The Winding Road toward the “Autonomous” Supply Chain (Part 2)

3d-matrix

Last week, I began this train of thought with The Winding Road toward the ‘Autonomous’ Supply Chain (Part 1)”.  Now, as this weekend approaches, I conclude my piece, but I hope to spur your ideas.

Detect, Diagnose, Decide with Speed, Precision & Advanced Analytics

Detection of incidental challenges (e.g. a shipment that is about to arrive late, a production shortfall, etc.) in your value network can be significantly automated to take place in almost real-time.   Detection of systemic challenges will be a bit more gradual and is based on the metrics that matter to your business, capturing customer service, days of supply, etc., but it is the speed (and therefore, the scope) that is now possible that drives more value today from detection.

Diagnosing the causes of incidental problems is only limited by the organization and detail of your transactional data.  Diagnosing systemic challenges requires a hierarchy of metrics with respect to cause and effect (such as, or similar to, the SCOR® model).  Certainly, diagnosis can now happen with new speed, but it is the combination of speed and precision that makes a new level of knowledge and value possible through diagnosis.

With a clean, complete, synchronized data set and a proactive view of what is happening and why, you need to decide the next best action in a timeframe where it is still relevant.  You must optimize your tradeoffs and perform scenario (“what-if”) and sensitivity analysis.

Ideally, your advanced analytics will be on the same platform as your wrangled supra data set.  The Opalytics Cloud Platform (OCP) not only gives you state of the art data wrangling, but also provides pre-built applications for forecasting, value network design and flow, inventory optimization, transportation routing and scheduling, clustering and more.  OCP also delivers a virtually unlimited ability to create your own apps for decision modeling, leveraging the latest and best algorithms and solver engines.

Speed in detection, speed and precision in diagnosis, and the culmination of speed, precision and advanced analytics in decision-making give you the power to transpose the performance of your value network to levels not previously possible (see Figure above).  Much of the entire Detect, Diagnose, Decide cycle and the prerequisite data synchronization can be, and will be, automated by industry leaders.  Just how “autonomous” those decisions become remains to be seen.

As yet another week slips into our past, I leave you with a thought from Ralph Waldo Emerson, “There is properly no history, only biography.”

Have a wonderful weekend and thank you, again, for stopping by.

The Winding Road toward the “Autonomous” Supply Chain (Part 1)

There is a lot of buzz about the “autonomous” supply chain these days.  The topic came up recently at a conference I recently attended where a topic of discussion was the supply chain of 2030. But, before we turn out the lights and lock the door to a fully automated, self-aware, supply chain decision machine, let’s take a moment and put this idea into some perspective.  I’ve heard the driverless vehicle used as an analogy for the autonomous supply chain.  However, orchestrating the value network where goods, information and currency pulse between facilities and organizations, following the path of least resistance may prove to be considerably more complex than driving a vehicle.  Most sixteen-year-olds can successfully drive a car, but you may not want to entrust your global value network to them.

Before you can have an autonomous supply chain, you need to accelerate what I call the Detect, Diagnose, Decide cycle.  In fact, as you accelerate the cycle you may learn just how much autonomy may be possible and/or wise.

Detect, Diagnose, Decide

The work of managing the value network has always been to detect challenges and opportunities, diagnose the causes, and decide what to do next –

  1. Detect (and/or anticipate) market requirements and the challenges in meeting them
  2. Diagnose the causes of the challenges, both incidental and systematic
  3. Decide the next best action within the constraints of time and capital in relevant time

The Detect, Diagnose, Decide cycle used to take a month.  Computing power, better software, and availability of data shortened it to a week.  Routine, narrowly defined, short-term changes are now addressed even more quickly under a steady state – and a lot of controlled automation is not only possible in this case, but obligatory.  However, no business remains in a steady state, and changes from that state require critical decisions which add or destroy significant value.

Data Is the Double-edged Sword

digital-value-network-matrix

Figure 1

The universe of data is exploding exponentially from networks of organizations, people and things.  Yet, many companies are choking on their own ERP data, as they struggle to make decisions on incomplete, incorrect and disparate data.  So, while the need for the Detect, Diagnose, Decide cycle to keep pace grows more ever more imperative, some organizations struggle to do anything but watch.  The winners will be those who can capitalize on the opportunities that the data explosion affords by making better decisions through advanced analytics (see Figure 1).  The time required just to collect, clean, and synchronize data for analysis remains the fundamental barrier to a better detection, diagnosis and decisions in the value network.

A consolidated data store which can connect to source systems and on which data can be programmatically “wrangled” into a supra data set would be helpful in the extreme.  While this may seem like an almost insurmountable challenge, this capability exists today.  For example, the Opalytics Cloud Platform enables you to use Python to automatically validate, reconcile and synchronize data from various sources, forming the foundation of a better Detect, Diagnose, Decide cycle.

Thanks for taking a moment to stop by.  As we enter this weekend, remember that life is short, so we should live it well.

I’ll be back next week with Part 2.

Do You Need a Network Design CoE?

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Licensed through Shutterstock. Copyright: Sergey Nivens

Whether you formally create a center of excellence or not, an internal competence in value network strategy is essential.  Let’s look at a few of the reasons why.

Weak Network Design Limits Business Success

From an operational perspective, the greatest leverage for revenue, margin, and working capital lies in the structure of the supply chain or value network.*

It’s likely that more than half of the cost and capabilities of your value network remain cemented in its structure, limiting what you can achieve through process improvements or even world-class operating practices.

You can improve the performance of existing value networks through an analysis of their structural costs, constraints, and opportunities to address common maladies like these:

  • Overemphasis on a single factor.  For example, many companies have minimized manufacturing costs by moving production to China, only to find that the “hidden” cost associated with long lead times has hurt their overall business performance.
  • Incidental Growth.  Many value networks have never been “designed” in the first place.  Instead, their current configuration has resulted from neglect and from the impact from mergers and acquisitions.
  • One size fits all.  If a value network was not explicitly designed to support the business strategy, then it probably doesn’t.  For example, stable products may need to flow through a low-cost supply chain while seasonal and more volatile products, or higher value customers, require a more responsive path.

It’s Never One and Done

At the speed of business today, you must not only choose the structure of your value network and the flow of product through that network, you must continuously evaluate and evolve both.  

Your consideration of the following factors and their interaction should be ongoing:

  1. Number, location and size of factories and distribution centers
  2. Qualifications, number and locations of suppliers
  3. Location and size of inventory buffers
  4. The push/pull boundary
  5. Fulfillment paths for different types of orders, customers and channels
  6. Range of potential demand scenarios
  7. Primary and alternate modes of transportation
  8. Risk assessment and resiliency planning

The best path through your value network structure for each product, channel and/or customer segment combination can be different.  It can also change over the course of the product life-cycle.

In fact, the best value network structure for an individual product may itself be a portfolio of multiple supply chains.  For example, manufacturers sometimes combine a low-cost, long lead-time source in Asia with a higher cost, but more responsive, domestic source.

Focus on the Most Crucial Question – “Why?”

The dynamics of the marketplace mandate that your value network cannot be static, and the insights into why a certain network is best will enable you to monitor the business environment and adjust accordingly.

Strategic value network analysis must yield insight on why the proposed solution is optimal.  This will always be more important than the “optimal” recommendation.

In other words, the context is more important than the answer.

The Time Is Always Now

For all of these reasons, value network design is more than an ad hoc, one-time, or even periodic project.  At today’s speed of competitive global business, you must embrace value network design as an essential competency applied to a continuous process.

You may still want to engage experienced and talented consultants to assist you in this process from time to time, but the need for continuous evaluation and evolution of your value network means that delegating the process entirely to other parties will definitely cost you money and market share.  

Competence Requires Capability

Developing your own competence in network design will require that you have access to enabling software.  The best solution will be a platform that facilitates flexible modeling with powerful optimization, easy scenario analysis, intuitive visualization, and collaboration.  

The right solution will also connect to multiple source systems, while helping you cleanse and prepare data. 

Through your analysis, you may find that you need additional “apps” to optimize particular aspects of your value network such as multi-stage inventories, transportation routing, and supply risk.  So, apps like these should be available to you on the software platform to use or tailor as required.  

The best platform will also accelerate the development of your own additional proprietary apps (with or without help), giving you maximum competitive advantage.  

You need all of this in a ubiquitous, scalable and secure environment.  That’s why cloud computing has become such a valuable innovation.  

If you found some of these thoughts helpful, and you are looking for value network capability to support your internal competence, you may want to have a look at the Opalytics Cloud PlatformYes, I work for Opalytics, but the Opalytics Cloud Platform has been built from the ground up for do deliver all of this.  

A Final Thought

I leave you with this final thought from Socrates:  “The shortest and surest way to live with honor in the world is to be in reality what we appear to be.”

 

*I prefer the term “value network” to “supply chain” because it more accurately describes the dynamic collection of suppliers, plants, outside processors, fulfillment centers, and so on, through which goods, currency and data flow along the path of least resistance (seeking the lowest price, shortest time, etc.) as value is exchanged and added to the product en route to the final customer.

Lean, 6-Sigma and Optimization

Introduction

There was a point in the past when, to many, the idea of “optimization” used to summon images of Greek letters juxtaposed in odd arrangements kept in black boxes that spewed out inscrutable results.  In some places, optimization was thought of as a subject best left to impractical theorists, sequestered in small cubicles deep in the bowels of the building to which few paths led and from which there were no paths out.   From that perspective, optimization was something that had to be reserved for special cases of complex decisions that had little relevance for day-to-day operations.

That perception was never reality, and today, growing numbers of business managers now understand that optimization, and those who leverage it intelligently, are not just valuable assets, but absolutely necessary to achieving and sustaining a more valuable enterprise.  Global competition mandates that managers not “settle” in their decisions, but that they constantly make higher quality decisions in less time.  Optimization helps decision-makers do just that.  The exponential increases in computing power along with advances in software, both tools and applications, have enabled the use of optimization in an ever widening array of business decisions.

Lean

The application of Lean principles is done to drive out waste through a reduction of lead times, lot sizes, and activity that does not increase the value of the whole.  Lean principles have been encapsulated by the five S’s, taken from the transliteration of five Japanese words, which, in turn, have been translated into English counterparts.  The Japanese set is as follows with English translation in parentheses.

  • Seiri (Sort) – sorting, i.e., proper arrangement of all items, storage, equipment, tools, inventory and traffic
  • Seiton (Set in order) – orderliness
  • Seiso – (Shine) cleanliness
  • Seiketsu – (Standardize) standardization, and
  • Shitsuke – (Sustain) self-discipline

The reduction of lead times and lot sizes in manufacturing has focused on reducing setup time and the frequent use of physical and visible signals for replenishment.  At times, the decision processes can be neglected in favor of execution processes, but that puts the proverbial “cart before the horse”.

Six Sigma

Six Sigma pursues reduced variability in processes.  In manufacturing, this relates most directly to the controlling a production process so that defective lots or batches do not result.  It has been encapsulated with the acronym of DMAIC:

  • Design
  • Measure
  • Analyze
  • Improve
  • Control

There has been a natural interest in the convergence of Lean and Six Sigma so that fixed constraints like lead time and lot size can be continuously attacked and reduced while, at the same time, identifying the root causes of variability and reducing or eliminating them.

There are obvious limitations to both efforts, of course.  Physics and economics of reducing lot size and lead time place limitations on Lean efforts and Six Sigma is limited by physics and market realities (the marketplace is never static).

Until it is possible to economically produce a lot size of one with a lead time of zero and infinite capacity, manufacturers will need to optimize tradeoffs.

Decision Points and Optimization

In many cases, the key levers for eliminating waste and variability in any process are the decision points.  When decisions are made that consider all the constraints, multiple objectives, and dependencies with other decisions, significant amounts of wasted time and effort are eliminated, thereby reducing the variability inherent in a process where the tradeoffs among conflicting goals and limitations are not optimized.

Intuition or incomplete, inadequate analysis will only result in decisions that are permeated with additional cost, time and risk.  Optimization not only delivers a better starting point, it gives decision-makers insight about the inputs that are most critical to a given decision.  Put another way, a planner or decision-maker needs to know the inputs (e.g. resource constraints, demand, cost, etc.) in which  a small change will change the plan and the inputs for which a change will have little impact.

Consider a few of the areas where organizations face these decision points.

Optimization

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The diagram below explains how optimization enables faster, better decisions that reduce waste and variability.

how opti works.

Of course, most people are not operations research professionals, so they need to see the results of optimization in easy-to-understand terms that are visual and sensible.   Some examples include the following:

Custom Views

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Conclusion

Lean and Six Sigma are very helpful approaches to improving business processes by eliminating waste and variability.  Optimization is a powerful tool that can and should be used within that context of that business process improvement to drive much better decisions which are the key leverage points in any business process.

A final thought to ponder over the weekend comes from Steve Sashihara in his book The Optimization Edge:  “Good optimization automates decision making; great optimization changes how things are done.”

Have a wonderful weekend!

The Process/Value/Symptom Matrix

I’ve been working on a concept and paper for a few years now, off and on.  I’ve collaborated with manufacturing industry veteran, Mike Okey, and we’ve gathered input from other supply chain experts.  We have been fortuntate to have been published by Supply Chain Digest in their “Supply Chain Comment” section. 

Mike and I have devised a Process/Symptom/Value (PSV) Matrix that provides a useful perspective for proritizing process improvement efforts by relating business decision processes to undesirable business symptoms and their impact on the drivers of enterprise value.   

You can read the first installment of the article at the link below, and I hope that you will take a moment to do so.

http://www.scdigest.com/experts/guest_11-09-13-1_Finding_Value_In_Value_Chain.php?cid=4949

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