Archive for the ‘Milesahead’ Category

How uncertainty changes our perspective of the purpose of planning has been the central theme of many of my blogs over the past 3.5 years while at Kinaxis. There are changes that are taking place in the supply chain space driven by the understanding that plans are a guess of what will happen and the likelihood that the guesses/assumptions were correct is fairly low.

I just the past four weeks I have talked to a number of leading electronics companies that are grappling with the idea that planning should be performed in parallel across functions, rather than sequentially.  If we are to collapse the planning/decision process lead times in our supply chains, we will have to adopt simultaneous or parallel processes, which in turn will lead to different organizational structures and systems that focus on collaborative decision making that focuses on human intelligence and machine speed.

These are all themes that are captured in the new eBook, Miles Ahead: The “miles ahead” vision of integrated supply chain planning and response management which is compilation of a number of my key blogs. The core concept is that planning is not enough, or, stated differently, planning is only the beginning. Making a promise to a customer is difficult enough. Keeping that promise is often immeasurably more difficult, especially in environments that have order-to-delivery lead times longer than a day or two. In the eBook I challenge several standard assumptions and approaches to planning and make some suggestions on how planning can be approached differently.  I’d really like to get your feedback on the ideas and concepts discussed in the eBook.

Download your copy here: http://www.kinaxis.com/go/miles-ahead/

This is a quote attributed to Ernestine Ulmer, an American writer born in 1925, that my middle child, a son, has lived by since his birth, though I suspect his sweet tooth has little to do with a philosophy of life, but is rather a life choice.

What I really like about the quote though is the combination of a realization about life, followed by a radical shift in behavior.

As importantly, from an operations perspective, is that the quote the neatly captures two really important concepts in planning, namely:

• How certain are we that the plan represents reality accurately?
• How will we behave when we know the plan is never accurate?

Life is Uncertain

Years ago, I had an on-going discussion with a colleague about the importance of uncertainty in any model.  The topic was about how likely it was to achieve a plan generated by a model. Think of likelihood as a measure of the accuracy of a plan. It turns out that the likelihood is scarily small, and gets smaller as the time horizon expands. Correctly, my colleague said that people wanted certainty; they didn’t want to know that it is unlikely that they would ever achieve the plan they put in place.  He is correct in that people don’t want to know this, but incorrect that they shouldn’t know this.  They should know and develop different competencies and skills.

We all need to plan. That the plan is not 100 percent accurate is not the problem. The fundamental problem is that we behave as if the plan is 100 percent correct.  This is manifested in the fact that we even develop metrics such a ‘Plan Conformance’ or ‘Plan Adherence’, and measure our factories and supply chains as if these are attainable. How silly is that?! Einstein’s definition of insanity is Doing the same thing over and over again and expecting different results. If we know that the plan is never 100 percent correct, why behave as if it is?  Why not also develop the competencies and skills to respond profitably and quickly to real conditions, which often don’t match what we anticipated through our planning process.

I believe that ‘uncertainty’ is a more fundamental concept than ‘volatility’, in Operations in general, and in supply chain specifically. Clearly Ernestine Ulmer would argue that uncertainty is a fact of life. Industry/market trends such as demand volatility, globalization, escalating customer expectations, shortening product lifecycles etc… only serve to amplify the underlying uncertainty. But uncertainty was always there whether for demand, capacity, quality, supply, lead time, or any number of other factors that go into the models used to create plans for our companies. The most succinct way to capture the result of this uncertainty on these models is GEP Box’s quote that

“Essentially, all models are wrong, but some are useful.”

Uncertainty and the consequent model inaccuracy are themes I have been writing about for some time, including my latest blog and in terms such as “embrace volatility”, “embrace complexity”.  Complexity and volatility only exacerbate the fact that the model is inaccurate and therefore the plan is inaccurate.

Eat Dessert First

If I were to define a tag line for Kinaxis, it would be “Plan, Monitor, Respond.”  The importance of this line is the implied equal importance of these three competencies in tandem.  This is a very important shift to recognize the importance and value of being able to detect quickly when and where the plan does not intersect with real life, and then be able to respond quickly and profitably. It is not enough to develop world-class planning skills in isolation. We must develop the competencies and skills to reduce the time to detect that our plans will not be realized, and the competencies and skills to reduce the time to correct by demand or supply shaping, or by modifying our plan.

This is a big change in approach because in life it seems unnatural to anticipate the worst and to change our behavior accordingly.  When we teach our kids to ride bicycles we teach them how to stay on the bike, not how to anticipate when they are going to fall off and how to roll when they do, so that they hurt themselves less.  And yet, we buy insurance to cover medical bills in case they fall and break an arm.  This behavior is predicated on the assumption that all kids can learn to ride bikes well enough so that in nearly 100 percent of the time they will be able to ride their bikes without incident, and insurance is there to cover the 0.1 percent of the time when they fall off. In other words our behavior is predicated on the assumption that our kids will only injure themselves very occasionally.

How would we behave if our kids fell off their bikes 30 percent of the time they went riding? What if they fell 50 percent of the time? One natural response is to teach them to ride better and therefore fall less frequently. How would we respond if after 10 years of trying, they were still falling off 30 percent of the time they rode their bike? (I reference the Terra Technologies study on forecast accuracy in this context.) Would we not at some point want to teach our kids to anticipate when they are going to fall and how to fall to minimize injury?  One logical response is to tell our kids that they cannot ride a bike, but that isn’t possible in the real supply chain world.  So aren’t we duty bound to teach our supply chains not only how to ride better, but also how to anticipate that they are going to fall, how to fall gracefully to reduce the likelihood of major injury, and most importantly, how to quickly get back up and on the path?

While driving around town on Saturday doing some errands I had the radio tuned into a CBC program appropriately called Quirks and Quarks.  Luckily my daughter wasn’t in the car otherwise the radio would have been tuned into one of the pop music channels and I would have missed a really interesting discussion by Lisa Randall, whom the NY Times describe as “… a professor of physics at Harvard and one of the more original theorists at work in the profession today.”

What caught my attention, and made me late for an appointment, was when the discussion turned to what was right or wrong about Newton’s Laws of Motion and Law of Gravity, which are so apparent in our everyday lives. I am an engineer by training and therefore I have a greater interest in the pragmatic than the theoretical, so when Prof Randall challenged the interviewer’s comment that Newton’s Laws have been proven to be wrong I was very interested. Prof Randall’s comment was that it isn’t so much that Newton’s Laws are wrong, but rather that they do not apply in all circumstances. In the scale of everyday life – time, size, distance – they are extremely good at predicting, for example, the flight of a football out of Tom Brady’s arm, to the distance a car will take to stop given its speed and the surface area of its tires on the road.

Prof Randall went on to explain that it is at large distances (universe scale distances), high speeds (approaching the speed of light), and very small distances (micro-atomic sizes) that Newton’s Laws fall apart. The pragmatic engineer in me scoffed “who cares?!”  But she had a really interesting point that at high speeds, the more fundamental theory that emerges is Einstein Law of General Relativity. And if we go to really small distances, the more fundamental theory that emerges is Quantum Mechanics. She went on to say that while we wouldn’t ever calculate the trajectory of a ball using anything other than Newton’s Laws, these other theories are more fundamental. Of these, Prof Randall says that Quantum Mechanics is “…more violating to our intuition…the idea that there are probabilities rather than definite predictive statements.”  Perhaps most intriguing to me is “dark matter”, which Prof Randall says is “…defined by the property that it interacts weakly. It interacts gravitationally, but it doesn’t interact with light…so it can’t be detected.”

So what has this all to do with supply chains?  Well little directly, but there are analogies that bring out concepts that aren’t necessarily new, but are not that well understood.

First and foremost is that for the most part, we treat the supply chain as if it conforms to the APICS definition of Manufacturing Resource Planning (MRP II), which I see as the equivalent of Newton’s Laws, meaning that MRP II certainly has merit but does not necessarily apply or is sufficient for all circumstances, and I contend that the number of circumstances in which MRP II is relevant is diminishing. I include the Oliver Wight standard supply chain planning processes in this perspective. For the most part MRP II can be used to generate a good plan.  But, and it is a big but, the business context in which we operate supply chains has changed sufficiently so that we need to reevaluate some of our approaches to the topic.

So what is different?

• Large Distances/Dark Matter
  Globalization and outsourcing have extended supply chains to the point that many products have circled the world several times by the time we buy them from a retail shelf.  And the full effects and costs of moving materials such large distances are only recently being understood with concepts such as Total Landed Costs (TLC), but as the article referenced points out, TLC is not easy to calculate because of constantly changing fuel prices and labor costs, but more importantly because there are so many factors that go into getting an accurate measure of TLC that the author states “I wondered if it was even possible to accurately calculate a company’s true landed costs.”

  If you can’t measure it, you can’t manage it.

  Well, not to the degree to which we would like to think we can manage it.  And I contend that the degree to which we can measure and manage TLC decreases exponentially with an increase in globalization and outsourcing, which is consistent with Complexity Theory. This is because not only is it difficult to get a good initial/annual measure of TLC, but also that the variables that go into calculating TLC are changing constantly and, in some cases, are barely recognizable from the assumptions made a year ago during a budgetary cycle.

• High Speed/Relativity
  Ok, so I am a late Boomer, and every generation has thought that life progresses more quickly, but few can argue with the fact that especially since 2008 we have seen huge and frequent swings in business cycles compounded by natural disasters.  In fact, we can extend this observation to the mid-1990’s as the internet began to take root.  As an aside, I had a great-grandmother who died at the age of 98 in 1974, so she would have been born in 1876.  In her life time, she experienced the advent of electricity, cars, steam ships, television, telephone, x-ray, penicillin, … Wow.  Back in modern life, we see the ever shrinking product lifecycles coupled with the ever shrinking dominance of companies.  Who would have predicted in 2005 that by 2010 Microsoft would be a “has been” in terms of driving innovation and change?  Oh, I know there are lots of people that will raise their hands now and say “I did. I did.” But these voices were few and far between in 2005.

  Perhaps we haven’t reached the point of warping the time and space continuum, but it does feel to me that we have reached the point that the speed of business has increased to the point that we need to examine the processes and manner by which we operate companies.  I am definitely hearing a lot more about rolling budgets, quarterly budgetary cycles, and even ad hoc or continuous budgetary cycles. I am definitely hearing and reading a lot about the merging of Financial Planning & Analysis (FP&A) with Sales & Operations Planning (S&OP). Perhaps I am hearing about these two processes being executed in lock-step whereas in the past there was minimal interaction.

  By warping, I mean changing processes to accommodate the speed of business. We cannot accommodate new scales of speed using organizational structures and business processes designed in the mid-1900’s.

• Small Distances/Quantum Mechanics
  This is the one that is really near and dear to my heart, because, as Prof Randal says, this is the one that challenges our intuition because of “…the idea that there are probabilities rather than definite predictive statements.” Most of us who work in supply chain management or operations are engineers, if not by training, then at least by nature.  We believe in things that are tangible, measurable, predictable.  So what do we do with systems that are not fully predictable?  We assume that they are predictable, and, even worse, we act as if they are predictable. The most obvious of this phenomenon in business is the uncertainty related to the revenue/sales forecast.  We use terms such as demand variability or demand volatility instead because they imply that yes, demand is variable/volatile, but it is predictable, if only we knew all the variables required to predict demand exactly. But the term uncertainty makes us feel, well, uncertain. How can we have confidence in our models if we are uncertain about a key input variable?

  But uncertainty permeates supply chains and operations. From yield uncertainty, to cycle time uncertainty, to transportation lead time uncertainty, to new product adoption uncertainty.  And many more. I contend that we are far better advised to focus on the skills and processes required to be agile in the face of uncertainty than in the effort to understand “all” the causes of uncertainty and, by extension, to try to remove all uncertainty from our understanding of the market conditions in which we operate.

  Planning is important, but the skills and processes for early detection of discrepancies between reality and what we predicted, and the agility to respond quickly and profitably to these discrepancies should be equivalent skills.

  Let us learn how to absorb and respond to uncertainty rather than thinking we can design it out of our operations and processes.

My take is that each of the concepts that Prof Randall described as challenging Newton’s Laws have an equivalent not only in supply chain management, but more importantly in the more general concept of Business Operations, which, in Wikipedia, is defined as:

The outcome of business operations is the harvesting of value from assets owned by a business. Assets can be either physical or intangible. An example of value derived from a physical asset like a building is rent. An example of value derived from an intangible asset like an idea is a royalty. The effort involved in “harvesting” this value is what constitutes business operations cycles.

As we can see from this definition, supply chain management is a more specific definition of Operations applied to companies that have physical products, but even in these companies Operations is a broader concept that relates to the business activities that even manufacturing organizations carry out in order to satisfy customer demand and, hopefully, make a profit, including Marketing, Product Design, etc.

In other words, balancing supply and demand of anything is everything.

As always I welcome comments, arguments, and contrary opinions.  They drive our collective learning.

For the first time at our user conference, Kinexions, we invited in a number of analysts, bloggers, and consultants to give them a deep dive on Kinaxis and the roadmap for RapidResponse. We had an afternoon session devoted to the Influencers in which we brought in some customers to speak about their journey with RapidResponse.

But the highlight for me was the Influencer Panel I hosted as the last event of the conference.  We had great participation from:

• Andy Coldrick of Ling-Coldrick
• Bob Ferrari of The Ferrari Research and Consulting Group
• Russell Goodman of SupplyChainBrain.com
• PJ Jakovljevic of Technology Evaluation Centers (TEC)

While all the panelists added greatly to the discussion, two highlights for me were comments made by Andy and PJ.

Andy said that we have moved from thinking we need a single number forecast (and therefore single number plan) to understanding that we need a single perspective and a range of plans that cover range of possible business conditions under which we will operate over the next period. My take on Andy’s point is that “what-if” analysis is an absolutely core capability at every level of planning, be that strategic , tactical, or operational.  Being able to understand what levers are available to you and the impact that pulling these levers will have on financial and operational metrics is crucial to developing flexibility and agility in your supply chain and broader operations functions.

PJ used the dramatic failure of the Boston Red Sox in August to illustrate that planning is not enough.  As PJ told it, the Red Sox had done a tremendous amount of planning over the past few years which is what resulted in their great season up to August.  But what management failed to do is monitor the health of the star performers and only realized that some players had put on as much as 15 lbs.  Even worse their mechanisms for responding were not in place meaning that they had no way of getting relief pitchers or other key players at such short notice. PJ’s anecdote captured my view that planning is not enough very well.  It also ties in very well with Andy’s observation about a range of plans.

How Andy’s and PJ’s comments link together is that you need to monitor how your operations, particular customer demand, are matching up with what you anticipated (your operational plan), and respond very quickly when the two do not match.  Having pre-evaluated a range of possibilities means that you are able to respond with confidence, even though reality will never quite match any of the scenarios you had pre-analyzed

Planning is not enough, but we all have to do it. Not planning would be very stupid. But not building the capabilities to detect when reality does not match the plan very quickly and then respond profitably to reality is equally short-sighted. Plan-Monitor-Respond.

At the annual Gartner Supply Chain Conference (which used to be the AMR Research Supply Chain Conference) Jane Barrett introduced the term VUCA – Volatility, Uncertainty, Complexity, and Ambiguity As I stated in a blog after the conference, far too much effort is spent on figuring out how to remove VUCA from supply chains, how to suppress it.  At the time I wrote that people should embrace volatility. So I was very pleased when four months later the Harvard Business Review headlined the September issue with “Embracing Complexity,” another of the elements of VUCA. While the articles were definitely interesting, there were several lively debates in the Comments section.  What struck me in the comments was the polarization between those that believe one can pull apart a system in order understand it and then use this knowledge to reduce the complexity and thereby dampen the volatility. On the other hand there are those that believe that most system are too complex to understand completely, that the whole is a lot more than the sum of the parts.  I fall more into the latter group than the former.

What fascinates me is that this same duality of ‘simplicity’ and ‘complexity’ plays itself out in supply chain management between those that believe if we could only create the perfect plan all would be OK, and those, like me, who believe the perfect plan is not achievable so you had best focus on what Hau Lee calls the “Triple-A” supply chain – Agile, Aligned, Adaptable.  But I don’t fall into the group that believe planning is “useless, ” but if we accept that we all have limited budgets, too much of that budget is focused on creating the perfect plan and too little of the budget is focused on developing the skills and capabilities to respond very quickly to real demand. It is only the very rich or the very stupid that do not plan for retirement. But life has interceded to bring us not one, but two major down turns in the stock market in three years.  What do we do now?  Understanding how we recover is not a simple 1+1=2 equation.  It is a lot more complex than that.

This is true too of most decisions made by companies, not only in their supply chains, but their operations as a whole.  What we need is some way to test alternatives – “what-if’s” – and evaluate the best course of action based on input from multiple people that allows me to balance hard facts with subjective option.

This is the essence of embracing complexity. There is no “easy” button because there is no linear relationship between action and result, cause and effect.  Before anyone discounts my views, let’s have a look at some points from the HBR articles.

• Embracing Complexity – Interview of Michael Mauboussin
  • Consider capital markets… complexity doesn’t lend itself to tidy mathematics in the way that some traditional, linear financial models do.
  • …humans are incredibly good at linking cause and effect – sometime too good. Ten thousand years ago most cause and effect was pretty clear…but it means that when you see something occur in a complex adaptive system, your mind is going to create a narrative to explain what happened…we have a tendency to think that certain causes will lead to particular effects.
  • …we tend to listen to experts, although it’s been well documented that expert predictions are quite poor.
  • …in complex adaptive systems, a rapidly changing environment, we don’t really know how things are going to unfold…recommend creating a set of decision rules…let people decide on the fly in the field what they think makes most sense…never violate the basic rules, but they have a lot of flexibility to actually decide from moment to moment.

• Learning to Live with Complexity – Gökce Sargut and Rita Gunther McGrath
  • Rare events can be more significant than average ones – and may occur more often than we think
  • Human beings’ cognitive limitations mean that no manager can understand all aspects of the business, but many refuse to acknowledge this limits
  • We’ve observed two problems commonly faced by managers of complex systems: unintended consequences and difficulty making sense of a situation
  • Embedded in many analytic tools are two assumptions that don’t hold for complex systems
    • The first is that observations of phenomena are truly independent
      • This is often not the case in complex systems, with their highly interconnected parts
    • The second is that it is possible to extrapolate averages or medians to entire populations
      • It shows up when we fail to consider that outliers are often more interesting than the average case.
      • In business, the problem shows up when companies try to predict customer behavior on the basis of average response.

• Smart Rules: Six Way to Get People to Solve Problems Without You – Yves Morieux
  • CEOs commit to 25 to 40 performance imperatives today compared to 4 to 6 in 1955
  • Many of those requirements appear to be in conflict
    • Customers want to satisfy their customers who demand low prices and high quality
  • In complicated organizations, managers spend 40 percent of their time writing reports and 30 percent to 60 percent of it in meetings
    • Note to reader: Complicated and Complex are not the same thing, though often companies will try to put in place complicated processes to manage complex situations, which only compounds the issue.
  • To address each new [business] requirement, companies typically set up a dedicated function and then create systems to coordinate it with other functions.  That explains why organizational (the number of procedures, vertical layers, interface structures, coordination bodies, and decision approvals) has seen a sharp increase.  This complicatedness hurts productivity and employee engagement.

In “Learning to Live with Complexity” the authors advocate that simulating the behavior of a system is key to both understanding and the ability to respond appropriately.  In fact they sate that

Instead of extrapolating from irrelevant medians, look for modeling that will give you insight into the system and the ways in which the various elements interact.

They go on to state one needs three types of predictive information in order to strike a happy medium between excessive and convoluted scenarios about what might happen and over-simplistic linear predictions that are over reliant on past knowledge:

• Lagging: data about what has already happened.
  • Most financial metrics and key performance indicators fall into this bucket
• Current: data about where you stand right now
  • Your pipeline of opportunities might be in this bucket
• Leading: data about where things could go and how the system might respond to a range of possibilities

My take on all of this – and I would be very interested in your opinion – is that the three articles together are suggesting that there are some ways in which complexity can be harnessed through simplicity.  Notice that I did not use the term simplification.  All too often we fall into the trap of simplification or linearization, meaning that the model we use to understand the system does not incorporate many of the subtleties that can lead to very different behavior for similar – but not the same – inputs. Examples of simplification are “predict customer behavior on the basis of average response” and “outliers are often more interesting than the average case”.

Simplicity is about developing a culture of exploration and providing a capability to evaluate alternatives in an off-line manner before executing on them.  As stated in “Smart Rules: Six Way to Get People to Solve Problems Without You”:

“Companies clearly need a better way to manage complexity. In our work with clients and in our research, we believe, we’ve found a different and far more effective approach. It does not involve attempting to impose formal guidelines and processes on frontline employees; rather, it entails creating an environment in which employees can work with one another to develop creative solutions to complex challenges. This approach leads to organizations that ably address numerous fluid and contradictory requirements without structural and procedural complicatedness.”

Amen to that.

Here is part 2 on the important of Response Management. Make sure to check out part 1!

Kerry Zuber, one of my colleagues who is a Lean/Six Sigma black belt, has introduced me to two great terms that are crucial to understanding the value and key capabilities required of effective response management:

• Time to detect
  This is all about knowing sooner that some event has occurred that is creating risk or harm to your organization or supply chain.  This may be that a customer has changed their mind about an order, or a supplier has de-committed on a delivery date or quantity, or that a tsunami has occurred in Japan that has wiped out a large part of world-wide semiconductor manufacturing capacity. But not only knowing about the event sooner, but also knowing about the impact sooner (which lines will go down, which orders will be impacted), and, perhaps as importantly, knowing who is impacted sooner.  Without knowing all of these three things you cannot act.
• Time to correct
  Once you know what went wrong, you need to act quickly to find a solution through compromise across multiple functions, even multiple tiers, often with competing objectives.  The timeliness of resolution is a key measure of the quality of the solution. We still hear of companies that have not fully understood the impact of the Japanese tsunami on their operations and their ability to satisfy demand, let alone put into place a recovery plan. And yet it is often the day-to-day events, such as when a customer changes their mind on an order, where most margin or customer satisfaction is lost because either the response to the customer is too slow or actions are taking with little understanding of the financial and operational impact.

The significance of these two terms is that for the most part the physical supply chain is a constraint in that your ability to change it in the short term is very limited so every minute lost in detecting something ‘wrong’ and, once the event has been detected, every minute used to determine what to do to reduce or eliminate the risk or harm, is a minute taken away from being able to use the physical supply chain in some manner to reduce the risk.  Even in situation where there is next to nothing that can be done to eliminate the risk, if you know sooner you can at least tell your customers who are impacted about the impact sooner so that they have more time to react.

There are three core capabilities required that impact the time to detect and the time to correct:

• Being able to represent several versions of a multi-tier supply chain in a single data model so that there is a single version of the truth, and being able to represent several states – historical, present, and future – of the supply chain in order to determine what of significance has changed that is causing future harm to the organization, as well as several scenarios that represent alternative ways of solving the problems.
• Being able to determine who is impacted by the change and therefore who needs to be alerted, not only in your own function, but in other function within your own organization and people in your customers, contract manufacturers, or component suppliers.
• Being able to bring those people impacted by other the original threat or proposed actions together in order to collaborate on the resolution of a problem by testing several ways of resolving the issue and being able to reach compromise through sharing of the impact of possible courses of action on financial and operational metrics.

These concepts and capabilities can be applied to a broad range of business problems, not just the supply chain.  In the diagram on the left we can substitute other function’s analytics for the supply chain analytics and still retain the core capabilities described above to solve response management problems in a wide range of business activity.  Within manufacturing industries the supply chain is absolutely central to being able to get product to market.  But within manufacturing companies response management issues are not constrained to the supply chain.  Workforce management is an easy one to describe in this context because it is (or rather it should be) tightly coupled to the longer term revenue forecast. If a company anticipates their demand or sale revenue growing they need to ramp up recruitment because of the long lead time in recruiting and training people, but they also need to be very quick in shutting off the recruitment process if they see that their revenue is not growing as anticipated. In other words workforce management also requires the ability to plan for the future based upon anticipated demand, monitor actual performance against anticipated performance, and a quick response to correct the misalignments.

What drives the need for response management is the fundamental fact that we cannot predict the future with a great deal of accuracy and our success or failure is dependent on how effectively we respond to the present, how we manage in the “now”.  What varies across industries, across functions, and across business processes is how far into the future we need to predict what we think is going to happen, and how quickly we can affect change.  But what does not change is that the time it takes us to detect that something of significance has changed and decide on the best course of action determines both the time we have available to carry out the course of action and the feasibility of the course of action, which is of course a key measure of the quality of the decision.

So where do you think the next breakthrough will come from?  Better forecasting or better response management?

Response Management is all about what you do when what you planned to do (forecast) does not match what is happening (actuals). This can be applied to any forecast, whether that is the traditional sales forecast of the number of units sold in a region, the projected cash position of a company, the expected completion of a new factory, or the commercial availability of a new product.  For this discussion I will focus on traditional supply chain forecasting because that is the underlying data I have available to support my description.

First let’s examine why companies create a revenue or sales forecast.  It is intuitively obvious that they do so to determine how much of their product they believe the market will purchase. But this is only the 10 percent of the iceberg that floats above the water.  The other 90 percent of the story  is that the forecast is used to drive investment plans in marketing, engineering, manufacturing capacity, inventory planning, and strategic sourcing.  In other words, if the forecast is inaccurate there are a whole bunch of consequences beyond just getting the revenue forecast incorrect.  Of course here I am referring to the longer term forecast used to drive the S&OP process or even the annual operating plan/budget and the principal forecast is investment.

The medium term forecast is used to drive how an existing infrastructure, particularly the supply chain infrastructure, can be used to meet market demand.  This is the period in which investments are not going to make a difference.  As one colleague told me, nine woman are not going to be able to produce a baby in one month, no matter how hard they try.  But it is important to realize that even within this period the significance of forecasting varies by industry, as is reflected in the diagram to the left from an Aberdeen report titled “Demand Management – Bridging External Market Inputs with Internal Statistical Forecasting” published in June 2011.  In this context it is important to understand how the difference between demand lead time and supply lead time varies across industries.  Airplane manufacturers typically have a 3-5 year lead time from a firm order to delivery date, with a manufacturing lead time of about six months.  A diaper manufacturer has between a one day to one week demand lead time, and about a three week supply lead time.  On the other hand, an airplane is highly customized to a specific customer’s order, whereas  a diaper is a diaper, even though there are variations.  The complexity in a plane is in how bought materials are coordinated to assembly a customer specific product.  The complexity in a diaper is in how it is distributed with hundreds, in some cases, thousands, of distribution locations through a multi-tier distribution network.

The combination of the difference between demand and supply lead time and degree of final product configurability determines the extent to which a company can use postponement as a strategy to mitigate the risks associated with forecasting incorrectly.  CPG companies, such as diaper manufacturers, typically have little opportunity to postpone at the manufacturing stage, and therefore will use a make-to-stock supply model. However, CPG companies can postpone distribution through their multi-level distribution network. On the other had an airplane manufacturer will seldom order components before they have a firm order, which is a “build-to-order “ postponement strategy.  Imagine the financial risk that an airplane manufacturer would be taking on if they built an airplane and then tried to find someone to buy it.  In reality a diaper manufacturer is taking on similar risk, but the risk is distributed over millions of diapers and many thousands of consumers, so their risk per item is much smaller.

And yet a semiconductor company takes on the same level of risk when they commit $3B-$5B to build a new factory over the next 24-36 months that an airplane manufacturer would be taking on if they used a “build-to-stock” supply chain model.  It is an equivalent risk when a company decides to invest in penetrating a new market and needs to invest in establishing a local legal entity, office rentals, marketing, and hiring and training local staff.  These are big commitments of funds that are based upon the anticipated behavior of the market, a forecast, and once in execution they take a lot unwind.

Terra Technology, one of the leading forecasting technology companies focusing on the CPG space, where statistical forecasting is very prevalent, published a study on forecast accuracy titled “2011Forecasting Performance Benchmark Study” in which they study best practice demand forecasting in leading CPG companies.  The reason that statistical forecasting is so prevalent in CPG is that demand is relatively stable – when compared with other industries – and products have fairly long life cycles so there is a lot of history to rely on.  And yet in the introduction the authors note that:

• Promotional volume jumped about 75 percent in 2010 as companies looked to drive sales by offering consumers additional value. Contrary to conventional wisdom, promotional periods are actually forecast as accurately as non-promotional periods for the same items. Perhaps this is due to the extra time demand planners spend on promotions. Not surprisingly, the bias is considerably higher during promotions.
• New products remain hard to forecast with weekly item/location error rates of 65 percent, compared to 46 percent for existing products
• Demand Sensing continues to provide a consistent step change in forecast accuracy for all scenarios, including promotions and new products. For the combined 2009-2010 period, Demand Sensing reduces average weekly error by 40 percent.
• Outdated mathematics and optimistic marketing departments continue to undermine the performance of Demand Planning. This highlights the opportunity for a structured approach to forecasting based on additional demand signals and new mathematics.
• MAPE is the correct measure for supply chain performance since it is the error that Product Supply contends with. However, insight from the report raises questions regarding MAPE as the proper metric to evaluate the performance of Demand Planning because it may not properly reflect the value add by planners. Using the dataset as a resource, Terra plans to evaluate a number of different metrics in the future editions of the study.

Before analyzing the numbers, let me reiterate that CPG, when compared with industrial equipment manufacturing for example, has stable demand and long product life cycles, which, in theory, means that CPG companies should be able to use statistical forecasting to predict demand fairly accurately, but, as the Terra Technology study shows, they can’t. When we consider the three month digital camera lifecycles and six months cell phone lifecycles, the relevance of the second bullet about the forecast accuracy of new products becomes very apparent.  New product launch is typically the time when the most margin is captured, an yet it is also the time when the forecast is most inaccurate, meaning that a lot of margin is not captured.  And therefore anything Terra describes in the report is significantly worse in industries with shorter product life cycles, which automatically leads to more volatile demand. In fact our anecdotal evidence from speaking to companies in consumer electronics is that their forecast accuracy is very seldom above 50 percent regardless of the life cycle stage of the product largely because the short life cycle means that the product is either being introduced or being phased out.

In the Aberdeen study referred to above, the author notes that Best in Class performance

• Average percent forecast accuracy at product family level (across a three-month time period) is 87.1 percent
• Average percent forecast accuracy at individual SKU item level (across a three-month time period) is 70.8 percent

In the Terra Technology study the author notes that

• During 2010, the average weekly error was 48 percent with a slight difference by top performers, who came in six points lower at 42 percent.
• Meanwhile, monthly error averaged 33 percent with a five point spread between top performers and the average.

With leading CPG companies struggling to get forecast error below 30-40 percent after years of trying, with product life cycles shrinking every year, and with market differentiation leading to product proliferation, what is the likelihood that forecast accuracy will improve dramatically over the next five years?  In my opinion very little.  So where do you think your next breakthrough in supply chain performance will come from?

• Learning to forecast and plan better?
• Learning to respond profitably to actual demand, or plan variance?

The third bullet from the Terra Technology report introduction states that “Demand Sensing continues to provide a consistent step change in forecast accuracy for all scenarios, including promotions and new products. For the combined 2009-2010 period, Demand Sensing reduces average weekly error by 40 percent.” hints at the importance of response management, but does not go far enough since it only indicates how to get a better understanding of demand in the short term. Response management is about satisfying that demand in the most profitable manner.  Demand sensing is about knowing sooner about demand shifts. Actually I find this standard definition of demand sensing a bit funny and it is a good illustration of how planning is seen from the wrong perspective. The term demand shift implies that the forecast is correct and somehow demand has shifted in time or location, which is of course completely wrong. The customer didn’t buy the ‘wrong’ stuff in the ‘wrong’ amount at the ‘wrong’ time. What actually happened is that we predicted incorrectly what they wanted, how much they wanted, and when they wanted it, even where they wanted it.  Nevertheless demand shift captures the concept that actual demand occurs at a time, quantity, price, and/or location that was not expected.  While demand sensing is really important, even perhaps the most important aspect of supply chain execution, it only describes part of the response management story. Response management is about knowing sooner about a broad range of supply chain disruptions and acting faster to provide a profitable response.

Stay tuned for part two of “The importance of Response Management” tomorrow!

As I wrote earlier, the topic of control towers seems to popping up everywhere lately. But I am sure that there are as many definitions of a control tower as there are analysts and commentators. So I thought I would weigh in with my own definition. 

• Let’s start from the perspective that a control tower is a decision making tool, not merely a visibility tool. Visibility is a precursor, a building block. In optimization parlance, visibility is an adequate but not sufficient definition of a control tower.
• A key requirement of a control tower is that several simulations can be performed to test high level hypotheses at a granular level to determine the potential effect on operational and financial metrics, as well as identify any constraints.
• A control tower by definition must cross functional and/or organizational boundaries. I think we can all agree that the term implies a view across a fairly broad span of operations, not a narrow functional perspective.
• Because a control tower crosses function and/or organizational boundaries it must support collaboration in order for people to be able to share perspectives and arrive at consensus and compromise that balances competing objectives and performance measures.
• Direct people to what is important and alert then to what is critical, and of course within their span of control.
• Identify consequences to underlying events, as well as the people who are responsible for both the events and the consequences, so that they can arrive at a joint resolution.

The analogy of the airport control tower has great deal applicability to supply chains. All of the systems required to manage airplane departures and arrivals, airplane cargo management, bookings, maintenance etc. are interwoven into a real-time mesh of information and decision making in addition to the visibility that an elevated position and radar afford. In contrast company supply chains, by and large, are separate, isolated, data ‘islands.’ The very best of these typically have ‘batch’ type connections to each other. When things go wrong the signals are late to arrive and impacts difficult to estimate. Imagine a supply chain world that is run like the world’s airports, with common standards (like pilot/tower communication always in English) and systems designed to communicate with each other in real-time. This is a very instructive model with standards and collaboration at its heart.

Below is what Sameer Patel wrote earlier this year in a blog titled “2011 Business and Technology Forecast,” with my emphasis:

As organizations increasingly start to see the benefits of deploying social and collaborative initiatives to improve employee, customer and partner engagement, they will soon begin to realize that the decade old notion of streamlining repeatable processes made popular by ERP and CRM system-of-record deployments was largely over promised. In practice, customers and prospects have unique questions not answerable in the knowledge base or by marketing; employees living in rigid ERP systems need to constantly find experts who have the best answers and to collaborate with them. And reseller partners are constantly spending time looking for the right answers not available on asynchronous partner portals to keep end customers happy. Silo’d but open collaboration initiatives on activity streams and other enterprise social networking utilities currently being deployed will expose such engagement not historically possible in an ERP or CRM laden design. Consequently, LOB and IT leadership will realize that traditional process approaches and fluid collaborative constructs need to come together to truly accelerate business outcomes.

My definition above focuses on the process and organizational requirements, but also on some key functional capabilities. What is clear from Sameer’s perspective is that by definition the solution will not be coming out of ERP or CRM systems. I think his observation that;

… they will soon begin to realize that the decade old notion of streamlining repeatable processes made popular by ERP and CRM system-of-record deployments was largely over promised.

is particularly important because for years these systems have been sold on the promise that they encapsulate best practice processes. If a process is repeatable isn’t it by definition a standard process and not a best practice process? OK, I concede that the process may be best practice and repeatable in a few organizations, but if everyone deploys a standardized best practice process, is it still best practice? I could go on forever on this topic, but I will leave it to Sameer’s devastating observation that;

In practice, customers and prospects have unique questions not answerable in the knowledge base or by marketing; employees living in rigid ERP systems need to constantly find experts who have the best answers and to collaborate with them.

And I will go further to state that not only should a control tower identify the experts for the employees, but in addition a control tower should allow an employee to suggest a resolution and to test its efficacy against operational and financial objectives. By all means get an expert to validate the assumptions and results, but all too often it will take too long to get expert feedback, especially if the expert must be identified, the problem described to the expert, and then have the expert investigate resolution in a separate system. The customer or prospect will have moved on to other problems by then.

In other words a thin collaboration capability that is little more than sharing ideas without the capability of evaluating the impact of the ideas is not likely to be of much value and is only a slight improvement on using phone, FAX, and Excel attachments. The issue isn’t that humans don’t have ideas, but rather that they don’t have a timely and effective manner of testing their ideas at a sufficiently granular level to ensure both feasibility and value. This is the key to having an effective control tower.

Gartner has also being weighing in on the topic of control towers, although nearly all the article focus on logistics visibility, hardly a ground breaking areas.  (Any references I make will require access to Gartner materials for full viewing.)  In an article titled “Supply Chain and Manufacturing Outsourcing Discussion with Supply Chain Leaders” published on (Michael Dominy, Hussain Mooraj; Nov 22, 2010), the authors define a control tower, that goes beyond logistics, as follows:

a “control tower” or centralized shared service organization to manage relationships and information between the company and its outsourced manufacturing, logistics and other service providers.

Unfortunately they don’t include a mention of collaborative decision making, but they do go on to state in the same article that;

Even if a partner delivers the supply chain visibility and events across the ecosystem of supply chain partners, the enterprise must control and direct the response across the supply network.

I can only presume that being able to control and direct the response across the supply network implies the ability to reach
a timely and consensus decision on what that response will be.

What I do like about this Gartner article is that it includes a maturity model. I am often guilty of describing nirvana without painting a picture of how to get there and the steps between. Something else that is brought out by the Gartner diagram and article is the applicability of control towers to outsourced environments. In other words when key operational activities are outsourced it is necessary to get on overview of the operations in order to provide the control and direction to the extended supply chain.

So far I have focused very much on the definition of an “operational” control tower. In my next blog I will weigh in on a “corporate” control tower that focuses more on the needs of the executive level. In the meantime please give me some feedback. I’d love to hear your ideas about control towers and the convergence of consumer-led social networks concepts, enterprise 2.0 ideas, and control towers.

It is amazing how there can be a synergy that drives several parties to coalesce around the same idea. I once read about a crazy idea that if one cow learns how to cross a cattle grid this capability will somehow be transmitted to all other cows. Sounds pretty quirky right?

Well somehow the notion of a supply chain control tower has gained broad interest lately across a broad spectrum of industries. I stole the title of my blog from James Cooke of CSCMP who wrote about the same topic a few weeks ago. James comments that:

In one notable session, for instance, a Procter & Gamble executive described the company’s initiative to set up “control towers” to help it manage its distribution networks in emerging markets in Eastern Europe, the Middle East, and Africa. In the supply chain sense, a control tower is a location that provides visibility into inbound and outbound distribution flows—much as a control tower coordinates the movement of aircraft to and from an airport. Although a number of companies in Europe have adopted the control tower concept, P&G’s case is particularly interesting because executives believe that this approach will help the consumer goods giant control distribution costs and manage supply flows into emerging markets.

I must admit that the last sentence left me wondering what the justification was for the other control towers?  It’s the ‘particularly interesting’ bit that I found a bit confusing, not that P&G expects real benefits from the control tower, which I would assume.

From my perspective, the application of the control tower concept only to distribution is a fairly narrow use of a very powerful concept. A multi-tier control tower in the high-tech/electronics space that spans customer location, the OEM, several contract manufacturers, and component suppliers has much greater likelihood of both success and benefit.  Time and time again I hear about visibility being a problem in this environment.  What better way to get visibility than to have a single system that sucks in data from multiple systems of record (not just ERP) to provide this visibility?

But visibility is not enough.  Don’t get me wrong, knowing about an issue is hugely better than not knowing about an issue until only after you feel the impact.  But even better than knowing about an issue is knowing about the consequences of that issue.  Will it affect customers service?  Will more material be required? Will we need an extra shift?  Will we miss the quarter?  Without this type of consequence analysis, knowing of the issue itself is relatively uninteresting and of little value (beyond the people directly affected that is.)

Even better, is to be able to perform “what-if” analysis to determine the best course of action to avoid the risks and negative consequences of events. But in a multi-tier, and therefore multi-organization supply chain there is no obvious ‘best way’ to resolve the issue in the best interests of all the participants.  There is a natural tension between the objectives of the different tiers that has to be negotiated. However, in a multi-tier supply chain, how do you determine who is impacted and who needs to take action? After all, without this knowledge how is resolution possible? I suppose resolution is still possible using conventional means – exporting data into Excel and emailing it around followed by several conference calls – but the time this takes is a primary cause for supply chain waste and inefficiency and the very problem that a control tower will solve…well, a control tower that goes beyond simple visibility anyway. Visibility is a start, consequence evaluation the next step, “what-if” analysis leading to resolution is the third step, and multi-enterprise collaboration the final stage and most complete description of a control tower.

So what is different now?  Do we trust our trading partner more than we did two years ago? To be honest, I am not sure.  Perhaps it is that we are getting management coming through that is very familiar and comfortable with sharing information across the internet, the so called Millenials.  But, as I never tire of pointing out, we have been talking about control tower like concepts for 50 years ever since Jay Forrester published his work in industrial dynamics – “Industrial Dynamics-A Major Breakthrough for Decision Makers,” in: Harvard Business Review, 1958, Vol. 36, No. 4, pp. 37–66 – and the bull whip effect in which he states that:

Company (and supply chain) will come to be recognized not as a collection of separate functions but as a system in which the flows of information, materials, manpower, capital equipment, and money setup forces that determine the basic tendencies towards growth, fluctuation, and decline.

For years, many analysts such as Lora Cecere while at AMR Research and now the rest of the Gartner supply chain people have been writing about demand-driven value networks (requires membership), which, while slightly different in concept, is all about pushing the demand signal down the supply chain across functional and organizational boundaries. Without a doubt, technical solutions have improved and lately even the concept of social networks has gained a lot of acceptance in the consumer community. But technology is the enabler, perhaps the driver too in some cases, for example Facebook. But we need to get well beyond the social graph provided by the likes of Facebook and the interest graph required by social media marketing to the responsibility graph required in B2B collaboration across enterprise boundaries. We need to get from ‘this is what is happening’ to ‘who needs to know.’

As I mentioned above, I do think it is the arrivals of the Millenials in the workforce, particularly in management positions, that is leading to the re-evaluation of existing inter-company processes. This combined with the huge adoption of social networks applications such as Facebook and Twitter in the consumer space has opened the curtain just a little.  I think we have some way to go before this all gels into something really significant, but it is very exciting to be part of the process, to see customers and prospects exploring control towers and making the first steps to meaningful adoption, well beyond simple visibility. And there is a significant body of discussion on the use of social media in the supply chain, such as Sameer Patel’s blog titled “Talking Collaborative Supply Chains at Enterprise 2.0 Conference” in which he states that:

Supply Chain efficiency is a big big deal. More over, this area of the enterprise technology stack and associated strategy has seen little to no innovation for well over a decade

Well, I am not quite sure of that, but the pace of innovation has sure slowed down, especially in the area of collaboration, particularly multi-tier collaboration.  In the blog above Sameer refers to an earlier blog he wrote on 2009 titled “Taming the Supply Chain beast, Enterprise 2.0 style” in which he states that:

… one of the biggest opportunities to enterprises using such social computing technology is be able to pry open the gates that lock out supply chain access to core processes such as product management, R&D, marketing, end customer support, etc. And conversely – giving these business units access to knowledgeable supply chain partners as well.

First of all, I find Sameer’s description too narrowly focused within the enterprise whereas I believe the greatest benefit to be achieved is from collaboration between enterprises. Second, I find that the lack of innovation has been on the part of the user community.  The activity in control towers is a clear indication that this has changed.

Mike Burkett wrote in this Mondays’ First Thing Monday, a regular Gartner commentary on supply chain issues that requires registration, that

Although there may be no such thing as a perfect forecast, there are plenty of opportunities for improvement and managing demand in support of a more cost-effective supply response. Better collaboration between trading partners is the necessary next step toward waste elimination in the value chain.

Maximizing value from the supply chain won’t be reached without improved collaboration between trading partners.

In fact, Mike identifies the lack of collaboration between trading partners as the primary cause of demand volatility. (I agree that the lack of demand visibility increases demand volatility, but there are other industry dynamics that are increasing demand variability.)  Of course, visibility and collaboration are the key messages behind The Beer Game (or beer distribution game) which was originally invented in the 1960s by Jay Forrester at MIT as a result of his work on system dynamics to illustrate the bull-whip effect.  Absolutely critical to a smoother functioning supply chain is demand visibility across the entire supply chain.  Of course visibility isn’t a very mature form of collaboration, but it is at least a start and a necessary precursor to more mature forms of collaboration. And let’s be honest, after 50 years we still haven’t really got to effective demand visibility, although there have been improvements.  To get some estimate of how well your company is performing in this regard you can take the Gartner Demand-Driven Value Network (DDVN) maturity self-assessment.(Registration or Gartner membership required.)

But it isn’t just so-called Easy-West collaboration between customers and suppliers that is required.  I would be happy to start with the so-called North-South collaboration between strategic and financial plans, usually the focal area of the executive suite and Finance, and operational plans. Some analysts and commentators have called this integrated business planning, which also dates in concept from the 1960’s. I’d even settle for East-West collaboration between demand and supply functions within one organization, including Gartner’s stage 4 Sales & Operations Planning (S&OP) maturity. There is no inter-company process that is a better measure of collaboration than S&OP done well.  According to Jane Barrett of Gartner, most companies are stuck in stage 2 maturity.  This is largely due to the inertia and resistance to change so prevalent in many companies.

But how is it that 50 years on from Jay Forrester’s work we are still struggling with this issue? My opinion is expressed in a blog titled “Do you trust yourself to collaborate? The real barrier to collaboration is not technology, but trust”.  Let’s face it, technology can always be improved, especially the user experience. But the capabilities of the technology available for inter-company and intra-company collaboration exceeds the willingness of organizations to share information at the moment.  As Mike Burkett points out, huge gains can be achieved by collaboration, so why don’t companies try to capture these benefits?  My opinion is that the issue is the same that is preventing them moving from stage 2 to stage 3 S&OP process maturity, namely that they simply want to automate their existing manual processes and don’t realize that effective collaboration requires both new ways of working and supporting technology capabilities. 

Nothing captures this better than a Henry Ford quote that “If I had asked my customers what they wanted, they would have said faster horses.”  There is a strong synergy, even symbiosis, between technology and process.  In a great slide show titled “What if Peter Drucker taught Enterprise 2.0?”, Mark Fidelman states that

Neither technology nor people determines the other, but each shapes the other.

But let us recognize that the financial and operational risks of collaboration are tangible and can be high.  There is a good series on collaboration in the Harvard Business Review.  The one I like best dates from June 7, 2011 and is titled “Collaboration is risky.  Now get on with it.”  The author, Whitney Johnson, writes that

Why is teamwork so difficult?

Because collaboration is actually a pretty risky business. Perhaps, like me, you are generally of the mindset that two heads are better than one. But because your ideas frequently get co-opted, there’s a risk-reward imbalance that makes you reluctant to engage. Or maybe you’ve reached out to a potential collaborator only to have your lack of expertise exploited. So, rather than ever again experiencing the one-two punch of ignorance and vulnerability, you’d prefer to soldier on alone. In both instances, the fundamental barrier to collaboration is a lack of trust.

While writing about individuals within a group, clearly Whitney’s observations and opinions are very applicable to collaboration between functions and organizations.  Her prescription is to

1. Start with simple exchanges where the cost of betrayal is low.
2. Remember that our collaborators are competent.
3. Don’t take advantage of our collaborators’ deficiencies.
4. Give others their due, and expect yours in return.

Sound advice.  Yet so difficult to put into practice.  The comments section is well worth a read too, including links to some interesting materials.

Mike Burkett concludes his First Things Monday contribution with the observation that

By closing gaps between partner nodes in the supply chain, there’s an opportunity to address the unnecessary waste across the value chain.

These are the hard benefits of supply chain collaboration.  They are real and they are achievable.  Yes, the risks are real, but the rewards are equally real.