Ava Ex Machina and the Retail Supply Chain

There is a lot of buzz about the “autonomous” supply chain these days.  The subject came up at a conference I attended where the theme 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 “Ava Ex Machina”, let’s take a moment and put this idea into some perspective.

 

The Driverless Car Analogy

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 freely, fast, and securely between facilities, organizations, and even consumers, following the path of least resistance (aka the digital supply chain), may prove to be even more complex than driving a vehicle.  Digital technologies, such as additive manufacturing, blockchain, and more secure IoT infrastructure, advance the freedom, speed and security of these flows.  As these technologies makes more automation possible, as well as a kind of “autonomy”, the difficulty and importance of guiding these flows becomes ever more crucial.  

 

Most sixteen-year-old adolescents 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 the Detect, Diagnose, Direct Cycle – let’s call it the 3-D Cycle, for short, not just because it’s alliterated, but because each “D” is one of three key dimensions of orchestrating your value network.  In fact, as you accelerate the 3-D Cycle, you will learn just how much automation and autonomy makes sense.

 

Figure 1

Detect, Diagnose, Direct

The work of managing the value network has always been to make the best plan, monitor issues, and respond effectively and efficiently.  However, since reality begins to diverge almost immediately from even the best plans, perhaps the most vital challenges in orchestrating a value network are monitoring and responding.

 

In fact, every plan is really just a response to the latest challenges and their causes.

 

So, if we focus on monitoring and responding, we are covering all the bases of what planners and executives do all day . . . every day.

 

Monitoring involves detecting and diagnosing those issues which require a response.  Responding is really directing the next best action.  That’s why we can think in terms of the “Detect, Diagnose, Direct Cycle”:

 

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

 

The 3-D Cycle used to take a month, in cases where it was even possible.  Digitization – increased computing power, more analytical software, the availability of data – have made it possible in 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.   

 

You will need to excel at managing and accelerating the 3-D Cycle, if you want to win in digital economy.

 

There is no industry where mastering this Cycle is more challenging than in retail, but the principles apply across most industries.

Data Is the Double-edged Sword

The universe of data is exploding exponentially from growing connections among organizations, people and things, creating the need for an ever-accelerating 3-D Cycle.  This is especially relevant for retailers, and it presents both a challenge and an opportunity for competing in the digital economy with a digital value network.  Redesigned, retail supply chains, enabled with analytics and augmented reality, are not only meeting, but raising consumer expectations.

 

Figure 2

Amazon’s re-imagination of retail means that competitors must now think in terms of many-to-many flows of information, product, and cash along the path of least resistance for the consumer (and not just to and from their own locations).  This kind of value network strategy goes beyond determining where to put a warehouse and to which stores it should ship.  Competing in today’s multi-channel world can mean inventing new ways to do business, even in the challenging fashion space – and if it is happening in fashion, it would be naive to think rising consumer expectations can be ignored in other retail segments, or even other industries.  Consider a few retail examples:

Zara leverages advanced analytics, not only to sense trends, but also to optimize pricing and operations in their vertically integrated supply chain.

Stitch Fix is changing the shopping model completely, providing more service with less infrastructure.

Zolando has been so successful in creating a rapid response supply chain that they are now providing services to other retailers.

Nordstrom, of all organizations, is opening “inventoryless” stores.

Walmart has been on an incredible acquisition and partnership spree, recently buying Flipkart and, as early as two years ago, partnering with JD.com.  And, then, there is the success of Walmart.com.

Target is redesigning the way their DC’s work, creating a flow-through operation with smaller replenishment quantities.

 

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 3-D Cycle to keep pace grows more ever more intense, 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 faster through advanced analytics (see Figure 2).

 

The time required just to collect, clean, transform and synchronize data for analysis remains the fundamental barrier to better detection, diagnosis and decisions in the value network.  A consolidated data store that can connect to source systems and on which data can be consolidated, programmatically “wrangled”, and updated into a supra data set forms a solid foundation on which to build better detection, diagnosis, and decision logic that can execute in “relevant time”.  This can seem like an almost insurmountable challenge, but it is not only doable with today’s technology, it’s becoming imperative.  And, it’s now possible to work off of a virtual supra data set, but that’s a discussion for another day.

Detect, Diagnose and Direct with Speed, Precision & Advanced Analytics

Detection of incidental challenges (e.g. demand is surging or falling based on local demographics, a shipment is about to arrive late, a production shortfall at a vendor, etc.) in your value network can be significantly automated to take place in almost real-time, or at least, in relevant time.   Detection of systemic challenges will be a bit more gradual and is based on the metrics that matter to your business, such as customer service, days of supply, etc., but it is the speed and, therefore, the scope, that is now possible that drives better visibility 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 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 understanding possible through diagnosis.

 

With a clean, complete, synchronized data set that is always available and always current, as well as a proactive view of what is happening and why, you need to direct the next best action while it still matters.  You need to optimize your trade-offs and perform scenario and sensitivity analysis.

 

Figure 3, below, shows both incidental/operational and systemic/strategic examples for all three dimensions of the 3-D Cycle.

Figure 3

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.  Much of the entire 3-D 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.

 

Fortunately, you don’t need Ava Ex Machina, but your ability to develop a faster and better 3-D Cycle is fundamental to your journey toward the digital transformation of your value network.

 

The basic ideas of detecting, diagnosing and directing are not novel to supply chain professionals and other business executives.   However, the level of transparency, speed, precision and advanced analytics that are now available mandate a new approach and promise dramatic results.  Some will gradually evolve toward a better, faster 3-D cycle.  The greatest rewards will accrue to enterprises that climb each hill with a vision of the pinnacle, adjusting as they learn.  These organizations will attract more revenue and investment.  Companies that don’t capitalize on the possibilities will be relegated to hoping for acquisition by those that do.

 

Admittedly, I’m pretty bad at communicating graphically, but I’ve attempted to draft a rudimentary visual of what the architecture to support a state-of-the-art 3-D Cycle could look like (below), as a vehicle for facilitating discussion.

 

 

The convergence of cloud business intelligence (BI) technology and traditional advanced planning solutions supports my point, and that is definitely happening.  Cloud BI solutions (e.g. Aera, Birst, Board) incorporate at least some machine learning (ML) algorithms for prediction, while Oracle, Microsoft, IBM, and SAP are all making ML available in their portfolios, adjacent to their BI applications.  Logility recently purchased Halo which embeds ML.  Many advanced planning vendors are pitching “control towers” which are really an attempt to move toward combining BI capabilities and planning.  Perhaps most importantly, Oracle has built their cloud supply chain management solutions with embedded BI, really making an effort toward a faster, better 3-D Cycle.

 

For maximum success, you may need a partner who can do four things for you:

 

  1. Bring an in-depth understanding of the 3-D Cycle
  2. Listen and learn your business strategy and operations
  3. Offer thought leadership on the application of the 3-D Cycle in your business
  4. Craft, configure and deliver a solution that provides competitive advantage

 

As the unstoppable train of time pulls us into the weekend, I leave you with this thought to ponder: 

“Life is short, so live it well, in gratitude, honesty and hope, and never take it for granted.”

 

Have a wonderful weekend!

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The Potential for Proven Analytics and Planning Tools in Healthcare Delivery

I’ve spent time in a hospital.  I was well cared for, but I didn’t like it, and I worried about the cost and how well I would be able to recover (pretty well, so far!)  Also, my daughter is a doctor (obviously takes after her mom!), so healthcare is obviously an area of high interest for me.

To say that managing a large, disaggregated system such as healthcare delivery with its multitude of individual parts, including patients, physicians, clinics, hospitals, pharmacies, rehabilitation services, home nurses, and more is a daunting task would be an understatement.

Like other service or manufacturing systems, different stakeholders have different goals, making the task even more challenging.

Patients want safe, effective care with low insurance premiums. 

Payers, usually not the patient, want low cost. 

Health care providers want improved outcomes, but also efficiency.

The Institute of Medicine has identified six quality aims for twenty-first century healthcare:  safety, effectiveness, timeliness, patient-centeredness, efficiency, and equity.  Achieving these goals in a complex system will require an holistic understanding of the needs and goals of all stakeholders and simultaneously optimizing the tradeoffs among them.

This, in turn, cannot be achieved without leveraging the tools that have been developed in other industries.  These have been well-known and are summarized in the table below.

While the bulk of the work and benefits related to these tools will lie at the organization level, such techniques can be applied directly to healthcare systems, beginning at the environmental level and working back left down to the patient, as indicated by the check marks in the table.

A few examples of specific challenges that can be addressed through systems analysis and planning solutions include the following:

1 – Optimal allocation of funding

2 – Improving patient flow through rooms and other resources

3 – Capacity management and planning

4 – Staff scheduling

5 – Forecasting, distributing and balancing inventories, both medical/surgical and pharmaceuticals

6 – Evaluation of blood supply networks

Expanding on example #5 (above), supply chain management solutions help forecast demand for services and supplies and plan to meet the demand with people, equipment and inventory.  Longer term mismatches can be minimized through sales and operations planning, while short-term challenges are addressed with inventory rebalancing, and scheduling.

Systems analysis techniques have been developed over many years and are based on a large body of knowledge.  These types of analytical approaches, while very powerful, require appropriate tools and expertise to apply them efficiently and effectively.  Many healthcare delivery organizations have invested in staff who have experience with some of these tools, including lean thinking in process design and six-sigma in supply chain management.  There are also instances where some of the techniques under “Optimizing Results” are being applied, as well as predictive modeling and artificial intelligence.  But, more remains to be done, even in the crucial, but less hyped, areas like inventory management.  Some healthcare providers may initially need to depend on resources external to their own organizations as they build their internal capabilities.

I leave you with a thought for the weekend – “Life is full of tradeoffs.  Choose wisely!”

Multi-echelon Inventory Optimization and Lean/Six Sigma

The Emerging Role of Optimization in Business Decisions

For many, there was a point in the past when the idea of “optimization” used to summon images of Greek letters juxtaposed in odd arrangements kept in black boxes that spewed out inscrutable results.  Optimization was sometimes considered 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 the role of optimization.  Those leaders who leverage it intelligently, are not just valuable assets, but absolutely essential to achieving and sustaining a more valuable enterprise.  Global competition mandates that executives never “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 have enabled the use of optimization in an ever-widening array of business decisions.

 

How Lean Thinking Helps

Lean principles are applied to drive out waste.  One of the most predominant lean tools used for identifying waste is Value Stream Mapping which helps identify eight wastes, including overproduction, waiting, over-processing, unnecessary inventory, handling and transportation, defects, and underutilized talent.  In inventory management, this often happens through a reduction of lead times and lot sizes.

The reduction of lead times and lot sizes through lean in manufacturing has focused on reducing setup time to eliminate waiting and work-in-process inventory, as well as the frequent use of physical and visible signals for replenishment of consumption.  One of the challenges is that consumption or “true demand” at the end of the value network is never uniform for each time period, despite efforts to level demand upstream.

Acting and deciding are closely related and need to be carefully coordinated so that the end result does not favor faster execution over optimizing complex, interdependent tradeoffs.

 

The Importance of Six Sigma

Six sigma pursues reduced variability in processes.  In manufacturing, this relates most directly to 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 in manufacturing and inventory management so that fixed constraints like lead time and lot size can be continuously attacked 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 crucial tradeoffs. 

 

Crucial Tradeoffs for Manufacturers

In a manufacturing organization, 60% to 70% of all cash flow is often spent on the cost of goods sold – purchasing raw materials, shipping and storing inventory, transforming materials or components into finished goods, and distributing the final product to customers.  So, deciding just how much to spend on which inventory in what location and when to do it is crucial to success in a competitive global economy.  Uncertain future demand and variations in supply chain processes mandate continuous lean efforts to reduce lead times and lot/batch sizes as well as six sigma efforts to reduce and control variability.

As long as we operate in a dynamic environment, manufacturing executives will continue to face decisions regarding where (across facilities and down the bill of material) to make-to-order vs. make-to-stock and how much buffer inventory to position between operations to adequately compensate for uncertainty while minimizing waste.

Taken in complete isolation, the determination of a buffer for a make-to-stock finished good at the point of fulfillment for independent demand measured by service level (not fill rate) is not trivial, but it is tractable.  But, for almost every manufacturer, the combination of processes that link levels in the BOM and geographically dispersed suppliers, facilities and customers, means that many potential buffer points must be considered.  Suddenly, the decision seems almost impossible, but advances in inventory theory and multi-echelon inventory optimization have been developed and proven effective in addressing these tradeoffs, improving working capital position and growing cash flow.

 

So What?

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.

Multi-echelon inventory optimization perfectly complements lean and six sigma programs to eliminate waste by optimizing the push/pull boundary (between make-to-stock and make-to-order) and inventory buffers as lean/six sigma programs drive down structural supply chain barriers (e.g. lead time and lot/batch size) and reduce variability (in lead times, internal processes and demand).

Given constant uncertainty in end-user demand and the economics of manufacturing in an extremely competitive global economy, business leaders cannot afford not to make the most of all the tools at their disposal, including lean, six sigma, and optimization.

Forecasting vs. Demand Planning

Often, the terms, “forecasting” and “demand planning”, are used interchangeably. 

The fact that one concept is a subset of the other obscures the distinction. 

Forecasting is the process of mathematically predicting a future event.

As a component of demand planning, forecasting is necessary, but not sufficient.

Demand planning is that process by which a business anticipates market requirements.  

This certainly involves both quantitative and qualitative forecasting.  But, demand planning requires holistic process that includes the following steps:

1.      Profiling SKU’s with respect to volume and variability in order to determine the appropriate treatment:

For example, high volume, low variability SKU’s will be easy to mathematically forecast and may be suited for lean replenishment techniques.  Low volume, low variability items maybe best suited for simple re-order point.  High volume, high variability will be difficult to forecast and may require a sophisticated approach to safety stock planning.  Low volume, low variability SKU’s may require a thoughtful postponement approach, resulting in an assemble-to-order process.  This analysis is complemented nicely by a Quantitative Sanity Range Evaluation, which should be an on-going part of your forecasting process.

2.       Validating of qualitative forecasts from among functional groups such as sales, marketing, and finance
3.       Estimation of the magnitude of previously unmet demand
4.       Predicting underlying causal factors where necessary and appropriate through predictive analytics
5.       Development of the quantitative forecast including the determination of the following:

  • Level of aggregation
  • Correct lag
  • Appropriate forecasting model(s)
  • Best settings for forecasting model parameters
  • Deducting relevant consumption of forecast

6.      Rationalization of qualitative and quantitative forecasts and development of a consensus expectation
7.      Planning for the commercialization of new products
8.      Calculating the impact of special promotions
9.      Coordinating of demand shaping requirements with promotional activity
10.    Determination of the range and the confidence level of the expected demand
11.    Collaborating with customers on future requirements
12.    Monitoring the actual sales and adjusting the demand plan for promotions and new product introductions
13.    Identification of sources of forecast inaccuracies (e.g. sales or customer forecast bias, a change in the data that requires a different forecasting model or a different setting on an existing forecast model, a promotion or new product introduction that greatly exceeded or failed to meet expectations).

The proficiency with which an organization can anticipate market requirements has a direct and significant impact on revenue, margin and working capital, and potentially market share.  However, as an organization invests in demand planning, the gains tend to be significant in the beginning of the effort but diminishing returns are reached much more quickly than in many other process improvement efforts.

This irony should not disguise the fact that significant ongoing effort is required simply to maintain a high level of performance in demand planning, once it is achieved.

It may make sense to periodically undertake an exercise to (see #1 above) in order to determine if the results are reasonable, whether or not the inputs are properly being collected and integrated, and the potential for additional added value through improved analysis, additional collaboration, or other means.

I’ll leave you once again with a thought for the weekend – this time from Ralph Waldo Emerson:

“You cannot do a kindness too soon, for you never know how soon it will be too late.”

Thanks for stopping by and have a wonderful weekend!

Do You Need a Network Design CoE?

shutterstock_148723100

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.  

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.

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.

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!
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