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

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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

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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.

A Demand Plan Sanity Check: Five Best Practices

Sch 1There is a process that is fast becoming a necessary and key component of both demand planning and sales and operations planning.  I have heard it described as “forecastability”and “demand curve analysis”, among other terms, but, here, I will call it a “Demand Plan Sanity Check” or DPSC for short.  I am seeing this across industries, but particularly in consumer products.  The concept is simple – how does one identify the critical few forecasts that require the skill and experience of demand planners, so that planner brainpower is expended on making a difference and not hunting for a place to make a difference.

At a minimum, a DPSC must consider the following components:

  1. Consideration of every level and combination of the product and geographical hierarchies
  2. A very high quality quantitative forecast
  3. A statistically developed range of “sanity” out through time
  4. Metrics for measuring “sanity”
  5. Tabular and graphical displays that are interactive, intuitive, always available, and current.

If you are going to attempt to establish a DPSC, then you need to incorporate the following five best practices:

1.  Eliminate duplication.  When designing a DPSC process (and supporting tools), it is instructive to consider the principles of Occam’s razor as a guide:

– The principle of plurality – Plurality should not be used without necessity

– The principle of parsimony – It is pointless to do with more what can be done with less

These two principles of Occam’s razor are useful because the goal is simply to flag unreasonable forecasts that do not pass a statistical “sanity check”, so that planners can focus their energy on asking critical questions only about those cases.

2. Minimize human time and effort by maximizing the power of cloud computing.  Leverage the fast, ubiquitous computing power of the cloud to deliver results that are self-explanatory and always available everywhere, providing an immediately understood context that identifies invalid forecasts and minimizes the need for planners to sort through and compare massive amounts of data manually and individually.

3. Eliminate inconsistent judgments By following #1 and #2 above, you avoid inconsistent judgments that vary from planner to planner, from product family to product family, or from region to region.  A DPSC tool should present the minimum essential data that will flag forecasts with questionable validity for planners so that they can leverage their skill, experience and intelligence on these exceptions rather than trying to apply their individual assessments to many different sets of data in order to identify the exceptions.

4. Reflect statistical realities.  Any calculations of upper and lower bounds of “sanity” should reflect the fact that uncertainty grows with each extension of a forecast into a future time period.  For example, the upper and lower limits of “sanity” for one period into the future should usually be narrower than the limits for two or three periods into the future.  These, in turn, should be narrower than the limits calculated for more distant future periods.  Respecting statistical realities also means reflecting seasonality and cyclical demand in addition to month-to-month variations.  It also means capturing the actual variability in demand and forecast error so that you do not force assumptions of normality onto the sanity check range(s).  Among other things, this will allow you to predict the likelihood of over and under-shipment.

5. Illustrate business performance, not just forecasting performance with “sanity” ranges.  The calculation of upper and lower “sanity” intervals should be applied, not only from time-period to time period, but also cumulatively across periods such as months in the fiscal year.

If you are engaged in demand planning or sales and operations planning, I’d like to know your thoughts on performing a Demand Plan Sanity Check.

Thanks again for stopping by Supply Chain Action.  As we leave the work week and recharge for the next, I leave you with the words of John Ruskin, “When skill and love work together, expect a masterpiece.”

Have a wonderful weekend!

The Time-to-Action Dilemma



dreamstime_m_26639042If you can’t answer these 3 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) What proportion (and how many) of your customer orders (or margin or revenue) shipped at 99% on-time and in-full?  How many at 98%? And so on . . . Do you understand the drivers?

The slack time that global competition is allowing you to have between planning and execution is collapsing at an accelerating rate.

You need to know the “What?” and the “Why? so you can determine what to do before it’s too late.  

You need to answer the questions that your ERP and APS can’t so your supply chain makes your business more valuable.

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 have a platform that consolidates and blends 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 the kind of platform that will deliver 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.

If you don’t have all three now – Detect, Diagnose and Direct – in a way that covers your end-to-end value network, you need to explore how you can get there.

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 compels maximum contribution, whether that is leading, following, or just getting out of the way.”

“Moneyball” and Your Business

MV5BMjAxOTU3Mzc1M15BMl5BanBnXkFtZTcwMzk1ODUzNg@@__V1__SY317_CR0,0,214,317_It’s baseball season again!  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.

That’s what competing on analytics really means.

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 analytics software.  Yet, many companies who have made huge investments know only a fraction of what they should know from their ERP and other systems while they are mired in long, costly projects that are rapidly losing momentum and delivering little or no value.

Take operational excellence as an example.

Are you able to see a bottleneck build in your order-to-cash process at exactly the step or steps where it is occurring, immediately comprehending the impact because you are seeing hard data in an intelligent context?

What about visibility of supply chain performance?

Can you see that what proportion of your perfect order performance is being caused by days of supply which has been recently impacted by changes in customer order request dates or forecast error?

If operational excellence or supply chain visibility and performance sit high on your list of priorities, your wish list should include the following:

  • Pre-built connectors to your ERP system from a secure, scalable, speedy cloud platform for immediate plug-in and start-up
  • Fast harmonization across multiple ERP instances or data models
  • Comprehensive, domain-specific (supply chain and maybe industry) interrelated metrics that focus new light on the levers for revenue, margin and working capital
  • Simple, but powerful, self-service configuration beyond out-of-the-box metrics
  • Root cause analysis
  • Role-based views with collaboration
  • (Almost) zero learning curve
  • A continuous stream of new value-added services (e.g. what-if scenario analysis, predictive and prescriptive analytics, etc.) based the fact that your provider is now the secure custodian of your enterprise data

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”?

As always, thanks for stopping by and having 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, from Socrates:  “The wisest man is he who knows his own ignorance.

Do you know yours?  Do I know mine?

Have a wonderful weekend!

Whither Supply Chain Analytics?

IBM has just 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 are rapidly moving with value network analytics.  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, decision-making that is “in the moment” that 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, half of this information 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? 

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!

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