“Moneyball” and Your Business

 

It’s MLB playoff time, and my team (the Tribe) is there, again.  (Pregnant pause to enjoy the moment.)

A while back, the film “Moneyball” showed us how the Oakland A’s built a super-competitive sports franchise on analytics, essentially “competing on analytics”, within relevant business parameters of a major league baseball franchise.  The “Moneyball” saga and other examples of premier organizations competing on analytics were featured in the January 2006 Harvard Business Review article, “Competing on Analytics” (reprint R0601H) by Thomas Davenport, who also authored the book by the same name.

The noted German doctor, pathologist, biologist, and politician, Rudolph Ludwig Karl Virchow called the task of science “to stake out the limits of the knowable.”  We might paraphrase Rudolph Virchow and say that the task of analytics is to enable you to stake out everything that you can possibly know from your data.

So, what do these thoughts by Davenport and Virchow have in common?

In your business, you strive to make the highest quality decisions today about how to run your business tomorrow with the uncertainty that tomorrow brings.  That means you have to know everything you possibly can know today.  In an effort to do this, many companies have invested, or are considering an investment, in supply chain intelligence or various analytics software packages.  Yet, many companies who have made huge investments know only a fraction of what they should know from their ERP and other systems.  Their executives seem anxious to explore “predictive” analytics or “AI”, because it sounds good.  But, investing in software tools without understanding what you need to do and how is akin to attempting surgery with wide assortment of specialized tools, but without having gone to medical school.

Are you competing on analytics?

Are you making use of all of the data available to support better decisions in less time?

Can you instantly see what’s inhibiting your revenue, margin and working capital goals across the entire business in a context?

Do you leverage analytics in the “cloud” for computing at scale and information that is always on and always current?

I appreciate everyone who stops by for a quick read.  I hope you found this both helpful and thought-provoking.

As we enter this weekend, I leave you with one more thought that relates to “business intelligence” — this time, attributed to Socrates:

“The wisest man is he who knows his own ignorance.”

Do you know what you don’t know?  Do I?

Have a wonderful weekend!

Does Your Demand Planning Process Include a “Quantitative Sanity Range Evaluation”?

There is a process that should be part of both your demand planning and your sales and operations planning.  The concept is simple – how do you find the critical few forecasts that require attention, so that planner brainpower is expended on making a difference and not hunting for a place to make a difference?  I have heard this process called a “Forecast Reality Check” and a “Forecast Reasonability Check”.  Just to be difficult, I’ll call it a Quantitative Sanity Range Evaluation (I have my own reasons.)  It may be similar in some ways to analyzing “forecastability” or a “demand curve analysis”, but different in at least one important aspect – the “sanity range” is calculated through bootstrapping (technically, you would be bootstrapping a confidence interval, but please allow me the liberty of a less technical name – “sanity range”).  A QSRE can be applied across industries, but it’s particularly relevant in consumer products, where I have seen a version of this implemented first hand by Allan Gray, a really smart gentleman – back when I worked with him for End-to-End Analytics – just so you know I didn’t think this all up on my own!

At a minimum, QSRE must consider the following components:

  1. Every level and combination of the product and geographical hierarchies
  2. A quality quantitative forecast
  3. A sanity range out through time
  4. Metrics for measuring how well a point forecast fits within the sanity range
  5. Tabular and graphical displays that are interactive, intuitive, always available, and current.

If you are going to attempt to establish a QSRE, then I would suggest five best practices:

1.  Eliminate duplication.  When designing a QSRE process (and supporting tools), it is instructive to consider the principles of Occam’s razor as a guide:

– The principle of plurality – Plurality should not be used without necessity

– The principle of parsimony – It is pointless to do with more what can be done with less

These two principles of Occam’s razor are useful because the goal is simply to flag unreasonable forecasts that do not pass a QSRE, so that planners can focus their energy on asking critical questions only about those cases.

2. Minimize human time and effort by maximizing the power of cloud computing.  Leverage the fast, ubiquitous computing power of the cloud to deliver results that are self-explanatory and always available everywhere, providing an immediately understood context that identifies invalid forecasts. 

3. Eliminate inconsistent judgments By following #1 and #2 above, you avoid inconsistent judgments that vary from planner to planner, from product family to product family, or from region to region.

4. Reflect reality.  Calculations of upper and lower bounds of the sanity range should reflect the fact that uncertainty grows with each extension of a forecast into a future time period.  For example, the upper and lower limits of the sanity range for one period into the future should usually be narrower than the limits for two or three periods into the future.  These, in turn, should be narrower than the limits calculated for more distant future periods.  Respecting reality also means capturing seasonality and cyclical demand in addition to month-to-month variations.  A crucial aspect of respecting reality involves calculating the sanity range for future demand from what actually happened in the past so that you do not force assumptions of normality onto the sanity range (this is why bootstrapping is essential).  Among other things, this will allow you to predict the likelihood of over- and under-shipment.

5. Illustrate business performance, not just forecasting performance with sanity ranges.  The range should be applied, not only from time-period to time period, but also cumulatively across periods such as months or quarters in the fiscal year.

If you are engaged in demand planning or sales and operations planning, I welcome to know your thoughts on performing a QSRE.

Thanks again for stopping by Supply Chain Action.  As we leave the work week and recharge for the next, I leave you with the words of John Ruskin:

“When skill and love work together, expect a masterpiece.”

Have a wonderful weekend!

The Time-to-Action Dilemma in Your Supply Chain



dreamstime_m_26639042If you can’t answer these 3 sets of questions in less than 10 minute
s
(and I suspect that you can’t), then your supply chain is not the lever it could be to
 drive more revenue with better margin and less working capital:
1) What are inventory turns by product category (e.g. finished goods, WIP, raw materials, ABC category, etc.)?  How are they trending?  Why?
2) What is the inventory coverageHow many days of future demand can you satisfy with the inventory you have on-hand right now?
3) Which sales orders are at risk and why?  How is this trending?  And, do you understand the drivers?

Global competition and the transition to a digital economy are collapsing your slack time between planning and execution at an accelerating rate.

 

You need to answer the questions that your traditional ERP and APS can’t from an intelligent source where data is always on and always current so your supply chain becomes a powerful lever for making your business more valuable.

 

You need to know the “What?” and the “Why? so you can determine what to do before it’s too late.  

 

Since supply chain decisions are all about managing interrelated goals and trade-offs, data may need to come from various ERP systems, OMS, APS, WMS, MES, and more, so unless you can consolidate and blend data from end-to-end at every level of granularity and along all dimensions, you will always be reinventing the wheel when it comes to finding and collecting the data for decision support.  It will always take too long.  It will always be too late.

 

You need diagnostic insights so that you can know not just what, but why.  And, once you know what is happening and why, you need to know what to do — your next best action, or, at least, viable options and their risks . . . and you need that information in context and “in the moment”.

 

In short, you need to detect opportunities and challenges in your execution and decision-making, diagnose the causes, and direct the next best action in a way that brings execution and decision-making together.

 

Some, and maybe even much, of detection, diagnosis and directing the next best action can be automated with algorithms and rules.  Where it can be, it should be.  But, you will need to monitor the set of opportunities that can be automated because they may change over time.

 

If you can’t detect, diagnose and direct in a way that covers your end-to-end value network in the time that you need it, then you need to explore how you can get there because this is at the heart of a digital supply chain.

As we approach the weekend, I’ll leave you with this thought to ponder:
Leadership comes from a commitment to something greater than yourself that motivates maximum contribution from yourself and those around you, whether that is leading, following, or just getting out of the way.”
Have a wonderful weekend!
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