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!

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Leadership Lessons: Learning From Experiences – Part 1

In supply chain analytics and in life, we learn best through experiences, both positive and negative.  The hardest lessons to learn are not the quantitative ones, but rather those lessons that are qualitative or behavioral – how we interact with others.

Take the real, but anonymous, case of an analyst working on process improvement for a company that sold jewelry through direct marketing.  The challenge the company faced was predicting demand across a size curve.  After the rings were promoted, the company was left with too many rings that did not sell.  These remaining rings disproportionately consisted of the less frequently ordered sizes.

The analyst proposed a solution:

Purchase assembled rings for 90% of the most frequently ordered sizes and purchase settings and stones to cover the forecast for the remaining 10% of those sizes and for the less frequently ordered sizes.  If demand exceeded 90% of the forecast for the more common sizes or if sizes were ordered from either tail of the size distribution, they would be assembled to order. 

Of course, this approach had the advantage of eliminating left over rings without materially impacting customer service.  Furthermore, the stones and settings could be salvaged for a significant portion of the procurement cost.

She thought it sounded like a slam-dunk!

However, her proposal was quickly rejected after minimal consideration – dismissed with a few objections that more or less amounted to “we haven’t ever done that and (therefore) it won’t work.”  However, after some time, when she was no longer working on that project, that very solution was eventually implemented, resulting in improved fill rates and reduced obsolescence! 

So what can she (and we) learn from her experience?  It might be convenient for her to chalk the failure of her proposal to the failings of her colleagues.  Were they threatened?  Were they in imaginative?  Were they just plain stubborn?  Are they unintelligent?

It is unlikely that any of these are the case.  Instead of blaming others, she could challenge at her own approach.  For example, she may have failed to comprehend the perspective of her audience, both as individuals and as a group.  She could have tried to understand what kind of message they were capable of receiving in terms of the following (as examples):

  • Extent of change that the rest of the team could accept
  • Her colleagues point of view (e.g. Are there real or perceived reasons why this won’t work?  Has it been tried before?)
  • What did each member of the team need to get out of this interaction

If she can forget about getting appropriate credit for the idea and deliver her message with these things in mind such that it can be received and appreciated by her teammates, then I suspect that she very well may have been successful.

Effectively interacting with others requires this kind of 360 degree thinking that you see visually on Google Earth or in the special effects replays in NFL broadcasts.

It is also critical to remember that while you may have a great idea, someone else may have a better idea, so listening must be both a skill and a habit that you continually hone.

Bear in mind that common sense is often the best sense:

  • Keep an open mind (yes, we all have blind spots)
  • Put yourself in the other person’s shoes (we are all too self-centered)
  • Love your neighbor as yourself (remembering from the parable of the Good Samaritan that everyone is our neighbor)

For more thoughts on effectively interacting with teams, please take a look at these posts on Supply Chain Action:

Leadership Is Not Just Telling Other People What to Do

Leadership:  Motivation or Manipulation

Or my article in Analytics magazine:  Why the soft side of analytics is so hard to manage

I also highly recommend Dr. Jeannie Kahwajy.  Find her at her website, Effective Interactions.

Thanks for stopping by and have a wonderful weekend!

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