Decision framing cases

A powerful approach to illustrating the process of framing decision problems is to use the case study method. Below is a list of generic cases, each generated by ChatGPT from prompts prepared by Professor Powell. Most of the cases are based on the applications in chapter 2 of the monograph “Framing the Problem.”

Each case features a standard list of decision framing questions (given below). There is nothing special about these cases. Our feeling is that the decision framing questions could be added to the back of any case study that describes a setting where we want to improve some product or process.

Feel free to download any of the cases for use in classes or simply to obtain a general understanding of the framing process. Or, copy the decision framing questions (at the end of each case, or at the bottom of this webpage) that you can attach to any case, or for use in real problems.

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• List of decision framing cases
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List of decision framing cases

Asterion Therapeutics — The Enrollment Clock. A biotech’s chief development officer must decide whether to accelerate, restructure, or pause a Phase II trial of a promising therapy as enrollment lags, safety signals emerge, and the patent clock ticks.

Aurora Motors — The Flow Coordination Dilemma. An automaker must synchronize uncertain global parts supply, production, and customer deliveries when tariffs, federal incentives, gasoline prices, and buyer preferences change faster than its plants can respond.

Bellwether Department of Health — The 36,000-Kit Decision. A state public-health office receives fewer naloxone kits than its partners requested and must allocate the limited overdose-reversal supply, decide whom to train, and whether to reserve kits for future waves.

Blue River System Operator — The 6:00 p.m. Reliability Decision. A regional grid operator faces a record evening peak, thunderstorms moving toward the solar corridor, and a warning from a critical generator, and must choose how to cover the reliability gap in real time.

BrightNest Home Services — The Two-Sided Launch Dilemma. A home-cleaning startup with strong household demand but thin cleaner supply must decide how to balance geographic expansion, pricing, and platform investment across three pilot markets.

Crestline Biotherapies — The Week 28 Decision. A biotech must decide whether to expand, redesign, pause, or terminate a Phase II clinical trial when time, patients, capital, and remaining patent life are all limited.

Dispatch Dynamics — The Recommendation Gap. A decision-automation company finds that only 43% of its truckload dispatch recommendations are acted on across twelve customer implementations, and must decide how to close the last-mile-of-implementation gap.

Harbor County — Measles Response. A county health officer must decide how aggressively to respond when measles risk is concentrated, vaccination confidence is falling, and the costs of prevention are immediate — but the benefits are largely invisible.

Harborview Hotel — The Eight-Week Pricing Decision. A hotel general manager must set pricing and inventory strategy for a citywide technology conference when peak nights are nearly booked but surrounding weekends remain soft.

Hearthline Home — The Demand Balancing Problem. A regional furniture retailer must decide how aggressively to stimulate holiday-season demand without creating stockouts in popular styles or deep markdowns on products customers no longer want.

Lakeside Medical Group — The Next Ninety Days. A primary-care team must redesign treatment for a Type 2 diabetes patient when clinical response, daily behavior, technology, and affordability are difficult to disentangle.

The Meridian Campaign — The Ninety-Three-Vote Map. A presidential campaign manager must allocate money, candidate time, information, and field capacity across a changing electoral landscape with eleven weeks to Election Day.

Northbridge Industrial Systems — The Ninety-Day Payment Decision. A manufacturer with enough cash for its own near-term obligations but not enough to absorb tariff-driven inventory purchases must decide how to protect liquidity without destabilizing critical suppliers.

Northstar Living — The Inventory Bet. A home-goods retailer must commit to holiday-season purchases of its fastest-growing products before it knows how strong demand will be or whether ocean shipments will arrive on time.

PrairieLine Freight — The Friday Afternoon Dispatch. A truckload fleet VP must balance freight selection, driver assignment, home time, and network balance when 1,184 tractors are in the wrong places heading into the weekend.

Redwood Department of Recovery Services — Redwood’s Funding Needle. A state addiction agency commissioner must balance naloxone distribution, treatment, and recovery services against an $18.6 million funding gap that threatens 126 positions and a heavily restricted revenue mix.

Ridgeway Industrial Systems — The Energy Crossroads. A manufacturing network COO must respond to a utility’s request to study its service connection as rising electricity costs and data-center-driven infrastructure investment reshape the region’s power system.

Riverton County — Public Health Response. A county health commissioner must decide how to respond when local overdose deaths remain high even as the national crisis appears to be easing.

Summit Ridge Asset Management — The Monday Morning Cash Decision. A mutual fund CIO must decide how to manage investor flows, liquidity, portfolio performance, and trading pressure after a weekend of bad news.

Decision framing questions

Each of the cases above contains a set of questions that challenge students to frame the case by identifying performance metrics, types of decisions, and types of uncertainties. These questions, listed below, are the same for each case. There are links to relevant supporting information on the SDA website.

1. Identify the different metrics that can be used to evaluate every aspect of the performance of the system. Organize these into a pyramid to indicate the relative importance, following the style given here.

2. What are the types of decisions? Use the list of decision types here as a guide, illustrated by the applications here.

3. Use the interactive framing matrix here to fill out the performance metrics along the top (from most to least important) followed by the list of decisions (in any order). Use your judgment to assess whether the impact of each decision on each metric is H (high impact), M (medium), L (low) or N (none). Finally, use the resulting matrix to rank the decisions in terms of their impact on the most important metrics.

4. What are the different sources of uncertainty? Use the 12 categories of uncertainty here as a guide.

5. Repeat the exercise in (3), but this time using uncertainties instead of decisions.
   • a) Which sources of uncertainty would be captured using average performance over time?
   • b) Which sources of uncertainty represent forms of risk that are not properly captured using averages?
6. Choose the single decision that seems to have the highest impact on the most important metric.
   • a) What approach do you think you would use to make this decision?
   • b) What data appears to be necessary to make this decision, including the calculation of any performance metrics where the decision would have a high or medium impact?
7. Using the same decision you identified in (6), identify the people, departments, groups or organizations that you would need to work with to implement the decision.