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What’s the “So What?” – Effective Storytelling in Supply Chain Analytics

A Vice President of Supply Chain at my previous company was known for infamous one-liners that would disrupt and often derail even the most well-prepared presentations.

Phrases such as “Don’t read me the news”, “tell me exactly what I need to know”, and “what is the recommendation here?” were interjected sporadically throughout an otherwise seamless presentation of project updates and supply chain KPIs.

Perhaps the most jarring one-liner to our meticulously crafted charts, slides, and reports was “So what?” What do you mean, so what? Our supply chain team would spend what felt like 40 days and 40 nights summarizing endless amounts of data – was the point not clear? After taking time to lick our wounds and self-assess our work, we (reluctantly) began to understand the VPs point.

The point was simple – anyone can summarize data. But what are the implications from the analysis, and what actions are required? What was learned and what will be done differently next time? An audience, especially key stakeholders and executives, should not have to guess what insight you want them to know or what your recommendations are. There should be no gap between data analysis and next steps (actionable items).

To deliver trusted, insightful, data-driven insights that would cause any stakeholder or executive to sit up and take notice, I learned to use a story-telling framework of “What? So What? Now What?” This structure enables a synthesis of data rather than a summary which 1) conveys a deeper understanding of the data and problem at hand 2) builds confidence and earns trust with the audience and 3) creates the motivation for the audience to act. The following paragraphs further define the “What? So What? Now What?” story-telling framework and provides examples of each.


As a first step, ask “what?” and simply answer with the facts. A fact should be supported by data with minimal value judgement. A fact remains a fact regardless of the filter or perspective, and no matter how shocking, surprising, or underwhelming the fact may be, the data itself should not be up for debate. If you are unsure of how to tell your audience “what” is going on, consider the following questions:

  1. What happened? (Quantify)
  2. What does this data tell us?
  3. What are the surprises in the data?
  4. How does this data compare to established benchmarks?
  5. Are these results good or bad?

Consider the following example regarding the recent performance of a popular toy store.

Fact: Very-Beary Toys unit sales of Teddy Bears, the premium priced SKU, have decreased year over year by 20%. At the end of quarter 4 this year (TY), Teddy Bears represented 55% of the unit sales, compared to 85% last year (LY); and 40% of the unit inventory compared to 70% LY. Year-end sell thru rose 23% over LY.

Those are the facts. The audience may jump ahead and want to understand why this is important, what the causes are, and what to do about it. This is perfectly fine, as you want them to be eager to hear what you are going to tell them. However, it’s important to first establish a data-driven foundation from which you can provide actionable recommendations. This is key to story-telling.

So What?

Next, “so what” is opinion based and blends facts with judgement. This is where analysis ties to insights that help make meaningful decisions. Share what the data means to you, and illustrate clearly what the implications are. Be sure to focus on the issues that the audience values the most. Supply chain issues may call for analysis, but not every supply chain issue is worth analysis. Understanding “so what?” will lead you to deeper, more concentrated evaluations and help you avoid analysis paralysis. The following questions will help frame the “so what?” thought process:

  1. What does this data mean to the business? Why should I care?
  2. Why are we seeing these results?
  3. What happens if this trend continues? Short Term? Long Term?
  4. What does this data mean to the company’s strategic and aspirational goals?
  5. What does this say about the company’s business models?

Consider the following answer to “so what?” using the Very-Beary Toys performance data above.

Conclusion: Teddy Bears’ product line closed the year at 55% of unit sales and are down 20% year over year. This is concerning because the Teddy Bear product line historically represents 80%+ of unit sales and are revenue and margin drivers due to their price point. Our analysis indicates 2 potential causes for the decrease in unit sales: 1) inventory replenishment did not align to the sales rate, and 2) Supplier ABC delayed and/or cancelled deliveries. Our business overestimated the Supplier ABC’s ability to deliver on time and in the quantities we need. If we don’t address this issue quickly, we risk achieving our sales ($) and margin plan next quarter and will be challenged to close the gap by year end due to supply chain constraints.

This answer provides an overview of what and why, and the performance implications if the business were to continue as is. It also provides foresight to what the recommendation may be based on the analysis and transparent judgement. With this clarity, the audience will be invoked to take action.

Now What?

As you may recall, my VP would often ask, “what is the recommendation here?” The “now what?” converts insights into actionable items. Provide a recommendation that links the facts, your analysis, and your conclusions. Clearly state what should happen next, ensuring the recommendation is fact-based and quantifiable. Try centering your answer around the following the questions:

  1. What does the business need to do to move forward?
  2. What will solve the problem?
  3. What actions are required by team, department, leadership?
  4. What areas of opportunity has this analysis uncovered? How can we use this data for our benefit?
  5. What must be done differently next time? What will we double-down on?

The following is a recommendation to address the “now what?” using the Very-Beary Toys performance data above.

Actionable Recommendation: Going forward, we recommend selecting two new Teddy Bear suppliers, a primary and secondary, that can each deliver to a 90% in-stock rate each month. This translates to a bi-weekly replenishment of 2,000 units which includes safety stock and supports next quarter’s sales plan. Each supplier must contractually agree to a 95% On-Time Delivery (OTD) rate that will be reviewed monthly via a new scorecard system developed by the supply chain team. This new scorecard will provide real-time visibility to key performance indicators for which we value and set strategic goals. Leadership is required to attend the monthly scorecard review to ensure the supplier meets performance standards.

This recommendation is quantifiable and unbiased. The days of going with “your gut feel” or relying on the legacy knowledge of the oldest stakeholder have passed. When facts and analytics are on your side, the unbiased nature of the data enables clear, concise recommendations and drama-free decisions.

In summary, I’ve found the story-telling framework of “What?”, “So What?” and “Now What?” incredibly beneficial in my work as a Supply Chain Planner, a Project Manager, and even in my personal relationships. It provides valuable insights and recommendations that motivate an audience to act. Rather than just summarizing data or “reading the news”, discuss the implications of your analysis and present a data-driven recommendation. Lastly, embedding fact-based decision making can revolutionize the way you, your company culture and supply chain operate. Most importantly, it provides the ultimate retort to the question: “So what?”.
—Amanda Montgomery, St. Onge Company

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