Yes, indeed I am talking about is how the demand changes, even if your forecast is 100% accurate. I use the term volatility to represent the amount demand changes from period to period.

I do not agree that “Anyone can manage this demand pattern when it is 100% accurate”. (Well, perhaps theoretically they can with high inventories.) This is absolutely key to my posting. Supply chains are highly non-linear and non-deterministic. The models we use are approximations and the data values are more or less correct. This is the point you make in your last sentence.

The question I raise in my posting is if your forecast isn’t 100% accurate and your execution of the plan isn’t 100% accurate, what are you going to do about it. The prospect with whom I was talking was making the assumption that having a 100% accurate forecast would remove all demand volatility, and he wouldn’t need to worry about the supply side.

There is no doubt that having a forecast that has an accuracy of less than 50% will create a lot of turmoil in your supply chain, so getting a better forecast is a good thing in these situations. Ultimately thoug, the breakthrough in performance will come from responding better to demand and supply disruptions, not from better planning.

Google “Coeeficient of Variation” on Google for more discussion on demand volatility and how this differs across industries.

Regards
Trevor

I do not understand your terminology. You said “Having a 100% accurate forecast does not reduce the demand volatility one bit” Are you talking about the variation in demand even when it is predicted 100% accurately? Ex: 2,100,5,250 as monthly demand? Any one can mange this demand pattern when it is 100% accurate. The problem we face is forecast accuracy not being 100% and also the execution of the plan not being 100% (due to supply variation)

This is a great dialoque and allow me to share some additional thoughts.

In my view, the need for accurate forecasting has meaning within the context of the business model that the supply chain must support. That may be different for make-to-order or make-to-stock. If product demand is mature and highly predictable, than a forecasting process will certianly pay dividends.

However, in today’s dynamic business world where the forces of most efficient supply chain meet the more empowered customer, business models and product cycles are becoming more dynamic, resulting in more demand volatility. Thus, your argument that accurate forecasting may not matter, does have some merit for today’s more dynamic supply chains.

My belief is that when demand volaitility increses, planning and execution processes must merge together. A few years ago, I helped SAP develop a vision framework termed “the adaptive supply chain”. Putting the marketing intent and ultimate execution aside for a moment, the concept and tenets were that that supply chain planning and execution processes had to come together, far different than the classical MRP/DRP models defined in texts so many years ago. Planning would shift more toward insuring that long-term capacity and critical supply needs were resourced appropriately, along with the ability to perform continuous what-if scenario analysis. Execution would shift to a combination of predicting and sensing of product demand, coupled with highly responsive demand and supply response capability. The adaptive supply chain era has now arrived, and planning and execution processes must now adapt.

Bob Ferrari
Executive Editor of Supply Chain Matters

Enough people have interpretted that I am arguing against the importance of forecasting that I can only conclude that I have not communicated my ideas very well. I am not, though I am focussing more on execution than the tactical aspects of forecasting. I have no question that forecasting adds a great deal of value in all the tactical planning areas you list.

However, I don’t see how this addresses the title of my blog. Having a 100% accurate forecast does not reduce the demand volatility one bit, and it is the volatility that causes all the problems on the supply side. Forecast accuracy is not the same as demand volatility, though high demand volatility does make it more difficult to create an accurate forecast.

The point I am really trying to address is the following: So we have done our best at forecasting and planning, now we need to execute. And guess what, in environments with high demand volatility, the customers are not ordering the items and quantities in the periods we anticipated and supply has not lived up to their commitments. What do we do now? Simply record this as a low order fulfillment KPI? Or a forecast error KPI? I don’t think this is good enough.

I am arguing that the divide between planning and execution is in fact a chasm. This chasm will only get wider as demand volatility and uncertainty increases for all the reasons many analysts have written about in the past. There is a great deal of value to be extracted from the supply chain by being able to bridge this chasm.

Happy new year.

Welcome to a firestorm. Let me see if I can help. In short, folks are confused about demand planning. They try to force tactical demand planning concepts into the shorter-term operational horizon (0-10 weeks) where it does not work very well. This confusion is compounded when there is a volatile demand environment because in these environments, the concepts of Distribution Requirements Planning (DRP) and Materials Requirements Planning (MRP) do not work vey well either. Let me share some insights based on seven years as an industry analyst….

In your blog, you are reflecting the traditional definition of demand planning. Let’s face it, demand planning was defined too narrowly in the definition of Advanced Planning Systems (APS). The concept of where companies forecast the future demand based on historic demand, and the forecast is consumed by supply which translates to order fulfillment is not sufficient. The more important role of demand forecasting is in planning.

For that reason, I would argue the inverse to your logic. I believe that as demand volatility increases that good demand planning processes matter more than ever. These are longer term (12-18 months) forecasting processes based on market views. Why? There are five processes that need good long-term demand forecasting processes more than ever with increased demand volatility:

-Building and executing commodity strategies: What positions should we take on commodity buying? What do we hedge?
-Pricing and go-to-market positioning: Which markets do we enter when? What is the price elasticity for our products in these markets? What is the potential for these products and services at these price points?
-Long-term asset planning: What assets do I need to have when? And, where?
-Strategic relationship management: Which contracts do I need to have with which partners?
-New product launch: What is the market potential for the new products that I am bringing to market?

My logic is not solely tied to supply chain execution. The processes of order or contract fulfillment are too limiting. Unfortunately, all too many supply chain executives, were lulled to sleep by the powerpoints of APS vendors, and have largely missed the greater role of demand planning in driving supply chain excellence.

When the recession hit, the companies that did the best job of managing demand and translating demand into source, make, and deliver processes used econometric modeling and what if analysis on market drivers. They understood the impact of the market shifts before they happened and had developed contingency plans.

In make to stock environments, these companies also did not confuse demand planning with demand sensing. Longer term demand planning processes cannot be confused with shorter-term demand consumption or demand execution processes. In volatile environments, the concept of rules-based consumption is too limiting. Instead of using rules, to break the demand plan into time-phased requirements, supply requirements need to be modeled from the outside-in (from the market-back) using statistical modeling to forecast what is needed in the short-term horizon at a distribution center or for a plant to make. Just reacting to order demand will minimize opportunities (cycle stock management, transportation opportunities, etc), because customer orders do not represent true demand.

In make-to-order environments, short-term demand is driven by the configuration of the item. Too few companies have built good systems to translate tactical demand planning into specific material requirements. Configuration systems largely lie in isolation.

I think the breakthroughs will come thru innovation of process and collaboration. My first wish would be to go into a meeting and not just show inventory projections, but the error lines around that future prediction based on historical forecast bias and variability. Collaborating with not just the marketing people on whether this bias is real or fixable, but also with my procurement people on what do we really tell our upstream suppliers (and is there anyway to use this information to build visibility & trust in the supply chain). How this collaboration takes place, how often, and the robustness of it, will be the innovation, I predict.

As far as the demand variability, that’s life. I know one product that we were making that had 89% of demand in 3 months of the year with lead-times at > 120 days sometimes. That’s not a sustainable business unless you have an employee named Carnac the magnificient, or you have distributed risk to the business over other product lines.

One thing that I’ve been hearing from smaller businesses is the desire to fire some customers: ‘We’re just not going to serve customers that have unreliable demand (or monthly hockey sticks)–or give them low-priority’. Customers that help us have even, steady demand are kept. Has anyone else seen this behavior? Is it a recent increase?

This is a very good discussion. The general feeling here is that I am advocating that we don’t forecast. Far from it, though I am sceptical of putting “blind faith” in statistical forecasting methods.

I also want to state that my arguments are mostly focussed on the build-to-order and engineer-to-order segments. I accept that statistical forecasting has a role to play for many make-to-stock industries, though this is less true for consumer electronics and high-tech/electronics.

On the demand side, shorter product life-cycles and reduced customer loyalty are adding a lot of uncertainty to demand. On the supply side, outsourcing and off-shoring has both extended the supply lead time and increased supply uncertainty. Lean and, probably more importantly, the current economic downturn has reduced inventory levels greatly thereby reducing the ability of the supply chain to absorb either demand or supply variability.

It is in this context that I ask the question: Where do you think the next breakthrough will come? From planning better or from responding to plan variance better? Planning is good, but execution is what really matters.

What I am really arguing against is the belief that a plan is going to be realised fully. Or if we only put just the right amount of inventory in just the right place then we can “absorb” all the demand volatility. What if we can’t afford the inventory investment required?

Postponement is nothing other than removing inventory as a mechanism of absorbing demand and supply variabilty at the finished goods level and pushing these buffers further up the supply chain, perhaps even to the component level. So how does one respond to demand and supply variability below the postponement level?

I am arguing even more strongly that we cannot “optimize” the supply plan beyond a rudimentary level because:
a) we use deterministic methods to calculate the supply plan when in fact the supply chain is not only stochastic, but it is also highly non-linear.
b) the (deterministic) data we use to calculate our supply capabilities is at best an approximation of our true capabilities

So to what level is the “optimum” accurate? Is it even achievable?

I am not suggesting that we do not plan. We have many customers that plan far out into the future in order to estimate the supply capabilites required within the lead time for building new supply capacity. Or to evaluate the inventory policies to deploy.

What I am suggesting is that we recognise the limitations of the plan in satisfying true customer demand. We should be focussing on the capabilities – processes, network configuration, manufacturing capabilities, systems – that allow us to respond to true customer demand.

We understand that demand is stochastic (the opposite of deterministic: http://en.wikipedia.org/wiki/Stochastic) and determine optimal levels of inventory to meet a target service level based on predicting the probability of demand and also understanding that supply is also variable in terms of new buy and repair leadtimes and yields. Aerospace, high tech and capital equipment companies with a significant aftermarket business must turn to a solution like ours that deals with the uncertainty inherent in the aftermarket supply chain.

Trevor, I agree that Lean is not a cure all in this environment. As we note in our blog (http://blog.mcasolutions.com/Blog/bid/24059/Lean-and-Inventory-Optimization-in-the-Service-Supply-Chain) Lean is valuable in aftermarket service in reducing process variability, but variability cannot be eliminated, which means that very intelligent buffers and planning are required to meet customer expectations in this environment.

Tim
SVP Marketing, MCA Solutions