Memorial Day in the USA

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.

The American ideal of individual liberty and responsibility is not much in vogue these days, but in the end, may be the only principle worth a national sacrifice of blood.  May the dishonor of those who would compel the ultimate sacrifice from others for any lessor reason be as acute and lasting as the depth of their betrayal and the resulting waste and ruin of lives.

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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 coverageWhat will projected inventory be at by the start of a promotion or season.  Within sourcing, manufacturing or distribution constraints, what options do I have if my demand spikes or tanks?
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!

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, accelerated by digital technologies, including additive manufacturing, more secure IoT infrastructure, RPA, and, potentially, blockchain. 

If each node in the value network makes decisions in isolation, the total value in one or more supply chain paths becomes 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 relevant-time 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.

When these techniques are combined with intelligent visibility that allows you detect and diagnose anomalies in your supply chain, 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!

Supply Chain Action Blog

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…

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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!

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