and which were cost-effective to address.

After 12 years of investment, more than 4.5k registered nurses were added, nursing school curriculums improved, bloodstream infections and readmission rates reduced and 1k lives saved a year from sepsis.

Inductive logic trees

Appropriate for when we do not yet know much about the general principles behind the problem, but we do have some data or insights into specific cases.

Use inductive reasoning (a.k.a. bottom-up reasoning) that argue from specific observations towards general principles.

Specific observation 1: Priyanka is a LMU MBA student and has a minimum of a 2.2 for an honours degree (or equivalent).

Specific observation 2: Michael is a LMU MBA student and has a minimum of a 2.2 for an honours degree (or equivalent).

Specific observation 3: Sarah is a LMU MBA student and has a minimum of a 2.2 for an honours degree (or equivalent).

Inductive assertion: LMU MBA students typically need a minimum of a 2.2 for an honours degree (or equivalent) to enter the programme.

Inductive logic trees show probabilistic relationships, rather than causal relationships.

Although you are primarily working from the specific to the general, there will likely also be some deductive thinking about the drivers and general principles, where you work from both the trunks of the trees and the leaves.

Case: How to address artifacts of figures with contested and difficult historical legacies?

Historical artifacts that chronicle or memorialise historical figures whose views are out of step with modern day values.

Even some of our heroic historical figures (e.g., Churchill, Gandhi) held views incompatible with modern values.

An inductive reasoning exercise was undertaken to look at a range of historical figures about whom there is rich information and consensus on what people think about individuals.

By working backwards from what people think about individuals, some general principles of judgment became apparent.

This established a list of threshold questions that underpinned the judgments or assessments, but no clear hierarchy of what questions were most important.

Which of These Questions Are Most Important?

Inductive reasoning exercise

Decision tree: How to address artifacts of figures with contested and difficult historical legacies?

By combining reasoning about contested individuals with reference to fundamental moral rules or principles it was possible to generate a decision tree to guide action in a more systematic way.

Conclusions

Problem disaggregation provides us with manageable chunks to work on and allows us to begin to see the structure of the problem.

Start with simple factor/compoenent/lever logic trees when you are starting out and don’t know a lot.

Use those top guide your research then move on to more complete logic trees using hypothesis, deductive and decision trees.

You can also work backwards with inductive logic trees when you know more about the detailed issues (i.e., the leaves) than you do about the root causes.

Your logic tree structures should be both mutually exclusive (i.e., no overlapping branches) and collectively exhaustive (i.e., no missing branches).

Workshop: Help a struggling local restaurant

A local restaurant has approached you asking for consulting advice as to how to grow their business.

The business has recently taken a downturn as number of customers have dropped and their energy costs have increased.

Working in pairs write a problem statement (i.e., a question or objective) and draw an initial logic tree (i.e., a factor/lever/component logic tree).

The levers of profit

Increase the number of customers

Increase the number of transaction per customer

Increase the value of each transaction

Increase margin

Raise your price

Reduce your costs

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Synthesising findings: Turning analysis into a compelling narrative

Dr. Stephen Hills

Learning objectives

To be able to synthesise your analysis to highlight insights.

To structure a compelling narrative of your synthesised findings to communicate them in a way that motivates action.

Sythesising results and telling a great story

These final two steps are the culmination of your problem solving project and should provide a solution to your problem.

They are your conclusions and should be an engaging story supported with facts, analyses and arguments that convince your audience of the merits of your recommended solution.

Project structure

Definition of the problem

Disaggregation of the problem

Prioritisation of the problem

Workplan

Analysis and findings

Synthesis of findings and solutions

Synthesising results

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Step 6: Synthesise findings from the analysis

Assemble findings into a logical structure

Synthesise in a way that convinces others

Synthesising findings

Synthesis of your data gathering and analysis.

Synthesis: Combining components or elements to form a connected whole.

As you move to final synthesis, draw together the individual findings of the work on each branch of your logic tree into an overall picture.

Represent each of your findings in the form of pictures or graphics that highlight the insights that emerged from your work.

Implications and reasons to drive action

Evidence-based action.

What should we do and how should we do it?

Compelling narrative

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Step 7: Prepare a powerful communication

Develop a storyline from the conclusions

Governing thought or argument derived from the situation –observation – conclusion

Support with synthesised findings and assembled into component arguments

Lead with action steps or pose a series of questions that motivate action.

Telling compelling stories

Once you have synthesized your findings into a series of convincing graphics, the final step is to structure a compelling communication for your audience.

Return to your problem definition worksheet and remind yourself:

What problem are we trying to solve?

Has the problem evolved during project (e.g., as new information comes to bear? If so, how?

Did the boundaries change (e.g., to allow for a more creative solution)? If so, how?

What are the key criteria for success? These should be explicitly reflected in our story.

Pyramid structure

The pyramid structure helps us to show clearly how each element of our argument is supported by data and analysis.

At the very top level is our lead or governing statement of the problem.

Final one-day solution – your latest situation-observation-resolution statement.

Using insights from your synthesis stage, fill in supporting arguments that back up your top-level answer.

Structure options

Choose an appropriate structure depending on the nature of your answer and your audience.

Argument types: Deductive and inductive

Arguments can be made both deductively and inductively:

Deductive: From a general principle to observations (data) to a conclusion.

Inductive: From individual observations to general conclusions.

Case: Hechinger Draft Storyline

The following example looks at the complete narrative for Hechinger.

It draws together evidence from the analysis phase into a synthesis of the findings and then tells the story: Hechinger needed to change its business model quickly to address the competitive threat of Home Depot.

The whole story is on a single page with the governing thought and call to action at the top.

Resolution – situation – observation.

Underneath are the three major arguments that underpin the governing thought.

Then underneath these are the supporting arguments and data that provide the proof for the need for action and the formula for change.

1 – Home Depot Advantage

2 – Sales and Operating Income

3 – Store Openings

Draft Storyline

1983 – 1988

Case: Oilco

Recommendation was for the refinery business to cut costs substantially and become a modest growth, niche operation.

Communicated via a revealed approach – did not lead with the resolution.

Case: Oilco

Using a decision tree final storyline structure, you can provide evidence for each yes/no branch in your tree, slowly working the decision maker toward your solution.

You reveal the answer, rather than leading with it.

Revealed compelling competitor data, layer by layer, so to get comfortable with difficult conclusions.

Conclusions

Synthesis brings together all the separate pieces of your analytic work in a way that highlights your insights.

Revisit your original problem definition and answer your decision maker’s question – what should I do? – in a compelling way that motivates action.

Use the logic tree pyramid structure to organise a compelling story.

The pyramid structure helps to structure arguments and support into a powerful story.

Your final one-day answer structure (leading with resolution, then situation and observation) can be used as the governing thought of your narrative.

Try several storyline structures to see which are most clear and compelling, such as a decision tree format to reveal the answer step-by-step.

Workshop: McKinsey & Co’s Insurance in 2030

Step 1: Define the problem

Which three MBA programmes should I apply to and in what order?

Constraints:

Assumes you have already concluded that you are going to undertake postgraduate study and that an MBA is the course you want to study.

You can only apply to three MBA programmes and you should rank them in terms of first, second and third choice.

There is no point applying to MBA programmes for whom you do not meet the entry requirements.

Workshop: McKinsey & Co’s Insurance in 2030

Read the document Insurance productivity 2030: Reimagining the insurer for the future by Mckinsey & Co.

Translate this into a one-page compelling story using the pyramid structure.

Governing thought and call to action at the top (Resolution – situation – observation).

Underneath put the major arguments that underpin the governing thought.

Then underneath these put the supporting arguments and data that provide the proof for the need for action and the formula for change.

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Wicked problems: Unpicking wicked problems

Dr. Stephen Hills

Learning objectives

To be able to recognise wicked problems.

To be able to select and apply bulletproof problem solving tools to tackle wicked problems.

Wicked problems

Problems that are difficult to define and find a solution for because they are impossibly linked to complex systems, which involve:

Multiple causes.

Major values disagreements between stakeholders.

Unintended consequences.

Require substantial behaviour change.

Wicked problems

Where the problem changes shape as a result of an intervention: For example, welfare payments, which can lead to dependency and undermine positive behaviours. A theory of change should be mapped out, including feedback loops so to get incentives right.

Where there is no such thing as a single right answer to the problem: For example, the use of nuclear power in a country’s energy mix. Explore trade-offs between reasonably right answers or least-bad outcomes.

Where values play an important role: For example, gun control in the US is an issue where values play an important role. Problem solving should seek to identify common ground by looking at sources of accidental and homicide deaths from gun ownership which can be addressed with interventions such as mental health and criminal record background checks.

Where the real problem is nested inside other, more apparent problems: For example, shelter is a more apparent problem of homelessness, but efforts should be directed at the underlying social, financial, mental health and domestic violence problems.

Wicked problem: Obesity

Obesity is a global phenomenon with huge economic, social and personal costs, estimated to have a similar social burden of $2 trillion to smoking, armed violence and terrorism.

Obesity is a complex, systemic issue with no single or simple solution.

There is discord on how to move forward, requiring integrated assessments of potential solutions.

Obesity has dozens of contributing causes, including genetic, environmental, behavioural, cultural, societal, income and education dimensions.

Problem definition

Overweight and obese people comprise 30% of the UK adult population, which continues to rise.

The economic burden is $73 billion per year, as of 2012 (smoking is $90 billion).

The direct medical costs are $6 billion per year.

Body mass index (BMI) is positively correlated with healthcare costs.

The UK Government is the decision-maker.

Goal of 20% overweight/obese individuals to reach a normal weight category within 5 years.

Few constraints, with regulatory interventions (e..g., taxes on high-sugar drinks) on the table.

Interventions should be:

Cost effective.

Impactful.

An evidence-base.

Disaggregating the problem

Obesity problem was disaggregated using a supply and demand cleaving frame.

Use of a cost curve provided a menu of opportunities in descending order of cost and size of impact.

Analysis

74 interventions were categorised into 18 groups, including:

High-calorie food and drink availability.

Weight management programmes.

Portion control.

Public health education.

Cost and impact in terms of $ per disability-affected life years were estimated for each intervention,

44 interventions deemed highly cost-effective, impactful and evidence-based were selected.

Estimated that it would cost the UK $40 billion to implement all of the interventions

Cost curve: Setting priorities

Implement the low-cost, high-impact initiatives first, particularly the small wins, before tackling the more divisive and costly initiatives – the widest bars to the left.

Ordering of initiatives will change as more information on cost and impact comes to bear.

Decision makers were urged to act now on the portfolio of interventions, rather than wait for an unlikely silver bullet solution.

Wicked problem: Overfishing

Problem definition

The US West Coast Groundfish fishery off the coast of California had been in decline for some time.

1987 – catch valued at $110 million.

2003 – catch values at $35 million.

Trawling was having a substantial negative effect on habitat and species diversity.

Conventional solutions

The solutions to reduce the negative effects of bottom trawling were difficult to implement.

Conventional top-down regulatory had poor success.

Reasoning by analogy

A potential solution path emerged from land-conservation easements and market transactions in the marine environment.

The Nature Conservatory bought over 50% of the trawl permits from fishermen for $7 million on the condition that they support the establishment of a marine protected zone prohibiting trawling.

The Nature Conservatory then leased the permits back to fishermen with conservation restrictions.

Rather than highly competitive fishing between individuals, a cooperative fishing approach was adopted.

Fishermen shared catches.

Case: Morro Bay Regional Port

A community quota fund was established in Morro Bay regional port.

Morro Bay transitioned from a larger fleet reliant on trawl and large volume to a smaller fleet catching a diversity of species using less damaging methods.

Catch volume was spread out over time, resulting in fishers being able to access higher market values

Conclusions

Some problems are particularly difficult because they are impossibly linked to complex systems, which involve multiple causes, major values disagreements between stakeholders, unintended consequences and require substantial behaviour change.

These problems are harder to solve, but the bulletproof problem solving methodology and its tools can be applied to unlock insights and provide solutions.

Workshop: WeWork team problem solving

Workshop: Team problem solving

Your team has been brought in by WeWork.

Move on to the remaining steps of the bulletproof problem solving process:

Prioritise solution drivers.

Conduct simple analysis on relevant secondary data.

Using a pyramid structure, communicate your synthesised findings.

Prepare to present your story using a pyramid structure in the next class.

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The bulletproof problem-solving approach: A seven-step process

Dr. Stephen Hills

Learning objectives

To understand the importance of problem solving for a MBA student.

To consider a variety of problem solving approaches.

To be able to avoid pitfalls and common mistakes in problem solving.

To gain a high level understanding of the seven-steps of bullet proof problem solving.

To apply the process to a problem that you have recently solved yourself.

Why a module on business problem solving?

Background: Managerial skills evolution

The focus of organizational capability has shifted from strategy to execution to complex problem solving.

70s – 80s: Strategy – Where and how to compete

90s -2015: Execution – Business process redesign, getting things done, assuming correct strategic direction

2015 onward: Complex problem solving – Critical thinking, agile and creative problem solving

World Economic Forum Future of Jobs Report (2020): Top 10 Skills

Complex problem solving

Critical thinking

Creativity

People management

Coordinating with others

Emotional intelligence

Judgment and decision-making

Service orientation

Negotiation

Cognitive flexibility

The importance of problem solving

“Problem solving is decision making when there is complexity and uncertainty that rules out obvious answers, and where there are consequences that make the work to get good answers worth it.”

Conn & McLean (2018)

Problems are larger, more complex, and faster moving than ever before.

In this module you will learn how to define a problem, creatively break it into manageable parts, and systematically work toward a solution.

In business decisions are often made under strong time pressure so there is a need for effective problem solving and the recognition you are likely to be operating with imperfect information.

A problem can be solved in a day, but it may not be the best solution. Equally, taking 6 months to decide may be far too slow.

The importance of problem solving

“It doesn’t matter if you are working in the cafeteria or the inspection line of a plant, companies will only hire people who can see problems and organize responses.”

Brooks (2018)

“The world no longer rewards people just for what they know – Google knows everything – but for what they can do with what they know.”

Csapo & Funke (2017)

The importance of critical thinking

“Critical thinking is the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action.”

(Ennis, 2015)

Critical thinking is very important because everyday as managers, we have an ocean of decisions to make.

We are bombarded with so many decisions that it’s impossible to make a perfect decision every time.

But by analyzing and evaluating information, critical thinking helps us to make better decisions.

Components of critical thinking

To think critically is to be sceptical, to be analytical, to evaluate the information and evidence we see, and to not take things at face value.

To think independently is to be thought leaders with our own original ideas, and to make up our own mind about what to believe or do.

To think with an open-mind is to look at the various sides of an issue, and to recognize our biases and how they influence what we believe or do.

To be fair and reasonable is to come to a conclusion on the basis of objective and logical thinking.

Problem solving approaches

Gemini Consulting Problem Solving Process

Problem Statement

Background

Idea Generation

Selection

Benefits/Concerns

Critical Concerns Identification

Action Plans

Learning

Leading and managing the people issues

Recognition and

starting the process

Diagnosis

Planning

Implementation and reviewing progress

Sustaining the change

Hayes (2018) Model of Change

Managing change involves seven core activities:

Recognising the need for change and starting the change process

Diagnosing what needs to be changed and formulating a vision of a preferred future state

Planning how to intervene in order to achieve the desired change

Implementing plans and reviewing progress

Sustaining the change

Leading and managing the people issues

Learning

Current

state

Future

state

The Change Process

Coaching – The GROW Model – from Coaching for Performance by John Whitmore

Goal – what are you seeking to achieve?

Reality – what is the current situation? An objective evaluation; confronting the facts

Options – maximise the choices, beware of negative assumptions

What – will you do?

“Coaching is unlocking people’s potential to maximise their own performance”.

In other words, to solve the problems that face them such as growing a business or gaining promotion.

Bullet Proof Problem Solving

The bulletproof problem-solving process is both a complete process and an iterative cycle.

This cycle can be completed over any timeframe with the information at hand.

Once you reach a preliminary end point, you can repeat the process to draw out more insight for deeper understanding.

Pitfalls and common mistakes in problem solving

Pitfalls and common mistakes

Weak problem statements: Vague problem statements that are not specific in terms of:

Establishing what is required to make a decision on solving the problem

The action that will follow the problem being solved

Constraints of the problem-solving

Time frame and level of accuracy required for the problem to be solved

Asserting the answer: Proposing a solution without going through the objective process of problem-solving, based upon anecdotal evidence and existing biases:

Availability bias: Drawing only on the facts that you have to hand

Anchoring bias: Relying too heavily on the first information we learn

Confirmation bias: A tendency to search for, interpret, favor, or recall information in a way that confirms or supports our prior beliefs or values.

Actionable questions

What type of new car should I buy?

Where should I live?

How do we reduce the litter in the park?

How do we grow the business?

Does London need a 3rd runway?

Should we invest in HS2?

What organisational structure do we need?

Should we outsource?

Pitfalls and common mistakes

Failure to disaggregate the problem: Not breaking the problem up into its component parts.

For example, the problem of asthma in Sydney was only solved when broken up in terms of frequency and severity.

When looking at the problem in terms of frequency, asthma was consistent across all of Sydney, thus not revealing any insights to solve the problem.

However, when looking at the problem in terms of severity (i.e., deaths and hospitalizations), there were significant differences between areas.

It was already understood that asthma was linked to tree cover and the areas in Sydney where deaths and hospitalizations were greatest had least tree cover.

Therefore, what was the solution?

Pitfalls and common mistakes

Incomplete analytical tool set: Although some issues can be resolved with ‘back of the envelope’ simple calculations, others require sophisticated tools of analysis.

Failing to link conclusions with a storyline for action: Finishing the project after the analysis is complete, without synthesizing your findings and communicating to diverse audiences.

For example, the problem of the decline of the bee population can get lost in the technical language used in describing the important role that bees play in pollination, but becomes more compelling when linked back to humans, so to stimulate action.

Failing to link conclusions with a storyline for action:

The seven-steps of bullet proof problem solving

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Step 1: Define the problem

Good problem definition is essential.

Specific

Measure success

Time bounded

Meets values of the decision maker

Step 2: Disaggregate the issues

Use a logic tree to disassemble the problems into parts

Use theoretical frameworks to understand drivers of the problem solution

Step 3: Prioritise the issues, prune the tree

Identify which branches of the logic tree have the biggest impact on the problem and which you can most affect

Aim to make the best use of team time and resource

Step 4: Build a workplan and timetable

Make a plan for fact gathering and analysis

Assign team members to tasks with specific outputs and completion times

Step 5: Conduct critical analyses

Start with heuristics – short cuts or rules of thumb – to get an order of magnitude understanding of each component and assess priorities

Understand where there is a need for more work and for more complex techniques

Make frequent use of one-day answers

Step 6: Synthesise findings from the analysis

Assemble findings into a logical structure

Synthesise in a way that convinces others

Step 7: Prepare a powerful communication

Develop a storyline from the conclusions

Governing thought or argument derived from the situation –observation – conclusion

Support with synthesised findings and assembled into component arguments

Lead with action steps or pose a series of questions that motivate action.

Case: Does Sydney airport have adequate capacity?

Step 1: Problem definition

Will Sydney airport capacity be adequate in the future?

The problem statement was bounded around passenger airport capacity, so excludes policy factors that might warrant a second airport, such as:

Greater accessibility

Safety

Environmental factors like noise

Alternatives like a very fast train link between major cities

Step 2: Problem disaggregation

Break down the problem of airport capacity in terms of supply minus demand.

If supply exceeds demand, then Sydney airport does have adequate capacity.

If demand exceeds supply, then Sydney airport does not have adequate capacity.

But how do we define and measure supply and demand?

The number of passengers the airport can process

The number of passengers wanting to use the airport

How to model demand?

GDP growth?

Reasons for travelling to Sydney?

Flight Costs (aviation fuel costs)

Runway utilisation

Operating hours

Planes per hour

People per plane

Runway Capacity

Plane Type

Dimensions

Number of Runways

Not a short term solution

Which Areas Can Be Most Easily

Influenced to increase capacity?

Step 3: Prioritize the issues, prune the tree

We can reframe the question of ‘will airport capacity be adequate?’ to ‘how can capacity be increased to ensure it is adequate?’ because this provides an actionable solution.

This helps us to prioritize the issues and prune the tree by focusing on the factors that can be actively managed to increase capacity.

First, it is easier to actively manage supply (airport capacity) than demand (airport use).

Then, of the the three ways that supply has been defined, in the short-term, the number of runways is fixed and so is runway capacity, defined in terms of size of aircraft that can fit on to a runway at a given time.

Therefore, in prioritizing the issues and pruning the tree, we should conclude that the variable that we can actively manage and, therefore, the key to solving this problem is runway utilization.

How can we define runway utilization?

Steps 4 and 5: Workplanning and analysis

Having concluded that runway utilization is the key, we can define this in terms of:

Hours of operation

Spacing between aircraft movements

Number of people per airplane

Our analysis should then focus upon which of these are the variables that we can actively manage to increase runway utilization and, therefore, airport capacity.

Hours of operation are limited by curfew periods, weather and maintenance, none of which can be actively managed.

Spacing between aircraft movements would require reducing the time between take-offs and landings, reducing safety, so not a good option for increasing capacity.

Therefore, our most viable solution is to increase people per plane.

Slot pricing favouring larger planes

Light aircraft policy

Reduce further the times between take-offs and landings

Difficult to change because of noise

Steps 6 and 7: Synthesis and storytelling

Supply is more actively manageable than demand.

Runway utilization is the most actively manageable component of supply – more so than number of runways and capacity per runway.

People per plane is the most actively manageable component of runway utilization – more so than hours of operation and spacing between aircraft.

Policies that can increase people per plane are:

Higher slot prices (the price airlines pay for permitted take-off and landing times) that require airlines to use larger planes to be more profitable.

Banning light aircraft from using the airport at peak hours.

Conclusions

Complex problem solving is a critical skill for MBA students.

Problem solving should follow a rigorous process, such as the seven-step process of bulletproof problem solving (although there are others).

Disaggregating problems into component parts using logic trees allows you to isolate the most important analyses.

Prioritizing analyses avoids working on parts of the problem that don’t contribute much to the solution – keeping you on the critical path, e.g. focusing on actively manageable variables.

We can use simple or sophisticated tools for analysis.

We need to synthesize our findings and tell a compelling story to convince someone to act.

Workshop: Which three MBA programmes should I apply to and in what order?

Step 1: Define the problem

Which three MBA programmes should I apply to and in what order?

Constraints:

Assumes you have already concluded that you are going to undertake postgraduate study and that an MBA is the course you want to study.

You can only apply to three MBA programmes and you should rank them in terms of first, second and third choice.

There is no point applying to MBA programmes for whom you do not meet the entry requirements.

Step 2: Disaggregate the issues

List the factors that mattered to you in terms of a good MBA programme to apply for, such as:

University reputation

Mode of learning

Location of university

Curriculum

Teaching staff

Cost

Funding

Define the factors in terms of what they mean and how you could measure them.

For example, university reputation means a university respected by the kind of employers I would want to work for after my MBA and could be measured using data from university league tables – using ranking to assign points to the universities being considered.

Step 3: Prioritise the issues, prune the tree

Starting with 100%, weight the factors in terms of importance to you.

For example, university location may be of utmost importance to you, so you give it a 40% weighting.

Prune the factors that are not on your critical path by:

Removing factors that are not sufficiently important to you (i.e., have an insignificant weighting)

Removing factors that conflict with others (e.g., if a student wanted to study their MBA remotely, this would remove the need to consider location of the university).

Collapse factors that can be encompassed in a single factor (e.g., cost and availability of funding could be collapsed into a single factor of affordability).

Remove factors that are not differentiating between your choices (e.g., if you have won a large scholarship, affordability would not differentiate between your choices).

Steps 4 and 5: Workplanning and analyses

Undertake ‘back of an envelope’ simple analysis to establish a list of viable MBA programmes to go forward to more detailed and sophisticated analysis.

Collect data (i.e., fact gathering) against your final list of weighted factors for the MBA programmes selected in the previous step.

Calculate a total weighted score for each MBA programme and rank the programmes.

How does this compare to the MBA programmes you applied to?

Steps 6 and 7: Synthesis and storytelling

Prepare a compelling pitch to your parents or significant others to explain your choice of MBA programmes to apply for.

Present this to the class.

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Becoming a great problem solver: Applying the process

Dr. Stephen Hills

Learning objectives

To understand the overall bulletproof problem solving methodology.

To be completely clear on assessment expectations.

To be able to apply the bulletproof problem solving methodology.

Assessment

The business client and their business problem (10%): A short description of the situation that prevails for your client at the outset of problem solving (i.e., the state of affairs that sets up the problem). A set of observations or complications around the situation that creates the tension or dynamic that captures the problem (i.e., what changed or what went wrong that created the problem). In the form of an objective (e.g., to reclaim market share), define a specific, measurable and actionable problem.

Basic problem structure logic tree (20%): Use an initial logic tree (i.e., factor/lever/component or inductive logic) to break the problem into component parts or issues to illustrate and define the basic structure of the problem (e.g., causes of the problem). This should be evidence-based, using a combination of credible industry and academic literature, evidence and theory, covering the problem generally and the problem in the context of your client. Provide a fully-referenced commentary of the logic tree.

Assessment

Drivers of problem solution logic tree (30%): Using the basic problem structure logic tree as a guide to locate further industry and academic literature, evidence and theory on the problem component parts of issues, produce a more complete logic tree (i.e., deductive logic, hypothesis or decision) of the drivers of the problem solution, which help us to see potential pathways to solve the problem (e.g., predictions of solutions that need to be tested). Provide a fully-referenced commentary of the logic tree.

Prioritised issues (20%): Using a prioritisation matrix, identify the ‘leaves’ (i.e., drivers of problem tree) of the more complete logic tree that have the biggest impact on the project and which you can most affect to find the critical path to solving your problem, pruning the tree to remove the branches that are not on the critical path to solving the problem. Provide a fully-referenced commentary of the prioritisation matrix.

Workplan (20%): Starting from the prioritised ‘leaves’ (i.e., drivers of problem tree) of the more complete logic tree, link each to detailed workplans of research questions, hypotheses, analyses, data sources, timing and anticipated analysis end product. Using a Gantt chart, produce a lean project plan covering key activities and fixed milestones of your proposed project.

Keys to the seven-step process

Becoming a great problem solver

Great problem solving consists of:

Good questions that become sharp hypotheses.

A logical approach to framing and disaggregating issues.

Strict prioritisation to save time.

Smart analytics that start with simple analysis and move to appropriate sophisticated analysis, if required.

A commitment to synthesize findings and turn them into a story that galvanizes action.

Take the time up front to really understand your problem

Check that your problem is really a problem.

Evidence it using data.

Your initial defining of the problem could evolve as you take time to really understand the problem.

Use the 5 Whys to get to the root of the problem.

Know the boundaries, the accuracy required, the time frame allotted and any other forces acting on the problem.

Be ready to revise your problem statement as you learn, refining it iteratively with one-day answers:

The situation that prevails at the start of problem solving.

The complication (i.e., what changed or what went wrong) that led to the problem existing.

Look at trend data to evidence that something changed or went wrong.

Get started with nothing more than a problem statement

The starting point for problem solving is a problem statement.

From this you can start with an initial logic tree to understand the structure of your problem.

Try several cuts at the tree

Write components or branches on sticky notes and move them around until they make sense in a logical grouping.

Consider different ways to disaggregate the problem with the aim being to disaggregate in the way that yields the most insight – the way that can help you identify pathways to the solution.

Use a team wherever you can

Teams serve to diversify thinking and experiences that deepen the richness of creativity.

Teams reduce the chance of confirmation and other biases.

Make the right investment in a good workplan

Prune your final logic tree to focus on the big and important levers of impact that you can move and take these forward into your chunky workplan.

A good workplan takes a little upfront time but will save so much wasted effort later.

Be precise about the research questions that arise from ‘leaves’ you take forward.

Hypothesise answers to quantitative research questions.

Your workplan should be chunky but your Gantt chart lean to keep your work on track.

Start your analysis with rules of thumb, summary statistics and heuristics to get a feel for the data and the solution space

Start by exploring the data to learn its quality, understand the magnitudes and direction of key relationships.

One-day answers, supported by good logic and simple analysis, are often sufficient to close the book on many issues, allowing you to move on to the more difficult ones.

Don’t be afraid to employ big analytic guns when required

Sometimes a complex research question requires more advanced analytics.

When problems play out over longer and more uncertain periods, you should consider using game-theory models, risk-management actions, strategy staircases and theories of change.

Put as much effort into synthesis and telling the story as doing the analysis

Even after completing powerful analysis that reveals great insight about a problem, resist declaring the problem as solved.

You still need to persuade the decision-maker to do something different.

Be persuasive to convince powerful stakeholders to follow your plans.

Humans are visual learners and love storytelling.

Treat the seven-step process like an accordion

You can compress or expand steps depending on the issue.

Use one-day answers to get to the level of analysis that the problem requires.

Don’t be intimidated by any problem you face

Investment in systematic problem solving can achieve insight into almost any problem of consequence:

Long-term and uncertain problems.

Wicked problems.

Conclusions

Take the time up front to really understand your problem.

Get started with nothing more than a problem statement.

Try several cuts at the tree.

Use a team wherever you can.

Make the right investment in a good workplan.

Start your analysis with rules of thumb, summary statistics and heuristics to get a feel for the data and the solution space.

Don’t be afraid to employ big analytic guns when required.

Put as much effort into synthesis and telling the story as doing the analysis.

Treat the seven-step process like an accordion.

Don’t be intimidated by any problem you face.

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Simple analysis: Rules of thumb, summary statistics and heuristics

Dr. Stephen Hills

Learning objective

To undertake simple analysis that can answer research questions about the issues identified in your problem disaggregation and prioritization to inform problem solutions.

Simple analysis

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Step 5: Conduct critical analyses

Start with heuristics – short cuts or rules of thumb – to get an order of magnitude understanding of each component and assess priorities

Understand where there is a need for more work and for more complex techniques

Make frequent use of one-day answers

Simple analysis: Rules of thumb, summary statistics and heuristics

Good problem solvers have a toolkit for fact gathering and analysis.

Starting with rules of thumb, summary statistics and heuristics to understand the direction and magnitudes of relationships.

We can structure and resolve many analytic issues with rules of thumb, summary statistics and straightforward heuristics.

Rules of thumb are shortcuts in analysis that we can quickly apply to answer a question.

Summary statistics are calculations that provide a summary of data, e.g. Mean average.

Heuristics are any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal or approximation.

All three help to size the different elements of the problem to determine the critical and efficient path in further analysis.

Rules of thumb

Rules of thumb: 80:20 rule and rule of 72

80:20 rule: Developed by the Italian economist Vilfredo Pareto, this rule of thumb describes the common phenomenon that 80% of outcomes come from 20% of causes. In other words, the 80:20 ration is a very common market structure, so it can be applied if your problem requires you to estimate a market structure. For example:

20% of consumers account for 80% of sales.

20% of the population account for 80% of healthcare costs.

Rule of 72: This rule is a shortcut for estimating compound growth or compounding effects, which can be applied if your problem requires you to estimate how quickly wealth might build, how quickly an enterprise might scale or how quickly a population might grow. The rule of 72 allows you to estimate how long it takes for an amount to double given its growth rate by dividing 72 by the rate of growth. For example:

If the growth rate is 5% an amount will double in 14.4 years: 72/5 = 14.4 years.

If the growth rate is 15% an amount will double in 4.8 years: 72/15 = 4.8 years.

If an amount doubles every 2 years, the growth rate is 36%: 72/2 years = 36% growth rate.

Rules of thumb: S-curve

The S-curve is a rule of thumb, which shows a common pattern of sales with a new product or a new market. It can be applied if your problem requires you to estimate the adoption rate for a new innovation.

This common pattern of the S-curve is that adoption starts slow and then picks up before starting to plateau.

Summary statistics

Simple risk and pricing calculations: Break-even point and marginal analysis

Break-even point: The level of sales where revenue covers cash costs, which can be applied to determine risk of a particular endeavour, e.g., how confident are we that we can cover our costs? Requires knowledge of marginal and fixed costs.

Break-even point = Fixed costs / unit price – unit variable costs

The unit price should be known and the fixed costs and variable costs can be quickly calculated, but the complexity comes in how these variables can change as you scale a business, e.g. step-fixed costs where to double volume involves significant investment.

Marginal analysis: Examining the cost or benefit of the next unit. Can be applied to inform problem solving around decisions on producing more, consuming more or investing more in an environment of scarce resources.

Where there are fixed costs of machinery and plant, marginal costs (i.e., the cost of producing one more unit) can fall very quickly, favouring more production until more machinery is required.

Example: Imagine that the total fixed costs (e.g., mortgage, staff, taxes) of a 200 room hotel are $10k per night or $50 per room. A last minute special offer of $30 per night for unsold rooms is below the average room cost of $50, but the marginal cost to sell one more room is very small (i.e., electricity, water, towels).

Simple probability calculations: Bayesian thinking and expected value

Bayesian thinking: Based upon conditional probability, which is the probability of an event given another event which also has a probability (i.e., a prior probability). Can be applied to predict probability on the basis of a given situation.

Example: The probability of raining is higher if it is cloudy than if it is sunny.

Expected value: The value of an outcome multiplied by its probability of occurring. It can be applied to set priorities and reach conclusions on whether to take a bet in an uncertain environment.

Example: The Australian research organization CSIRO decided on whether to defend its WiFi intellectual property in court using expected value in reverse. They calculated that they would win $100 million and their legal costs would be $10 million. This reflects a 10% probability of occurring, but CSIRO thought the probability of success was much higher, so it was a gamble worth taking.

Heuristics

Reasoning by analogy

Reasoning by analogy: When you have seen a particular problem structure and solution before that you think may apply to your current problem.

Example: Precedent in law is where conclusions in one court case are applied to another.

Distribution of outcomes

On the basis historical data about similar projects, you can estimate the distribution of outcomes, so to have contingency plans for budget cost overruns and possible delays.

Example: Companies planning large projects often add contingencies of 10% or more for cost overruns. However, research into comparable projects may show that similar projects as yours often need more than 10% contingencies.

Question-based problem solving

A deeper analysis after you have roughed out the scale and direction of your problem levers using rules of thumb and heuristics.

Paint a picture of the problem by asking questions, such as who, what, where, when, how and why.

A powerful root-cause tool to quickly focus problem solving.

Example: Should I have a knew arthroscopy?

A literature review established the key questions that informed the solution to the problem.

Root cause analysis

The technique of asking 5 Whys to get to the bottom of a problem was developed at the Toyota Motor Corporation.

The core idea is to dig deeper than the superficial or proximate causes of problems to uncover the source or root causes.

A root cause is one that when addressed removes the later problem.

The 5 Whys

A diagnosis of market-share loss, looking at superficial causes until the deeper source of the problem is found.

Forces us to push beyond the local or contributory causes of problems towards root causes by asking ‘why’ until there is no further ‘why’.

By forcing greater and greater specificity the ultimate source of customer loss is found in inadequate training of phone customer service agents.

Conclusions

Start all analytic work with summary statistics and heuristics that help you see the size and shape of your problem levers.

Rules of thumb can serve as useful short cuts.

Simple question-based analysis grounded in the literature can lead you to a solution.

Root cause and 5-Ways can help you identify fundamental causes of problems that then lead to a solutuion.

Workshop: Challenger Disaster Research

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Workplans: Undertaking efficient problem solving

Dr. Stephen Hills

Learning objectives

To be able to link logic tree hypotheses to a plan for fact gathering and analysis.

To be clear about the questions we need our analyses to address.

To order our analyses in the most efficient way.

To produce a workplan and a project plan.

To clarify current understandings and unknowns.

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Step 4: Build a workplan and timetable

Make a plan for fact gathering and analysis

Assign team members to tasks with specific outputs and completion times

Workplanning

Critical path

We need to prioritise the most important analysis for problem solving so that we are always working on the parts of the problem that have the highest probability of yielding insight.

Workplanning with frequent updated iterations after the initial workplan enable us to stay on the critical path.

Link your advanced logic tree ‘leaves’ to a plan for fact gathering and analysis.

Do not wade prematurely into data without doing the thinking on the underlying structure of the problem.

Think hard about problem structure and desired outputs before you start running numbers.

Workplan principles

Do not do any analysis for which we don’t have a hypothesis – you must be clear about what questions your analyses answer.

Clarify the outputs you want from analysis – visualise what the output might look like so to be clear on whether this is desirable or not.

Order analysis logically so that analysis that may make other analysis redundant is done first, e,g, a finding that solves the problem, such as to not pursue solar panels.

Do knock-out analyses first, the really important analyses next and the nice to have analyses last.

Workplan

Case: Workplanning the nursing problem

Deductive logic tree: Nursing outcomes

Workplan: Nursing outcomes

Issue 1

Issue 2

Chunky workplans & lean project plans

Chunky workplans and lean project plans

Long workplans are unnecessary and unhelpful.

They can quickly become out of date.

You are at risk of being swallowed up by the analyses that could be done, taking you off of the critical path.

Rather, workplans should focus on the most important initial analyses and revised as new insights emerge.

Couple these with lean (i.e., less detailed) project plans using a Gantt chart covering fixed milestone dates to ensure the overall projects stays on time.

Chunky workplan

Lean project plan

One-day answers

Crisp and concise.

Stating what you know about your problem at any point in the process helps to clarify:

What understandings are emerging.

What unknowns still stand between the answers and us.

One-day answers convey our current best analysis of the situation, complications or insightful observations and our best guess at the solutions, as we iterate between our evolving workplans and our analysis.

This helps us to divert resources to areas where we have the biggest gaps in problem solving and shut down analysis that is not taking us anywhere.

As analysis findings come in, we can refine our one-day answers and begin to synthesize our evidence into more complete stories.

Structuring one-day answers

Situation: A short description of the situation that prevails at the outset of problem solving. The state of affairs that sets up the problem.

Observation or complication: A set of observations or complications around the situation that creates the tension or dynamic that captures the problem. What changed or what went wrong that created the problem.

Implication or resolution: The best idea of the implication or resolution of the problem that you have right now. At the beginning this will be rough and speculative. Later it will be a more and more refined idea that answers the question “What should we do?”

One-day answers: What they are not

Case: Hardware company one-day answer

Situation: Herchinger is a dominant player with a long and successful history in one region and seeks to expand.

Observation or complication: A new competitor, Home Depot, has emerged with a warehouse superstore model that is growing faster due to lower pricing made possible by sourcing economies of scale, lower cost logistics and higher asset productivity.

Implication or resolution: To remain competitive via lower pricing Herchinger needs to quickly reform its inventory management and logistics systems and to develop lower-cost sourcing models.

Conclusions

Good discipline and specificity in workplanning will make your problem solving more efficient.

If you order your analyses correctly and undertake your knock-out analyses first you will stay on the critical path to your solution.

Workplans should be be chunky – short and specific.

Study plans should be lean – capturing key milestones so you deliver on time.

One day answers clarify where you are and what work is left to do.

Workshop: Which three MBA programmes should I apply to and in what order?

Workshop: Chunky workplan

Identify two issues from the previous problem of picking the best MBA programme for you.

Produce a chunky workplan for each issue:

Definition of issue

Hypothesis

Analysis

Source

End product

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Prioritising problems: Pruning your logic trees

Dr. Stephen Hills

Learning objectives

To be able to identify problem components that are important.

To be able to identify problem components that are actively manageable.

To be able to appropriately prioritise problem solving work.

Prioritising problems

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Step 3: Prioritise the issues, prune the tree

Identify which branches of the logic tree have the biggest impact on the problem and which you can most affect.

Prioritising problems and pruning logic trees

Good problem solving is as much about what you don’t do as what you do.

Good prioritization of your problem solving work makes your problem solving more efficient.

Solutions come faster with less work – you do not need to work on components of the problem that are not important in solving the problem.

Although we want our initial logic trees to be collectively exhaustive so that we have all the parts, we should not retain components of the problem that:

Are not important in solving the problem.

Are difficult or impossible to influence or affect.

Prioritization 2×2 matrix

Vertical axis: Potential scale of impact – whether or not the factor is important in solving the problem

Horizontal axis: Ability to influence the factor – whether or not it is possible to affect the factor (low to high).

Cleaving frames

Literature reviews and theoretical frameworks are important tools for disaggregating and prioritising problem components.

Cleaving frames are another important tool that act as lenses to visualize potential solutions to help with the disaggregating and prioritising of problem components.

There are many company performance and company questions that benefit from a series of frames that help to highlight the likely solution paths quickly.

Many problems combine elements from more than one frame.

Cleaving frames: Business

Business cleaving frame: Collaborate/Compete

Any business strategy needs to take account of the potential reaction by rival firms, so to decide where a company is willing to engage in intense competition and where it is not.

The elements of this cleaving frame are taken from game theory and include where to play, how to fight, acquiring a reputation, and signalling.

Example: What could the West done in the years building up to Russia’s invasion of Ukraine to discourage Putin?

Cleaving frames: Social/Citizen

Social/citizen cleaving frames: Equality/Liberty

Many policy decisions to address social problems face the fundamental frame choice of encouraging more equality among citizens versus allowing more individual freedom.

The elements of this frame include community needs and individual rights.

Example: How could more stringent gun laws be passed in the United States?

Case: Climate change and the cost curve

Climate change is an imminent threat to all of humanity and is often thought about using the cleaving frame of Mitigate/Adapt, which contrasts policy efforts to reduce harm from a causal factor (e.g., climate change) with efforts to adapt to the factor.

Elements include reduce harm, address harm, and resilience.

Another way that climate change can be though about is using the cleaving frame of Supply/Demand, which addresses questions such as ‘can we get more?’ versus ‘how can we use less?’

This can be operationalised using a cost curve.

A cost curve can be applied to visualize the returns from (below the line), or the costs of (above the line) reducing CO2 emissions.

The potential solutions are then ordered from left to right with furthest left representing highest returns and the furthest right representing the highest costs for reducing CO2 emissions.

Case: Climate change and the cost curve

Just do it now – it makes sense!

There are lots of potential actions for which there are positive returns for individuals and private companies.

With these, quick progress can be made against the problem via education and supporting tax credits for the investment costs.

Largely nature’s solutions and agricultural practices

There are another group of actions in the agricultural and land use space, e.g. reforestation, avoided deforestation, degraded land recovery where there are no positive returns, but investment costs are low so governments should invest in these to reduce CO2 emissions.

Invest in new technology and markets

Longer term actions that will require substantial private and social investment in new technology and markets.

Conclusions

Good prioritization of your problem solving work makes your problem solving more efficient.

Do not work on components of the problem that are not important in solving the problem.

Do not work on components of the problem that are difficult or impossible to influence or affect.

Focus your early efforts on the big levers you can pull.

Workshop Material

Workshop: Help a struggling local restaurant

A local restaurant has approached you asking for consulting advice as to how to grow their business.

The business has recently taken a downturn as number of customers have dropped and their energy costs have increased.

Working in pairs apply the prioritisation matrix to the initial logic tree (i.e., a factor/lever/component logic tree) you previously produced.

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Sophisticated analysis: The big guns of analysis

Dr. Stephen Hills

Learning objective

To undertake sophisticated analysis that can answer research questions about the issues identified in your problem disaggregation and prioritization to inform problem solutions.

Sophisticated analysis

The seven-steps process

How do you define a problem in a precise way to meet the decision maker’s needs?

How do you disaggregate the issues and develop hypotheses to be explored?

How do you prioritize what to do and what not to do?

How do you develop a workplan and assign analytical tasks?

How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?

How do you go about synthesizing the findings to highlight insights?

How do you communicate them in a compelling way?

Step 5: Conduct critical analyses

Start with heuristics – short cuts or rules of thumb – to get an order of magnitude understanding of each component and assess priorities

Understand where there is a need for more work and for more complex techniques

Make frequent use of one-day answers

Sophisticated analysis

You may be faced with a complex problem that really does require a robustly quantified solution:

Have you adequately framed the problem you face, and the hypothesis you want to test, so that it’s clear you do need more firepower?

Is there data available to support using an advanced analytic tool?

Which tool is the right one for your problem?

Is there user-friendly software available to help you use some of these tools?

Which big gun to choose?

Selecting an analysis approach

Are you primarily trying to understand the drivers of causation of your problem (how much each element contributes and in what direction), or are you primarily trying to predict a state of the world in order to make a decision?

The first question leads you mostly down the left-hand branch into various statistical analyses, including creating or discovering experiments.

The second question leads you mostly down the right-hand side of the tree into forecasting models, the family of machine or deep learning algorithms, and game theory.

Data visualization: Where to live due to air quality

Data visualization: Clusters and hotspots – London air quality

Problem: Where to live in London for good health.

The air that we breath is key to our health.

Feeding in data on accident and emergency admissions against postcodes and data on air quality (measurement of particles in the air that affect respiratory health) shows a positive correlation that can inform decision-making on where to live in London.

Free tool from UK Government: https://dataingovernment.blog.gov.uk/2016/03/30/free-tools-to-quickly-show-postcode-data-on-a-map/

Regression models: Obesity

Regression models for cause: Obesity

Regression analysis is a powerful analytic tool to understand the underlying drivers of the problem of obesity.

It shows us where to look for solutions.

Data was gathered on 68 US cities for the outcome variable of obesity prevalence and hypothesised predictors of educational attainment, median household income, city walkability and climate comfort score (suitability of weather to physical activity).

Regression analysis found that education, income, walkability, and comfort score are all negatively associated with obesity prevalence.

In other words, as these predictors increase, obesity prevalence decreases.

Regression models: Obesity

Income accounts for 71% of the variance in obesity prevalence.

Having a household income of $80k vs. $60k is associated with a 7% point drop in obesity prevalence (a 30% reduction in obesity).

A model explaining 82% of variance in obesity included income, education, comfort, walkability, and an income/education term (used because income and education were highly correlated).

Experiments: RCTs and A/B Testing

Randomised controlled trials involve randomly assigning participants to experience two different conditions.

Because participants are randomly assigned to condition, we can be confident that the two groups are equal in all respects other than experiencing A or B.

Therefore, we can be completely confident and differences in the outcome measured (e.g., purchasing behaviour) is due to having experienced A or B (e.g., presence of special offer).

Natural Experiments: Voter prejudice

RCTs are not always possible, but a natural variation can act like random allocation to a condition.

It is random whether or not a participant is presented with a delegate with a minority name or not.

Therefore, we can be confident that any difference between expected values and actual votes can be attributed to prejudice.

Simulations/Regression: Climate change

Simulation/Regression models for prediction : Climate change

Previously we looked at how a regression model can be used to determine the causes of obesity.

However, the same method (used exactly the same way) can also be used to predict an outcome.

We get a regression equation with coefficients (i.e., ratios) based upon the observed data.

We can then input hypothetical data into that equation to model what would happen in that hypothetical situation.

We could input different climate scenarios into a model to predict outcomes on the basis of a model based on observed data.

Game theory: Going to court

We can use game theory to work through our own choices and competitor choices.

To assess how we should respond to an opponent’s moved, we can simulate the position of competing parties and make a series of moves that the opposing party has to respond to.

Conclusions

To get to a solution for many complex problems may require sophisticated analytic tools.

To do this you need to understand your research question and the nature of your data.

RCTs are the gold standard for determining cause and effect, but where these are not possible you might be able to use a natural experiment or model causes using regression.

Regression can also be used to predict an outcome by constructing a model with observed data and inputting hypothetical data.

Game theory encourages you to think through different scenarios depending on the move of a competitor.

Bayesian statistics calculate probability of something under different conditions.

Workshop: Bayesian statistics and the Space Shuttle Challenger Disaster

Bayesian statistics: The Space Shuttle Challenger Disaster

The Space Shuttle Challenger Disaster was a problem solving failure

A failure to accurately risk assess O-ring damage, which could have been best assessed with Bayesian statistics.

Bayesian statistics are useful in incomplete data environments as a way of assessing conditional probability.

Conditional probability occurs in situations where a set of probable outcomes depends in turn on another set of conditions that are also probabilistic.

O-ring failure under low temperatures was the probable cause of the Challenger space shuttle erupting shortly after lift-off.

There was an unusually low temperature at launch of 31 degrees Fahrenheit (22 degrees below the minimum of previous launches).

In other words, there was incomplete data.

The disaster investigation subsequently found that O-rings are five times more responsive at 75 degrees that at 30 degrees.

Bayesian statistics: The Space Shuttle Challenger Disaster

Engineers only had data on flights where the temperature at launch was in the range of 53 to 81 degrees.

The data that they should have been looking at was the data on all flights in relation to temperature and O-ring damage.

This provides a different picture:

For temperatures below 65 degrees, all four (100%) flights had incidents.

Above 65 degrees only three out of twenty (15%) had damage incidents.

On the basis of this data, in groups, estimate likelihood of O-ring failure.

Bayesian statistics: The Space Shuttle Challenger Disaster

From the data, we can conclude that the overall prior probability of failure is 29% (failure of seven O-rings in 24 flights): 7/24*100 = 30.4%

However, the conditional probability (i.e., that incidents are more likely as temperature declines) of launching when the temperature is 31 degrees is 99.8%.

Although four out of four (100%) launches below 60 degrees were failures, this is a small sample size, so we need to fit a distribution to the data, thus reducing the likelihood of failure to below 100%.

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Long time frames & high uncertainty: Solving problems in complex contexts

Dr. Stephen Hills

Learning objectives

To be able to identify levels of uncertainty.

To be able to employ different strategic options appropriate for different levels of uncertainty.

To be able to step-out into new and uncertain markets using a staircase approach.

To be able to theorise change.

To be able to balance strategies and investments.

Long time frames & high uncertainty

Many of the problem examples to date in the core text exhibited

low to moderate uncertainty

where the cost of error is relatively low

and where time frames are relatively short.

But there is a class of problems where

time periods are long

complexity and uncertainty are high

and the consequences of error are substantial.

Levels of uncertainty

Levels of Uncertainty

1	Simple predictions and short term forecasts

Levels of Uncertainty

2	Alternative futures from legislative or technology change with a binary result
1	Simple predictions and short term forecasts

Levels of Uncertainty

3	Scenarios e.g. impact of AI on jobs, role of fossil fuels in 15 years time
2	Alternative futures from legislative or technology change with a binary result
1	Simple predictions and short term forecasts

Levels of Uncertainty

4	Sea-level in London in 2050
3	Scenarios e.g. impact of AI on jobs, role of fossil fuels in 15 years time
2	Alternative futures from legislative or technology change with a binary result
1	Simple predictions and short term forecasts

Levels of Uncertainty

5	When will be come into contact with alien life forms?
4	Sea-level in London in 2050
3	Scenarios e.g. impact of AI on jobs, role of fossil fuels in 15 years time
2	Alternative futures from legislative or technology change with a binary result
1	Simple predictions and short term forecasts

Where does the Russia invasion of Ukraine fit on this tool?

Levels of uncertainty

Levels of uncertainty range from level 1 (a reasonably predictable future) to level 5 (the unknown unknowns).

Uncertainty levels 1 through 4 are the known unknowns.

Uncertainty can be a good thing for strategic problem solvers.

If your problem solving is right you can earn good returns and guard your downside while others are floundering.

Example: Hedge fund managers hope for uncertain and volatile markets when they have an analytic edge.

We need to recognise and quantify the level and type of uncertainty, and then develop approaches to move toward our desired outcomes by managing the levers that we control.

What actions you can take to deal with a particular level of uncertainty?

Dealing with uncertainty

Dealing with uncertainty: The known unknowns

What actions you can take to deal with a particular level of uncertainty?

The spectrum of actions you can take when faced with level 2 level 4 uncertainty (the known unknowns) range from buying time (i.e., doing nothing and waiting for more information) to no regret moves and taking big bets.

When uncertainty sits at level 2 (alternative futures) or greater, most decision-makers will seek to defer irreversible commitments.

Thinking probabilistically: When we can estimate the parameters of uncertainty, math can help us to calculate what is a fair bet and estimate the value of different options.

Actions for Dealing With Uncertainty – Strategic Perspective

Buying information: Ranging from data gathering and analysis of underlying processes to clarifying the sources of uncertainty and the motives of protagonists (i.e., principal actors).

Hedges: Making a reasonable cost move or investment that will counter downside events.

Examples:

Fossil fuel companies investing in renewable energy.

Buying water rights in areas expected to have lower rainfall with climate change.

Sometimes these can involve options, futures contracts, or other financial instruments.

Acquiring low-cost strategic options: Having a portfolio of initiatives, or betting on multiple horses in the race, is another way we deal with uncertainty.

Major financial institutions investing relatively small equity stakes in financial technology (fintech) start up companies to be in a position where they can first understand and then capitalize on innovation that challenges established players.

Example: In 1986 IBM had an opportunity to buy 30% of Miscrosoft for under $300 million.

Actions for Dealing With Uncertainty

Buying insurance: The threat of global warming is sometimes framed as an insurance problem, with estimates that a little over 1% of global GDP has to be committed to paying the annual premium to keep CO 2 levels below a 2-degree centigrade increase.

There are also insurance products for a range of uncertain situations, known as cat bonds or catastrophe bonds, that cover events like typhoons or hurricanes.

No regrets moves: When you are comfortable with the level of uncertainty or can edge out into a competitive space with incremental learning moves.

These are no regret moves because they involve the capability building you’ll need no matter what the outcome.

Big bets: Taken when you have a level of confidence about an outcome not shared by others.

Example: The Big Short book/movie describes how a new financial instrument called a credit default swap was used to short the housing market – to sell positions on the assumption that housing prices will drop.

The investors felt the mortgage securities were mispriced and took a big bet

Case: How should I choose my career?

The impact of automation and advances in human level machine intelligence will have both a positive and negative impact on jobs.

Predictions of the labour market are much more uncertain than they have been in previous years.

Currently lots of new job titles that did not exist in previous years and in the future there will be other new job titles.

How do you navigate your way through career decisions now to avoid a high cost of error and have a good chance of realizing your goals?

How can you best position yourself in this changing labour landscape?

Logic tree: Economic labour landscape

The employment share of non-routine cognitive and manual jobs is growing, while the employment share of routine cognitive and manual jobs is shrinking.

You can use your knowledge about your personal level of ability, interests and risk tolerances to guide your career choice decisions.

Matrix: What are you passionate about and does that map against what skill sets are needed?

Each row is broad potential field or sector.

The first column is current economic predictions for the field.

The next are your personal assessments of your ability, interest and ability to take risks.

For each potential field, subject area, or employment sector, you fill in your self-assessed strength, interest, and risk-tolerance levels in the second, third, and fourth columns.

You can choose low, medium, or high, to rank your preferences.

Matrix: What are you passionate about and does that map against what skill sets are needed?

Eliminate or prune sectors in which you have low strength or abilities and low interest.

Case: How should I choose my career?

Beginning with the most promising sector, the sectors for which you have the highest interest and ability, you can use this tree to guide your next step or action.

Potential strategies or actions:

Make a big bet and embark on a path with considerable risk.

Make a no regrets move and gain a base level of education or training in a safe field that safeguards you from risk.

Hedge your bet by investing time and energy in education or training for two or more fields or positions.

Faced with high ability, high interest, high risk tolerance and high economic opportunity, a big bet entrepreneurial path is preferred.

However, where these factors don’t align, a no regrets move of obtaining an education base is preferred.

You could also hedge your bet by double majoring or studying while working.

Strategic growth staircases

Determining the steps that allow successful companies to edge out into new businesses in uncertain environments.

Emphasis is on learning, buying options and building capabilities.

What steps are taken and in what order to build capabilities, add assets and reduce uncertainty?

Work backwards from a planned outcome, thinking through the capabilities, assets and business learning required to be successful.

Take a step-by-step approach to strategic moves, allied to the uncertainty level, expanding commitment as uncertainty is reduced.

Frame in time the staircase steps, knowledge captured and capabilities that are being built.

Staircase architecture

Stretch: The extent of stretch in the step reflects the complexity of new capabilities and the degree of integration required. There should be a balance between the ability to absorb new skills and the competitive demand for speed to establish market presence. If not, the stretch is too much.

Momentum: The degree of momentum due to positive effect of early success from small moves and learning and confidence of the organization.

Flexibility: Maintaining fluidity in the face of uncertainty, such as avoiding sunk assets that could later become stranded, e.g., by contracting and outsourcing work.

Staircase architecture is determined by bringing these three considerations together and working through choices about staircase steps and how they are sequenced.

There are trade-offs between smaller vs. larger steps, option-laden moves vs. focused commitments (e.g., better at blocking competitors) and the speed at which moves can create momentum, which can be challenging to integrate with the existing business model

Case: J&J Contact Lenses

J&J were able to build a global contact lens business in little over a decade.

This was achieved in a series of initially small steps, then larger and more expansive moves

Theory of change

For large multiyear problems it is important to have an overall theory of change.

A theory of change is a strategic map for visualizing change for large, multiyear problems.

It is a comprehensive description and illustration of how and why a desired change is expected (i.e., theorised) to happen in a particular context.

Case: Pacific Salmon

Theory of change: Pacific Salmon

This theory of change summarises the overall strategy for preserving salmon and salmon ecosystem function.

At the bottom are the strategies, which are then mapped to how and why they will contribute to for preserving salmon and salmon ecosystem function.

Portfolio of strategies map: Pacific salmon

This matrix captures the stage of investment (see below) and level of aspiration (incremental or transformational change):

Seed: Those strategies designed to create early conditions for change,

Cultivation: Those strategies where interest groups had come to the table and were willing to work together on solutions for both ecosystems and economic users.

Harvest: Those investments to cement conservation gains and solidify institutional support for new conservation centered resource management processes.

Helps to balance the strategies and investments across logical stages of change: balance between initiatives which had transformational aspirations (i.e., expensive and risky) and initiatives aiming for incremental but important change.

Conclusions

Uncertainty is a feature of most difficult problems.

Understanding the level of uncertainty is the first step to problem solving under uncertainty.

Depending on level of uncertainty, you can manage risk through buying information, acquiring low-cost options, paying for insurance and taking low-regrets moves that build capabilities and knowledge.

A staircase approach can help companies step-out into new and uncertain markets, managing stretch, maintaining flexibility and driving momentum as new capabilities and assets are built.

Case: WeWork

Background

In 2008, Adam Neumann and Miguel McKelvey originally founded GreenDesk, an eco-friendly coworking space in Brooklyn, having spotted an opportunity of a market of people freelancing or starting companies, which they matched to an excess empty office space as a result of the 2007-2008 financial crisis.

They sold GreenDesk in 2010 and used the funds, alongside funding from real estate developer Joel Schreiber, who invested $15 million for a 33% interest to launch WeWork in New York’s SoHo district in 2010.

WeWork is a company that provides flexible shared workspaces and office services.

In 2011 WeWork launched WeWork Labs, which provided an open workspace to encourage collaboration between members, serving as a startup incubator.

Early investors included JP Morgan Chase, T Rowe Price and Goldman Sachs.

Then in 2016 WeWork raised a further $430 million in a new round of financing that valued the company at $16 billion, taking the amount of private capital that the company had raised to $1.7 billion.

In 2017, after extensive global expansion and another investment round, which brought in $500 million of investment primarily from Japanese investment giant SoftBank, WeWork reached a valuation of $20 billion.

A further $400 million was raised in 2018 to fund the purchase of properties directly and a further $500 million was raised later the same year to expand in China.

WeWork secured further funding of $3 billion in 2018 and $2 billion in 2019 from SoftBank.

Initial Public Offering

In 2019 WeWork confidentially filed an initial public offering (IPO), which involves offering shares of a private corporation to the public in a new stock issuance, looking to raise over $3.5 billion.

These plans became public when WeWork filed its IPO Prospectus, which included a description of the company and its operations, the terms and conditions of the initial stock offering, and any other information an investor may need to decide to invest.

The financial disclosures in the IPO prospectus revealed the heavy losses of WeWork, which brought into question WeWork’s valuation and ability to become profitable in the future.

Neumann told Forbes that WeWork’s “valuation and size today are much more based on our energy and spirituality than it is on a multiple of revenue.”

The IPO Prospectus also led to heavy criticism of WeWork’s management and its business model, in particular there was heavy criticism of Neumann’s leadership.

As a result, WeWork postponed its IPO until the end of 2019 and sought to limit its costs.

It sold off its private jet, which had been purchased for $60 million for Neumann’s use, sold off businesses it had previously acquired and laid off 2,400 members (20%) of its global workforce and further layoffs followed in 2020.

Loss in value and confidence in leadership

At the end of 2019, SoftBank announced $9.2 billion in write-downs on its $10.3 billion WeWork investments, which is an accounting practice to recognize the reduced value of an asset, effectively recognizing that its investment in WeWork had dropped 90% in value.

SoftBank, WeWork’s largest investor, had lost confidence in Neumann’s leadership and wanted him removed as chief executive, as well as his family members from the board.

At its peak, WeWork was valued at $47 billion, but was now valued at $8 billion, which was less than the $13 billion that SoftBank had invested in the company.

Neumann’s behaviour was later described by the board as “tumultuous”.

He was accused of self-dealing and self-enrichment via frequent sell-offs of stock and leasing of buildings he owned to WeWork and for licensing the We trademark to WeWork from a separate entitle called We Holdings that he and other WeWork founders owned.

Other criticisms

Neumann was also criticised for WeWork’s purchase of a Gulfstream G650 private jet for $60 million for his use at a time when employees were not receiving promised bonuses or raises was heavily criticised.

Neumann’s hard partying lifestyle and his decisions to expand WeWork into areas of personal interest, such as surfing, were also criticized.

WeWork’s culture of mixing work and pleasure, including free beer at some of its locations, had become a problem.

Critics of WeWork argued that WeWork was little more than a typical real estate company with shaky finances that had been obscured by Neumann's personal charm and style.

Nuemann’s exit package was reported to be valued at $1.7 billion, including $970 million for his remaining shares, a $185 million consulting fees and a $500 million credit to repay loans.

However, as well as standing down, Neumann was required to return to WeWork any profits he had made from his real estate dealings and also the $5.9 million in stock he received from selling the We trademark to WeWork.

Workshop: WeWork team problem solving

Workshop: Team problem solving

Your team has been brought in by WeWork.

Start with the first two steps of the bulletproof problem solving process:

Define the problem.

Disaggregate the problem to reveal its structure and solution drivers.

WeWork Net Loss & Revenue

WeWork Costs

WeWork Capacity

WeWork Occupancy

WeWork Income per Desk

WeWork Breakeven Curve: Rate vs. Occupancy

Monthly desk rate would have to increase by 61% with occupancy remaining stable

With occupancy returning to pre-covid peak (+18%), monthly desk rate would need to rise by 37%

A further restructuring of costs

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Definition of the problem

Client

Founded in 1892, Coca-Cola, based in Atlanta, is a global leader in soft drink production ( Ciafone, 2019).

A key decision-maker has commissioned a project addressing environmental concerns tied to plastic usage, showcasing Coca-Cola's commitment to responsible practices and global sustainability trends.

Problem

Coca-Cola, a global beverage manufacturer and distributor, sells 2,800 items in 200 countries (Smith & Brisman, 2021), contributing to over 200,000 metric tons of plastic waste.

This surge in plastic waste poses a critical problem, demanding immediate attention to align with sustainability expectations and safeguard Coca-Cola's corporate reputation in an era where responsible practices are central to consumer trust.

Definition of the problem

Cause

A recent Tearfund report, "The Burning Question," highlights that major corporations, including Coca-Cola, contribute to the annual burning or dumping of 500,000 tons of plastic waste in six developing nations. Coca-Cola tops the list, generating 200,000 tons of plastic pollution annually, with significant impacts in countries like Mexico. This underscores the urgent need to address the environmental crisis posed by single-use plastic bottles.( CNbeverage,2020).

Problem definition statement

Minimize plastic waste by 70 percent by 2030.

Looking at the graphical representation, it is clear that Coca-Cola is the largest contributor of plastic waste as compared to other companies (Simon, 2019).

Problem structure and components logic tree

That's the structure of the problem and its components.

A significant amount of plastic is extensively used in packaging.

However, after use, these plastic wastes are not effectively recycled and reused.

Solution drivers and hypothesised solutions logic tree

Prioritisation matrix

Difficult to Control

Easy to Control

High Impact

Low Impact

Reducing

Packaging

Layers

Collaboration

with Suppliers

Research and

Development

Eco-friendly

Packaging

Designs

Multimedia

Campaigns

Partnerships

with NGOs

Educational

Workshops

Clear Labeling

on Products

Workplan

Issue	Hypothesis	Analysis	Source	Responsibility + Timing	End Product
Why is the recycling rate so low?	Difficulty in sorting plastic wastes results in low recycling rate	Assess the rate of recycling of different wastes among consumer househollds	●Government reports on household recycling rates	Wang Ziqing -Every 2 Weeks	●Pie chart of types of recycled in consumer households; ●Pie chart of challenges encountered by consumers in recycling different types of materials
Why is the usage rate of plastic so high in external packaging?	Bio-basedMaterials will lead to an increase in the cost of external packaging.	Evaluate the costs associated with using different materials for external packaging.	●International raw material prices ●Factory processing quotation	Wang Ziqing -Every month	●Bar chart comparing different materials for the same external packaging

Analysis

Research Question: Effectiveness of Minimalist Design

Analysis Technique: Statistical Data Analysis

Undertaking the Analysis:

Advocating for new packaging standards to achieve a 70% reduction in plastic waste by 2030.

Employing statistical data analysis to guide the implementation (Pramanti & Chotim, 2019).

Findings:

100% recyclable packaging minimizes plastic waste.

Reusable containers for 50% of beverages align with sustainability goals (Chua et al., 2020).

Discuss the concept of sustainable packaging and its importance, as well as the balance between environmental protection and economic viability through the selection of environmentally friendly materials, optimization of packaging volume, improvement of packaging recyclability, reduction of packaging print colors and promotion of consumer awareness(JU.2023).

Insights:

New packaging standards offer a direct solution for the company's 70% reduction goal by 2030.

Using recyclable and reusable materials lowers environmental impact, carbon footprint, and conserves resources.

One-day answer

Situation

Complication

Resolution

Founded in 1892, the Coca-Cola Company is an American multinational corporation famous for producing the Coca-Cola drink. This beverage industry company also manufactures, sells, and markets other non-alcoholic beverage concentrates and syrups, as well as alcoholic beverages. (Wikipedia Contributors, 2019).

Coca-Cola's plastic dye ranks NO1

Globally, the organization's volunteers collected more than 475,000 pieces of plastic waste in total. Among these plastics, the most frequently found brand was Coca-Cola, with a collection of 11,732 items. Nestle ranked second, followed by PepsiCo as the third. (Nace, n.d.).

With the progress of society, the importance of environmental protection is becoming more and more important, after technological research simply using plastic recycling is not ideal for reducing plastic waste.

Currently, only 14% of plastics are recycled globally, of which only 2% are reused; the remaining 12% are downgraded and recycled to a lower quality and functionality than raw materials. Williams& Rangel-Buitrago.(2022-2023).

100% recyclable packaging minimizes plastic waste.

Reusable containers for 50% of beverages align with sustainability goals (Chua et al., 2020).

References

Chua, J. Y., Kee, D. M. H., Alhamlan, H. A., Lim, P. Y., Lim, Q. Y., Lim, X. Y., & Singh, N. (2020). Challenges and solutions: A case study of Coca-Cola Company. Journal of the Community Development in Asia, 3(2), 43-54.

Ciafone, A. (2019). Counter-cola: a multinational history of the global corporation. University of California Press.

Lawal, A., & Moerenhout, T. (2022). Coca-Cola and the Environment.

CNbeverage. (2020, April 3). Coca-Cola, Nestlé, PepsiCo, and Unilever Respond to Pollution Reports. Food and Beverage Industry Micro Journal, 03.04.20

Williams& Rangel-Buitrago.(2022-2023). BUSINESS & SUSTAINABILITY REPORT of Coca-Cola；world plastics council 2023

Wikipedia Contributors (2019). The Coca-Cola Company. [online]Wikipedia.Availableat: https ://en.wikipedia.org/wiki/ The_Coca-Cola_Company.

JU Lei.(2013). Sustainable Food Packaging Design: Balancing Environmental Protection with Economic Viability – CNKI

References

Phelan, A. A., Meissner, K., Humphrey, J., & Ross, H. (2022). Plastic pollution and packaging: Corporate commitments and actions from the food and beverage sector. Journal of Cleaner Production, 331, 129827

Pramanti, A., & Chotim, E. E. (2019). Critical Review of Growth Population, Plastic Waste and the Digital Society in Indonesia. Jurnal Partisipatoris, 1(2), 79-86.

Simon, E. (2019). Plastics from a whole planet perspective. Field Actions Science Reports. The journal of field actions, (Special Issue 19).

Smith, O., & Brisman, A. (2021). Plastic waste and the environmental crisis industry. Critical Criminology, 29, 289-309.

Fakirov, S. (2021). A new approach to plastic recycling via the concept of microfibrillar composites. Advanced Industrial and Engineering Polymer Research, 4(3), 187-198.

Nace, T. (n.d.). Coca-Cola Named The World’s Most Polluting Brand in Plastic Waste Audit. [online] Forbes. Available at: 14.https://www.forbes.com/sites/trevornace/2019/10/29/coca-cola-named-the-worlds-most-polluting-brand-in-plastic-waste-audit/?sh=2d521c0f74e0.‌

References