“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!

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Analytics vs. Humalytics

I have a background in operations research and analysis so, as you might expect, I am biased toward optimization and other types of analytical models for supply chain planning and operational decision-making.   Of course, you know the obvious and running challenges that users of these models face:

1. The data inputs for such a model are never free of defects
2. The data model that serves as the basis for a decision model is always deficient as a representation of reality
3. As soon a model is run, the constantly evolving reality increasingly deviates from the basis of the model

Still, models and tools that help decision-makers integrate many complex, interrelated trade-offs can enable significantly better decisions.

But, what if we could outperform very large complex periodic decision models through a sort of “existential optimization” or as a former colleague of mine put it, “humalytics“?

Here is the question expressed more fully:

If decision-makers within procurement, manufacturing and distribution and sales had the “right time” information about tradeoffs and how their individual contributions were affecting their performance and that of the enterprise, could they collectively outperform a comprehensive optimization/decision model that is run periodically (e.g. monthly/quarterly) in the same way that market-based economies easily outperform centrally planned economies?

I would call this approach “humalytics” (borrowed from a former colleague, Russell Halper, but please don’t blame him for the content of this post!), leveraging a network of the most powerful analytical engines – the human brain, empowered with quantified analytical inputs that are updated in “real-time” or as close to that as required.  In this way, the manager can combine these analytics with factors that might not be included in a decision model from their experience and knowledge of the business to constantly make the best decisions with regard to replenishment and fulfillment through “humalytics”, resulting in constantly increasing value of the organization.

In other words, decision-maker would have instant, always-on access to both performance metrics and the tradeoffs that affect them.  For example, a customer service manager might see a useful visualization of actual total cost of fulfillment (cost of inventory and cost of disservice) and the key drivers such as actual fill rates and inventory turns as they are happening, summarized in the most meaningful way, so that the responsible human can make the most informed “humalytical” decisions.

Up until now, the answer has been negative for at least two reasons:

A. Established corporate norms and culture in which middle management (and maybe sometimes even senior management) strive diligently for the status quo.

B. Lack of timely and complete information and analytics that would enable decision-makers to act as responsible, accountable agents within an organization, the same way that entrepreneurs act within a market economy.

With your indulgence, I’m going to deal with these in reverse order.

A few software companies have been hacking away at obstacle “B.”, and we may be approaching a tipping point where the challenge of accurate, transparent information and relevant, timely analytics can be delivered in near real-time, even on mobile devices, allowing the human decision-makers to constantly adjust their actions to deliver continuously improved performance.  This is what I am calling “humalytics”.

But the network of human decision-makers with descriptive metrics is not enough.  Critical insights into tradeoffs and metrics come through analytical models, leveraging capabilities like machine learning, optimization, RPA, maybe in the form of “mini-apps” models that operate on a curated supra set of data that is always on and always current.  So, at least two things are necessary:

1. Faster optimization and other analytical modeling techniques from which the essential information is delivered in “right time” to each decision-maker

2. An empowered network of (human) decision-makers who understand the quantitative analytics that are delivered to them and who have a solid understanding of the business and their part in it

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. (ftp://ftp.deas.harvard.edu/techreports/tr-06-11.pdf)  Maybe, we don’t need swarm of robots, after all.  Maybe we just need empowered decision-makers who not only engage in Sales and Operations Planning (or, if you will, Integrated Business Planning), but integrated business thinking and acting on an hourly (or right time) basis.

What think you?

If you think this might make sense for your business, or if you are working on implementing this approach, I’d be very interested to learn your perspective and how you are moving forward.

I leave you with these words from Leo Tolstoy, “There is no greatness where there is no simplicity, goodness, and truth.”

Have a wonderful weekend!

Metrics, Symptoms and Cash Flow

Metrics can tell us if we are moving in the right or wrong direction and that, in itself, is useful.  However, metrics by themselves do not help us assess our competitive position or aid us in prioritizing our efforts to improve.

To understand our competitive position, metrics need to be benchmarked against comparable peers. Benchmarking studies are available, some of them free.  They tell us where we stand relative to others in the industry, provided the study in question has sufficient other data points from your industry (or sub-industry segment).

Many times, getting relevant benchmarks proves challenging.  But once we have the benchmarks, then what?

Does it matter if we do not perform as well as the benchmark of a particular metric?  If that metric affects revenue growth, margins, return on assets, or available capital, it may matter significantly.

But, we are left to determine how to improve the metrics and with which metrics to start.  

Consider an alternative path.  Begin with the undesirable business symptoms that keep you up at night and give you that bad feeling in the pit of your stomach.

Relate business processes to symptoms and map potential root causes within each business process to undesirable business symptoms.

Multiple root causes in multiple business processes can relate to a single symptom.  On the other hand, a single root cause may be causing multiple undesirable symptoms.  Consequently, we must quantify and prioritize the root causes.

“Finding the Value in Your Value Network” outlines a straightforward, systematic approach to prioritizing and accelerating process improvements.  I hope you will take a look at that article and let me know your thoughts.

Thanks for having a read.  Remember that “You cannot do a kindness too soon, for you never know how soon it will be too late.”

Have a wonderful weekend!

Make Analytics Useful, Meaningful and Actionable

Last week, I identified reasons for the organizational malady of failing to fully leverage analytics to make higher quality decisions in less time.  As promised, this week, I want to share a remedy.

For the analyst, I recommend the following:

1. Put yourself in the shoes of the decision-maker.  Try to step back from the details of your analysis for a moment and ask yourself the questions he or she will ask.
2. Engage your decision-maker in the process.  Gather their perspective as an input.  Don’t make any assumptions.  Ask lots of questions.  They probably know things that you don’t know about the question you are trying to answer.  Draw them out.  Schedule updates with the decision-maker, but keep them brief and focused on essentials.  Ask for their insight and guidance.  It may prove more valuable than you think.
3. Take time to know, explore and communicate the “Why?” of your analysis – Why is the analysis important?  Why are the results the way they are?  To what factors are the results most sensitive and why?  Why are the results not 100% conclusive?  What are the risks and why do they exist?  What are the options? 
4. Make sure you schedule time to explain your approach and the “Why?”  Your decision-maker needs to know beforehand that this is what you are planning to do.  You will need to put the “Why”? in the context of the goals and concerns of your decision-maker.
5. Consider the possible incentives for your decision-maker to ignore your recommendations and give him or her reasons to act on your recommendations that are also consistent with their own interest.
6. “A picture is worth a thousand words.”  Make the analysis visual, even interactive, if possible.
7. Consider delivering the results in Excel (leveraging Visual Basic, for example), not just in a Power Point presentation or a Word document.  In the hands of a skilled programmer and analyst, amazing analysis and pictures can be developed and displayed through Visual Basic and Excel.  Every executive already has a license for Excel and this puts him or her face-to-face with the data (hopefully in graphical form as well as tabular).  You may be required to create a Power Point presentation, but keep it minimal and try to complement it with Excel or another tool that actually contains the data and the results of your analysis. 

Frustration with your decision-making audience will not help them, you, or the organization.  Addressing them where they are by intelligently and carefully managing the “soft” side of analytics will often determine whether you make a difference or contribute to a pile of wasted analytical effort. 

Thanks again for stopping by.  I hope that these suggestions will improve the usefulness of your analysis.  As a final thought for the weekend, consider these words from Booker T. Washington, “There is no power on earth that can neutralize the influence of a high, pure, useful and simple life.” 

Have a wonderful weekend!

Why the Soft Side of Analytics Is So Hard to Manage

I’m borrowing both inspiration and content from two good friends and long-time supply chain professionals, Scott Sykes and Mike Okey.  They deserve the credit for the seminal thoughts.  Any blame for muddling the ideas or poorly articulating them is all mine.

If you are an analyst, operations researcher or quantitative consultant, you probably enjoy the “hard” side of analytics.  What we often struggle with as analysts is what you might call the “soft” side of analytics which is always more challenging than the “hard” stuff.  Here are a few of the reasons why.

Many times, the problem is not insufficient data, defective data, inadequate data models, or even incompetent analysis.  Often, the reason that better decisions are not made in less time is that many companies of all sizes have some, if not many, managers and leaders who struggle to make decisions with facts and evidence . . . even when it is spoon-fed to them.  One reason is that regardless of functional or organizational orientation, some executives tend not to be analytically competent or even interested in analysis.  As a result, they tend to mistrust any and all data and analyses, regardless of source.

In other situations, organizations still discount robust analysis because the resulting implications require decisions that conflict or contrast with “tribal knowledge”, institutional customs, their previous decisions, or ideas that they or their management have stated for the record.  Something to keep in mind is that at least some of the analysis may need to support the current thinking and direction of the audience that is analytically supportable if you want the audience to listen to the part of your analysis that challenges current thinking and direction.

Understanding the context or the “Why?” of analysis is fundamental to benefiting from it.  However, there are times when the results of an analysis can be conflicting or ambiguous.  When the results of analysis don’t lead to a clear, unarguable conclusion, then managers or executives without the patience to ask and understand “Why?” may assume that the data is bad or, more commonly, that the analyst is incompetent.

Perhaps the most difficult challenge an organization must overcome in order to raise the level of its analytical capability, is the natural hubris of senior managers who believe that their organizational rank defines their level of unaided analytical insight.  Hopefully, as we grow older, we also grow wiser.  The wiser we are, the slower we are to conclude and the quicker we are to learn.  The same ought to be true for us as we progress up the ranks of our organization, but sometimes it isn’t.

So, if these are the reasons for the organizational malady of failing to fully leverage analytics to make higher quality decisions in less time, what is the remedy?

The remedy for this is the subject of next week’s post, so please “stay tuned”!

Thanks for having a read.  Whether you are an executive decision-maker, a manager, or an analyst, I hope these ideas have made you stop and think about how you can help your organization make higher quality decisions in less time.

A final thought comes from T.S. Eliot, “The only wisdom we can hope to acquire is the wisdom of humility—humility is endless.”

Have a wonderful weekend!