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 (and, I will even say more autonomous) 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 have developed “control towers” (e.g. Blue Yonder – formerly JDA) which are really an attempt to move toward combining BI capabilities and planning, as well as leveraging machine learning.  Even Oracle and SAP have built their cloud supply chain planning solutions with embedded BI, really making an effort toward a faster, better 3-D Cycle.

I work for Blue Yonder, so I’m biased, but I think the capabilities Blue Yonder is building out in support of the 3D Cycle are “spot-on”.

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 coverageHow many days of future demand can you satisfy with the inventory you have on-hand right now?
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!

Part 5 – Finding the ROI for an Investment in an Analytical SCM Solution

Technologyevaluation.com published a piece of mine on this topic a few years ago, but the ideas are important, so I am recapitulating the them here.  The first post in this series introduced the topic of overcoming the challenges to calculating the return for an investment in an analytical supply chain software application.  This post deals with the last of four challenges.

Part 5 — Fourth Challenge — No Patience for Context — “Just give me the bottom line!”

This reaction to understanding the value of a software investment is reminiscent of some people who are in a hurry to know what stock they should buy. 

This person does not want to learn about industry performance.

She or he is not interested in the relative competitive strengths of one company versus those of other companies in the same industry.

Nor is this person motivated to research financial statements in order to understand what might be driving a company’s performance or whether that performance is getting better or worse.

This person simply wants to know if it will be a good investment and how much can be made at the end of 12 months if it is sold.

So he or she scans a list of stock picks in one of the many financial publications, chooses the company ranked at the top, and then “places a bet” because at that point, the decision is little more than a wager based on an uninformed hunch.

It is likely that this person will be sadly disappointed in the return.

The investor will probably try to recover the loss with an equally unconsidered investment decision, leading to a cycle of poorly informed decisions.  Obviously, this individual is putting the amount of money about to be invested at great risk because he or she has latched on to an answer without context.

In the same way, decision-makers in a company may rush to a “bottom-line” conclusion, only to have their efforts frustrated by poor results because they did not take the time and do the work necessary to gain some understanding of what is driving their pain and how their decision may affect those drivers.

This will often lead to additional decisions that are made without adequate research and consideration in an effort to recover from the first one.

Successful business is driven by quality decisions that can be executed in a timely fashion.

Some of these decisions relate to investing in software applications that support supply chain management.  At least four hurdles face those seeking to make a timely, but intelligent decision, as we have seen in this series of posts.

Reliable predictions of ROI will continue to evade decision-makers who react to these challenges with the responses we have studied here.  However, if you do not succumb to that temptation, some careful analysis can provide information that will guide your software investment strategy.

Companies invest in analytical supply chain management software to improve their decision processes.  You can read about my unique approach to prioritizing and accelerating the improvement of decision processes in “Finding the Value in Your Value Network“.

I leave you to ponder this final thought from Thomas Carlyle, “Work is the grand cure of all the maladies and miseries that ever beset mankind–honest work, which you intend getting done.”

Part 4 – Finding the ROI for an Investment in an Analytical SCM Solution

Technologyevaluation.com published a piece of mine on this topic a few years ago, but the ideas are important, so I am recapitulating the them here.  The first post in this series introduced the topic of overcoming the challenges to calculating the return for an investment in an analytical supply chain software application.  This post deals with the third of four challenges.

Part 4 — Third Challenge — Making Sense of the Data — “We have tons of data, but it is not telling us what we need to know!”

Analyze the Data

If the data exist, you need to trace a symptom, like excess work-in-process (WIP) inventory, to the root causes such as forecast error that drove production of the wrong product.  Once that is done, then powerful, but a relatively simple analysis can be performed by collecting the data from the data warehouse, or wherever it is stored, by putting it into a spreadsheet and then creating a cumulative distribution of the symptoms by reason code (see Figure 1).

Figure 1 – Pareto Chart (Cumulative Distribution)

More commonly, however, the data cannot be readily segmented by root cause.  This is probably because the symptoms and the root causes have not been identified and linked.  Using a simple fishbone diagram (see Figure 2), a few folks who know the business processes involved can probably identify symptoms and trace them to possible root causes.  Naturally, a skilled facilitator (possibly a consultant) will help, but you can also learn by reading up on the idea and by doing it yourself.


Figure 2 – Cause and Effect (Ishikawa or fishbone) diagram with potential root causes marked with capital letter reason codes.

Once the potential root causes have been identified, then a system of recording the incidents by reason code has to be put in place.  In some cases, while occurrences will not be tied to a reason code or other explanatory data, there will be some data that can be used as an approximate surrogate to estimate the order of magnitude of the root cause.  In those cases, you can get to an answer sooner, albeit a less precise one.

As an example, forecasting may be coming from sales.  You can probably measure the accuracy pretty well by saving the forecast and then by comparing it with orders or shipments in the same period as the forecast (made at lead time).  What is harder to determine is how much better your purchasing, manufacturing and distribution would have been if forecasts were 50% more accurate, or what the bottom line benefits would have been.  But by making some observations like how often a job had to be interrupted to start another one based on a canceled order or a forecast that was wrong, you can begin to build a collection of data that will be the foundation for answering that question.  Then, by creating a cumulative distribution that shows the schedule changes by reason code, you will get an understanding of the size of this problem.  Both inventory turns and customer service will go up if you can create a plan that is more flexible, responsive and accurate by attacking the root cause.

Estimate the Benefits

You can make an assumption on how much improvement might be possible.  Then, hypothetically, reduce the schedule changes due to forecast errors by that amount.  Research average WIP and reduce that by the same factor.  Put a procedure in place to track premium shipments that are paid by your company by reason code.  Take the premium freight that is caused by bad forecasts to the bottom line.

Then, since you made an assumption that forecasts could be 50% more accurate, you will need to perform some sensitivity analysis. Vary the 50% and see what the results tell you.  The ratio of the change in the root cause to the effect on the metric you are trying to improve measures your sensitivity to that root cause.  This kind of simulation model can be created with a spreadsheet tool.

Summary

Borrowing the Pareto and Ishikawa tools from TQM practices can help you find data and create information that you did not know was there, but the speed with which this kind of analysis can be performed increases with the availability and accuracy of data.

Thanks for stopping by Supply Chain Action.  Next, I’ll share Part 5 of this article.  Until then, I leave you to also ponder these words from Leo Tolstoy, “There is no greatness where there is no simplicity, goodness and truth.”

 

Part 3 – Finding the ROI for an Investment in an Analytical SCM Solution

Technologyevaluation.com published a piece of mine on this topic a few years ago, but the ideas are important, so I am recapitulating the them here.  The first post in this series introduced the topic of overcoming the challenges to calculating the return for an investment in an analytical supply chain software application.  This post deals with the second of four challenges.

Part 3 – Second Challenge – Business analysis skills are lacking – “We are looking for the vendor to tell us!”

Can the Vendor Help?

After all, since the software vendor is proposing the solution, shouldn’t the vendor know how it will affect your company?  The vendor probably does have some useful information about whether the decision to purchase will be of some benefit.  They will be able to tell you in general what business symptoms can be affected.  They may even have survey data that show how other companies in your industry, or at least in other industries, have reported benefits.  They should have anecdotal evidence of how some existing customer plans to benefit or has benefited from investing in their approach or solution.

There are a couple of problems with the vendor’s input. First, the vendor cannot be objective. The vendor’s business is on the line.  It is probably a fierce competitor and its representatives may be under pressure to make this deal happen.  Second, directional information, surveys, and anecdotes may or may not be reasonable predictors of how your company will fare.

The current state of your business processes and how they are performing is pivotal to the potential return.

What Should You Do?

This reaction “We are looking for the vendor to tell us!”, is similar to the first challenge and reaction,“We need to know now!”  This second challenge is less driven by time than by the perception that the skill to perform the cause and effect analysis, data gathering, and statistical analysis does not reside within your company.  But, it is important for you to be able to understand, monitor and control the process, even if you use an outside consultant. Following these steps will help you do just that:

1. Identify and quantify undesirable symptoms.

2. Perform cause and effect analysis to find possible root causes.

3. Gather data by reason code (in order to prioritize root causes).

4. Quantify and analyze root causes (Pareto analysis).

5. Estimate the positive impact of your investment decision (e.g. a new supply chain management tool) on your root causes.

6. Extrapolate this to the positive impact on the undesirable symptoms.

7. Perform sensitivity analysis around your estimate in step 5 by varying the estimate and repeating step 6. This will give you a sense for the range of possible outcomes that is reasonable.

Your success at steps 1 through 7 will be most likely if you follow two additional guidelines:

1. “Time box” the analysis to a minimum of 2 weeks and a maximum of 30 days. These time frames are really only a guide to represent the order of magnitude for the minimum and maximum time frames.

2. Assign a primary internal resource for each area of analysis you undertake.

Once again, I’m grateful that you took a moment to read Supply Chain Action.  As a final thought, I remind you of the familiar words to ponder, “Luck is that point in life where opportunity and preparation meet.”

 

Part 2 – Finding the ROI for an Investment in an Analytical SCM Solution

Technologyevaluation.com published a piece of mine on this topic a few years ago, but the ideas are important, so I am recapitulating the them here.  The first post in this series introduced the topic of overcoming the challenges to calculating the return for an investment in an analytical supply chain software application.  This post deals with the first of four challenges.

Part 2 – First Challenge – Limited time to perform analysis – “We need to know now!”

It is usually true that, at some point, the incremental benefit of additional information decreases as one moves along the continuum from limited information toward the goal of perfect information about the future.  However, you will reap significant benefit from knowing with some certainty what you can know in a 2-4 week period.  So, the output of some rigorous analysis should not be under valued.

If you truly do need to know something with immediacy, here are some tips for a quick, cursory approach to identifying the potential return from an investment in several aspects of analytical tools for supply chain or value network, management.

Collaborative Forecasting and Planning

If you track the accuracy of your forecasts, then you have some idea whether or not your company anticipates marketplace requirements well.  However, you must look beyond the aggregate annual revenue projection.  To understand the impact of demand planning on operations, it must be examined at a level that can be executed.  In other words, are you accurately anticipating the requirements for parts, people and processing at a fairly detailed level?  If significant forecast misses regularly occur, then working together with your major customers to plan for demand may have a notable impact on your operating costs.

Your suppliers may levy additional charges because you have passed on abrupt corrections in your demand for their products and services as a response to changes in your own demand profile.  These additional charges derive from additional setups, work-in-process inventory, and lost material incurred by the vendor on your behalf.  If the structure of your industry prohibits suppliers with less bargaining power from passing on these charges, they are still no less real a cost.  Everywhere that the value network generates unnecessary costs, a savings opportunity exists for the members of the value network.  In this case, such savings might be with reach if you extend the collaborative planning loop to include not only your customers, but also your suppliers.

Optimized Manufacturing Planning

Optimized manufacturing planning entails the use of math and analytical tools to choose the least cost combination of plant, equipment, personnel, and material that will meet planning objectives that may include one or more of the following examples:

• Maximizing inventory turns

• Maximizing on-time delivery

• Maximizing revenue

• Maximizing profit

• Maximizing throughput

Because optimized manufacturing planning provides decision support that considers multiple tradeoffs and constraints, it may not be easy to point to one indicator that demonstrates the potential for improvement through implementation of this powerful technique.  However, clues can be found in your manufacturing cost variances and in your performance against the business metrics that correspond with the objectives you want to maximize.

Inventory Planning and Optimization

Look at your financial reports and make a judgment as to whether your balance sheet or your income statement will be positively affected by the decision.  For example, examine inventory levels relative to your revenue.  Calculate inventory turns by dividing revenue by the annual ending (or better yet, trailing 12-month average) inventory.  Compare your turns with your competitors.  If that information is not available, you can use a general industry measure that is more readily available.  The higher the turns, the more working capital you can invest elsewhere and/or the less total interest you pay the bank for the working capital that you borrow.

Gauge your company’s interest expense.  If turns are low and interest expense seems high, then you probably have some significant room for improvement in the way that you make decisions about acquiring and producing inventory.

Does your company keep a financial reserve account against inventory assets (a contra account)?  Does the proportion of your inventory that may have to be written down at the end of the year indicate that your company is making enough of the right decisions around purchasing, distribution and manufacturing?  If the reserve account seems high, that underscores the importance of having the right inventory at the right time in the right place.  It means that obsolescence is becoming an issue because your planning process is not keeping pace with the volatility of supply and/or demand.

Synchronized Planning and Scheduling

Does your company pay the freight for the product or do your customers pay it?  Perhaps it varies by customer.  It may be that you are paying a significant amount of premium freight in order to meet customers’ demand.  If the premium freight you have paid each month for the last several months is anything but negligible, there may be an opportunity to eliminate most of that expense through tools that facilitate synchronized planning and fast planning cycles.

Take a walk through the plant.  Do you see a lot of inventory that is partially completed?  Are there piles of work-in-process inventory that are not being rapidly used up, either on the shop floor, or in the warehouse?  That is an indication of a planning problem.  It may be that the distribution, purchasing and scheduling requirements are not synchronized.  Or, perhaps there are bottlenecks that the plant manager cannot deal with systematically because he does not have the right tools.  There could also be significant setup times that can be eliminated with more sophisticated planning algorithms.

Accurate Order Promising

Tally up the amount of charge backs you have received from customers in the last 12 months for late delivery.  If you are consistently getting charge backs for late deliveries or short orders, that is another area of cost savings that should be available to you. Accurate order promising that considers your real capabilities might eliminate a portion of those charges.

The sales force may also have a feel for orders that they lose because they cannot accurately commit to customers in real-time.  An integrated software application that provides that capability might yield a competitive advantage.

Transportation Planning

There may be savings available through transportation planning.  If you have any significant level of less-than-truckload shipments, you may be paying too much for freight.  The challenge of determining the least cost route when many alternative groupings of multiple stops into routes must be considered requires the rapid use of advanced algorithms in order to achieve an optimal, or near optimal result.

If most of your shipments are to consumers, almost every pair of order lines that ship separately to a customer within a given 24-hour period is an opportunity for improvement through co-packing.

Statistical Process Control

Another place to look is in the area of returns.  Unless you are an electronic retailer or a mail order house, returns should not be a significant cost of doing business.  How are they trending?  Talk to manufacturing, distribution, customer service, or all three and you will get an understanding of how often things come back and why. You may find an indication of a quality problem in manufacturing, packaging or distribution processes (shipping and handling, or possibly sorting).

That’s Part 2 — the longest part, so I promise the others will be shorter.

Thanks again for stopping by. I’m not sure who said this, but I will leave you to ponder it this week:  “Don’t be troubled if the temptation to give advice is irresistible; the ability to ignore it is universal.”

 

Part 1 – Finding the ROI for an Investment in an Analytical SCM Solution

Technologyevaluation.com published a piece of mine on this topic a few years ago, but the ideas are important, so I am recapitulating the them here.

Part 1 — Introduction

The competitive environment for every industry grows increasingly intense. Fast, reasonably accurate information about the impact of a software investment decision grows more critical.  Many decision-makers look for an exact forecast of return on investment (ROI) from the purchase of a supply chain management application.  At least four very real challenges make such perfect information elusive.  Too commonly, executives meet these challenges with responses that are not carefully considered. You have heard these challenges and their reactionary refrains before:

1. Limited time exists to perform analysis – “We need to know now!”

2. Business analysis skills are lacking – “We are looking for the vendor to tell us!”

3. The data to perform the analysis are almost always not available in the corporate databases – “We have tons of data, but it is not telling us what we need to know!”

4. It is always difficult to predict the future . . . as in forecasting, certain laws about a prediction of ROI will forever hold true . . .

  • the prediction will always be wrong
  • the prediction will always change for as long as the analysis continues
  • someone is going to be held accountable for the wrong, changing prediction

“Just give us the bottom line!”

After a quick look at these issues, one might question the effort to undertake the analysis to predict an ROI, as well as the validity of the outcome.  Perfect, or even complete, information may not be feasible, but if a few basic principles are followed, some analytical work can provide an understanding of the potential for bottom line impact.  It can also yield insight into the root causes of undesirable symptoms from which your business may be suffering.

The reactions of some decision-makers to each of the four challenges that are listed above provide a convenient outline for exploring a more thoughtful and strategic approach to evaluating a potential investment in supply chain management software.  I’ll explore each of these in successive, upcoming posts as follows:

Part 1 – “We need to know now!”

Part 2 – “We are looking for the vendor to tell us!”

Part 3 — “We have tons of data, but we it is not telling us what we need to know!”

Part 4 – “Just give us the bottom line!”

Part 5 — Where to start

Thanks for stopping by Supply Chain Action.  I leave you with a few words from Benjamin Franklin, “Be at war with your vices, at peace with your neighbors, and let every new year find you a better man [person].”

 

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