Business Problem Solving
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2.1Disaggregatingproblems.pptxDisaggregating 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
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
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
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
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?
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
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
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
