Part 3 – A New Supply Chain Planning Paradigm for the Digital Value Network?

In Part 1, I introduced the 3-D Cycle that integrates the 3 dimensions of orchestrating a value network.  In Part 2, I defined the 3-D Cycle in more detail.  In this post, Part 3, I’ll explore the some of the challenges and industry imperatives, drawing on some examples from retail (thought I’d pick one of the more challenging industries).  Next and finally, in Part 4, I’ll explore what the 3-D Cycle looks like in terms of specific examples and architecture.

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 with your digital value network in the global digital economy.

 

For example, redesigned, retail supply chains, enabled with analytics and augmented reality (AR), are not only meeting, but raising consumer expectations.

 

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 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.

 

The time required just to collect, clean, transform and synchronize data for analysis remains 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.

 

Thanks for stopping by.  I’ll leave you with this quote from the book, Hit Refresh (a read I thoroughly enjoyed), by Satya Nadella, CEO of Microsoft:

 

“Success can cause people to unlearn the habits that made them successful in the first place.”

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A Digital Value Network Needs an Accelerated “3-D” Cycle

Photo licensed through iStockphoto

The strength of any chain is defined by its weakest link.  A supply chain, or as I prefer to say, a value network, is similarly constrained.  By orchestrating the flow of material, information and cash through your value network, you can prevent negative business impact from weak links by detecting anomalies, diagnosing their causes, and directing the next best action before there is a serious business impact.  Do you need some kind of self-aware artificial intelligence to make this work?  Let’s think about that for a minute.

 

 

 

Photo licensed through Shutterstock

 

 

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 “Skynet”, 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 – has 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 robotic process automation (RPA).  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 with your digital value network in the global digital economy.

 

For example, redesigned, retail supply chains, enabled with analytics and augmented reality (AR), 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 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 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 vendor is behind on production, 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 through 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 “Skynet”, but 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 couple rudimentary visuals of what the architecture to support a state-of-the-art 3-D Cycle could look like (Figures 4 and 5, below), as a vehicle for facilitating discussion.  I do realize that the divisions I’m showing between Cloud, IoT, Extended Apps, and ERP are somewhat arbitrary and definitely fluid.

 

Figure 4

Figure 5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

So, I imagine that I’m at least partly wrong, and could be completely wrong-headed . . . but, then again, maybe not.  I will say this:  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.  Many advanced planning vendors are pitching “control towers” which are really an attempt to move toward combining BI capabilities and planning.  Logility recently purchased Halo which embeds ML.  Perhaps most importantly, Oracle has built their cloud supply chain planning solutions with embedded BI, really making an effort toward a faster, better 3-D Cycle.

 

So, the future would appear to be now.  If that’s true, you have to ask yourself whether your current paradigm for value network planning will guide you to competitive advantage or leave you hoping that someone else will ask you to the dance.

 

I’ll leave you with this thought for the weekend:  I know more now than I once did, especially about how much I still don’t know that I don’t know.

 

Have a wonderful weekend!

 

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!

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!

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!

Analytics vs. Humalytics

I have a background in operations research and analysis so, as you might expect, I am biased toward optimization and other types of analytical models for supply chain planning and operational decision-making.   Of course, you know the obvious and running challenges that users of these models face:

  1. The data inputs for such a model are never free of defects
  2. The data model that serves as the basis for a decision model is always deficient as a representation of reality
  3. As soon a model is run, the constantly evolving reality increasingly deviates from the basis of the model

Still, models and tools that help decision-makers integrate many complex, interrelated trade-offs can enable significantly better decisions.

But, what if we could outperform very large complex periodic decision models through a sort of “existential optimization” or as a former colleague of mine put it, “humalytics“?

Here is the question expressed more fully:

If decision-makers within procurement, manufacturing and distribution and sales had the “right time” information about tradeoffs and how their individual contributions were affecting their performance and that of the enterprise, could they collectively outperform a comprehensive optimization/decision model that is run periodically (e.g. monthly/quarterly) in the same way that market-based economies easily outperform centrally planned economies?

I would call this approach “humalytics” (borrowed from a former colleague, Russell Halper, but please don’t blame him for the content of this post!), leveraging a network of the most powerful analytical engines – the human brain, empowered with quantified analytical inputs that are updated in “real-time” or as close to that as required.  In this way, the manager can combine these analytics with factors that might not be included in a decision model from their experience and knowledge of the business to constantly make the best decisions with regard to replenishment and fulfillment through “humalytics”, resulting in constantly increasing value of the organization.

In other words, decision-maker would have instant, always-on access to both performance metrics and the tradeoffs that affect them.  For example, a customer service manager might see a useful visualization of actual total cost of fulfillment (cost of inventory and cost of disservice) and the key drivers such as actual fill rates and inventory turns as they are happening, summarized in the most meaningful way, so that the responsible human can make the most informed “humalytical” decisions.

Up until now, the answer has been negative for at least two reasons:

A. Established corporate norms and culture in which middle management (and maybe sometimes even senior management) strive diligently for the status quo.

B. Lack of timely and complete information and analytics that would enable decision-makers to act as responsible, accountable agents within an organization, the same way that entrepreneurs act within a market economy.

With your indulgence, I’m going to deal with these in reverse order.

A few software companies have been hacking away at obstacle B.”, and we may be approaching a tipping point where the challenge of accurate, transparent information and relevant, timely analytics can be delivered in near real-time, even on mobile devices, allowing the human decision-makers to constantly adjust their actions to deliver continuously improved performance.  This is what I am calling “humalytics”.

But the network of human decision-makers with descriptive metrics is not enough.  Critical insights into tradeoffs and metrics come through analytical models, leveraging capabilities like machine learning, optimization, RPA, maybe in the form of “mini-apps” models that operate on a curated supra set of data that is always on and always current.  So, at least two things are necessary:

1. Faster optimization and other analytical modeling techniques from which the essential information is delivered in “right time” to each decision-maker

2. An empowered network of (human) decision-makers who understand the quantitative analytics that are delivered to them and who have a solid understanding of the business and their part in it

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. (ftp://ftp.deas.harvard.edu/techreports/tr-06-11.pdf)  Maybe, we don’t need swarm of robots, after all.  Maybe we just need empowered decision-makers who not only engage in Sales and Operations Planning (or, if you will, Integrated Business Planning), but integrated business thinking and acting on an hourly (or right time) basis.

What think you?

If you think this might make sense for your business, or if you are working on implementing this approach, I’d be very interested to learn your perspective and how you are moving forward.

I leave you with these words from Leo Tolstoy, “There is no greatness where there is no simplicity, goodness, and truth.”

Have a wonderful weekend!

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