On Memorial Day Weekend

Let us remember and honor all who serve a higher good than their own comfort and fulfillment. This is Memorial Day weekend for us in the U.S., but may our memory serve us well when the calendar does not. Let us never forget when uncommon valor becomes a common virtue and those who shirk not their duty, nor shrink from their sacrifice leave their families bereaved. May God bless all who have bravely and knowingly walked into the “valley of the shadow of death” but have never returned, freeing others to pass by unharmed. May they rest forever in our hallowed memories of the price of peace with liberty.  May God bless the families who will always suffer without them. And, may God bless those who have returned but not without cost and their families who suffer with them.

Digital Transformation of Supply Chain Planning

A couple of years back, IBM released a study “Digital operations transform the physical” (capitalization theirs).

Citing client examples the report states,

“Perpetual planning enables more accurate demand and supply knowledge, as well as more accurate production and assembly status that can lower processing and inventory costs . . .

Analytics + real-time signals = perpetual planning to optimize supply chain flows

They are describing the space to which manufacturers, retailers, distributors, and even service providers (like say, health care delivery) need to move rapidly with value network planning.  This is a challenging opportunity for software providers, and the race is on to enable this in a scalable way.  The leading software providers must rapidly achieve the following:

1)      Critical mass by industry

2)      Custody of all the necessary data and flows necessary for informing decision-makers of dynamic, timely updates of relevant information in an immediately comprehensible context

3)      Fast, relevant, predictive and prescriptive insights that leverage up-to-the-minute information

Some solution provider (or perhaps a few, segmented by industry) is going to own the “extended ERP” (ERP+ or EERP to coin a phrase?) data.  Whoever does that will be able to provide constantly flowing intelligent metrics and decision-support (what IBM has called “perpetual planning”) that all companies of size desperately need.  This means having the ability to improve the management of working capital, optimize value network flows, minimize value network risk, plan for strategic capacity and contingency, and, perhaps most importantly, make decisions that are “in the moment”, spans the entire value networkThat is the real prize here and a growing number of solution providers are starting to turn their vision toward that goal.  Many are starting to converge on this space from different directions – some from inside the enterprise and some from the extra-enterprise space.

The remaining limiting factor for software vendors and their customers aspiring to accomplish this end-to-end, up-to-the-moment insight and analysis remains the completeness and cleanliness of data.  In many cases, too much of this data is just wrong, incomplete, spread across disparate systems, or all of the above.  That is both a threat and an opportunity.  It is a threat because speedily providing metrics, even in the most meaningful visual context is worse than useless if the data used to calculate the metrics are wrong.  An opportunity exists because organizations can now focus on completing, correcting and harmonizing the data that is most essential to the metrics and analysis that matter the most.

What are you doing to achieve this capability for competitive advantage? 

I work for AVATA, and in the interest of full disclosure, AVATA is an Oracle partner.  With that caveat, I do believe that Oracle Supply Chain Planning Cloud delivers leading capabilities for perpetually optimizing your value network, while Oracle Platform-as-a-Service (soon to integrate DataScience.com) provides unsurpassed power to wrangle data and innovate at the “edge”.  Both are worth a look.

Thanks for stopping by.  I’ll leave you with this thought of my own:

“Ethical corporate behavior comes from hiring ethical people.  Short of that, no amount of rules or focus on the avoidance of penalties will succeed.”

Have a wonderful weekend!

Unconventional Wisdom?

Over years of working with clients, I have found that the most effective way to evaluate an a strategic software project and assess its value has been through a small scale collaborative effort in which both client and vendor invest and participate.  Such an approach serves the best interest of both parties, not just the vendor.

This is true when a client-specific, use-case-specific solution is required for making very complex, very valuable decisions.  This collaborative approach provides several important benefits for the client:

A) Alignment – The vendor quickly gains deep insight into the client’s specific requirements.  In this way the vendor can be sure to capture all key requirements and fully test and demonstrate the value of the solution.  In many cases, the prototype can form the basis of the first phase of the implementation, so the project is ready to start, should the client decide to proceed.

C) Risk Reduction – Because of the learning that takes place prior to any major commitment on the part of the client, the risk associated with a decision to proceed with the overall project is greatly reduced.  The client’s decision regarding whether or not to proceed with the project is more informed than it could be in any other way.  For example, the estimate of the likely return on investment is much more precise.

D) Client Learning – The client learns the vendor’s software and its capabilities better than they could in any classroom setting and  in a very short period of time.

E) Time to Value – The alignment, risk reduction, and client learning drive a faster time-to-value for the overall project.

A joint investment in a small-scale collaborative effort is also a prudent approach.

As a case in point, consider an investment of $10K to evaluate a project costing say $200,000, with a potential ROI of $1 million or more per year.  One might say that it not only makes good business sense to invest the $10K, but that the value achieved in terms of alignment, risk reduction, learning, and time to value make it a bargain.

This seems like a wise approach to me, but unfortunately, it is far too infrequent.

Thanks for stopping by.  I’ll leave you with these few words to ponder from Sir Ronald Gould, “When all think alike, none thinks very much.”

Have a wonderful weekend!

What are “Analytics”?

“Analytics” is one of those business buzz words formed by transforming an adjective into a noun. 

So forceful and habitual is such misuse of language that one might call it a compulsion among business analysts and writers.

The term “analytics” commonly refers to software tools that can be used to organize, report, and sometimes visualize data in attempt to lend meaning for decision-makers.  These capabilities have been advanced in recent years so that many types of graphical displays can be readily employed to expose data and try to make information from it.   “Analytics” has been used to refer to a very broad array of software applications.  Numerous industry analysts have attempted to segment these applications in various ways.  “Analytics” refers to so many kinds of applications that it is useful to establish some broad categories.

A simple, though imperfect, scheme such as the following may be the most useful where the potential value that can be achieved through each category increases from #1 through #4.

Reports – repetitively run displays of pre-aggregated and sorted information with limited or no user interactivity.

Dashboards – frequently updated displays of performance metrics which can be displayed graphically.  They are ideally tailored to the needs of a given role.  Dashboards support the measurement of performance, based on pre-aggregated data with some user selection and drill-down capability.  Hierarchies of metrics have been created that attempt to facilitate a correlation between responsibility and performance indicators.  The most common such model is the Supply Chain Operations Reference Model (SCOR Model) that was created and is maintained by the Supply Chain Council.

Data Analysis Tools – interactive software applications that enable data analysts to dynamically aggregate, sort, plot, and otherwise explore data, based on metadata.  Significant advancements have been made in recent years to dramatically expand the options for visualizing data and accelerating the speed at which these tools can generate results.

Decision Support/Management Science Tools – simulation, optimization, and other approaches to multi-criteria decisions which require the application of statistics and mathematical modeling and solving.

Let’s focus on Decision Support/Management Science Tools, the category with the most potential for adding value to strategic (high value) decision-making in a sustained fashion. 

So, then, if that is what analytics are, do they enable higher quality decisions in less time, and if so, to what extent are those better decisions in less time driving cash flow and value for their business?  These are critically important questions because improved, integrated decision-making that is based in facts and adjusted for risk drives the bottom line.

Execution is good, but operational execution under a poor decision set is like going fast in the wrong direction.  It is bad, but perhaps not immediately fatal.  Poor decisions will put a business under very quickly.

Enabling higher quality decisions in less time depends on the decision-maker, but it can also depend on the tools employed and the skills of the analysts using the tools. 

The main activities in using these tools involve the following:

  1. Sifting through the oceans of data that exist in today’s corporate information systems
  2. Synthesizing the relevant data into information (a thoughtful data model within an analytical application is helpful, but not sufficient)
  3. Presenting it in such a way so that a responsible manager can combine it with experience and quickly know how to make a better decision

Obtaining a valuable result requires careful preparation and skilled interaction, asking the right questions initially and throughout the above activities.

Some of the questions that need to be asked before the data can be synthesized into information in a useful way are represented by those given below:

  1. What is the business goal?
  2. What decisions are required to reach the goal?
  3. What are the upper and lower bounds of each decision? (Which outcomes are unlivable?)
  4. How sensitive is one decision to the outcome of other, interdependent decisions?
  5. What risks are associated with a given decision outcome?
  6. Will a given decision today impact the options for the same decision tomorrow?
  7. What assumptions are implicitly driven by insufficient data?
  8. How reliable is the data upon which the decision is based?
    • Is it accurate?
    • How much of the data has been driven by one-time events that are not repeatable?
    • What data is missing?
    • Is the data at the right level of detail?
    • How might the real environment in which the decision is to be implemented be different from that implied by the data and model (i.e. an abstraction of reality)?
    • How can the differences between reality and its abstraction be reconciled so that the results of the model are useful?

Ask the right questions.

Know the relative importance of each.

Understand which techniques to apply in order to prioritize, analyze and synthesize the data into useful information that enables faster, better decisions.

We often think of change when a new calendar year rolls around.  Since this is my first post of the new year, I”ll leave you with one of my favorite quotes on change.  Leo Tolstoy:  “Everybody thinks of changing humanity, and nobody thinks of changing himself.”

Have a wonderful weekend!

Merry Christmas!

Phillips Brooks

Phillips Brooks

On this Christmas, I would like to share with you with the words of Phillips Brooks, penned in 1868, which embody my favorite thoughts at this time of year.  I wish you a wonderful Christmas and holiday season and the best of New Years.

O little town of Bethlehem,
How still we see thee lie;
Above thy deep and dreamless sleep
The silent stars go by:
Yet in thy dark streets shineth
The everlasting Light;
The hopes and fears of all the years
Are met in thee tonight.

 For Christ is born of Mary;
And gathered all above,
While mortals sleep, the angels keep
Their watch of wondering love.
O morning stars, together
Proclaim the holy birth;
And praises sing to God the King,
And peace to men on earth.

How silently, how silently,
The wondrous gift is given!
So God imparts to human hearts
The blessings of His heaven.
No ear may hear His coming,
But in this world of sin,
Where meek souls will receive Him still,
The dear Christ enters in.

O holy Child of Bethlehem,
Descend to us, we pray;
Cast out our sin, and enter in,
Be born in us today.
We hear the Christmas angels
The great glad tidings tell;
O come to us, abide with us,
Our Lord Emmanuel.

The Value Network, Optimization & Intelligent Visibility

The supply chain is more properly designated a value network through which many supply chains can be traced. Material, money and data pulse among links in the value network, following the path of least resistance.

If each node in the value network makes decisions in isolation, the potential grows for the total value in one or more supply chain paths to be less than it could be

In the best of all possible worlds, each node would eliminate activities that do not add value to its own transformation process such that it can reap the highest possible margin, subject to maximizing and maintaining the total value proposition for a value network or at least a supply chain within a value network.  This is the best way to ensure long-term profitability, assuming a minimum level of parity in bargaining position among trading partners and in advantage among competitors.

Delivering insights to managers that allow them to react “in the moment” without compromising the value of the network (or a relevant portion of a network, since value networks interconnect to form an extended value web) remains a challenge.

The good news is that many analytical techniques and the mechanisms for delivering them in timely, distributed ways are becoming ubiquitous.  For example, optimization techniques and scenarios can provide insights into profitable ranges for decisions, marginal benefits of incremental resources, and robustness of plans, given uncertain inputs.

If these capabilities can be combined with intelligent visibility that allows you to see every area and metric of your end-to-end supply chain in context, then everyone can make coordinated decisions as they execute.  

I will leave you with these words of irony from Dale Carnegie, “You make more friends by becoming interested in other people than by trying to interest other people in yourself.”

Thanks again for stopping by and have a wonderful weekend!

Resilience Versus Agility

Just a short thought as we move into this weekend . . .

Simple definitions of resiliency and agility as they relate to your value network might be as follows:

Resiliency:  The quality of your decisions and plans when their value is not significantly degraded by variability in demand and/or changes in your competitive and economic environment.

Agility:  The ability to adjust your plans and execution for maximum value by responding to the marketplace based on variability in demand and/or changes in your competitive and economic environment.

You can take an analytical approach that will make your plans and decisions resilient and also give you insights into what you need to do in order to be agile.

You need to know the appropriate analytical techniques and how to use them for these ends.

A capable and usable analytical platform can mean the difference between knowing what you should do and actually getting it done.

For example, scenario-based analysis is invaluable for understanding agility, while range-based optimization is crucial for resiliency.

Do you know how to apply these techniques?

Do you have the tools to do it continuously?

Can you create user and manager ready applications to support resiliency and agility?

Finally, I leave you with this thought from Curtis Jones:  “Life is our capital and we spend it every day.  The question is, what are we getting in return?”

Thanks for stopping by.  Have a wonderful weekend!

What’s Best for Advanced Analytics: A Package or a Platform?

ocp-for-data-sheet

The Dilemma

Many organizations struggle with the dilemma of deciding between a commercially available, prepackaged, off-the-shelf, software application to address a particular decision or planning challenge versus creating something completely from scratch.

There is an increasingly viable third option – leveraging a platform for rapidly developing and supporting a secure, scalable, enterprise-class application for business users that embeds powerful analytical techniques like optimization, machine learning, etc.  The accelerating viability of platforms, especially in the cloud, for developing your own applications has made this third option something that really can’t be overlooked.

In most, if not all cases, this availability of a platform like the Opalytics Cloud Platform means that building your own app for strategically differentiating decisions is not only doable, but imperative.

The Key Questions

The rather obvious question is how to compare apples with bowling balls, so to speak.

Alternatives for an off-the-shelf, prepackaged, software application approach can’t be evaluated in the same way as alternatives for a platform approach.

So, I offer you the following questions to determine which approach is best for a given use case:

  1. Is the financial opportunity strategically significant (i.e. will it make a difference in the value of the enterprise?)?
  2. Do you need to capture as much of the opportunity as possible (you can’t settle for 70% or 80%)?
  3. Because of #1, #2 and other reasons, do you need to make these decisions to achieve this opportunity in a way that is different from the competition and that can’t be copied by them?
  4. Do you intend to expand the use of advanced analytics and continuously improve the decision models you intend to build, thereby continuing to differentiate yourself in the market?

If the answers to all four of these questions of these questions is “yes”, then you should be focusing your attention on a “platform”, not on off-the-shelf packages.  If the answer to either of the first two is “no”, then you should probably look for a prepackaged application.

At Opalytics, we have the best of both approaches.  

The Opalytics Cloud Platform enables you to load, organize, clean, synchronize, share and analyze data from multiple sources so you can solve complex challenges rapidly, all with state-of-the-art technology.  Opalytics also has a growing number of prepackaged apps already on the platform that you can begin using immediately to solve challenges associated with value network design, multi-echelon inventory optimization, transportation routing, and supply chain risk, etc, while building your own enterprise-class apps that leverage a wide range of advanced techniques is flexible and fast on the Opalytics Cloud Platform.

Thanks for stopping by.  Have a wonderful weekend, for which I leave you this thought from Ralph Waldo Emerson:

“Though we travel the world over to find the beautiful, we must carry it within us, or we find it not.”

 

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!

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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