Category Archives: Forecasts

“Demand Shaping” or “Demand Sensing”?

The EE Times ran a great article by Romit Dey and Manoj K. Singh last month on “Demand Shaping” and how it aligns customer trends with supply. But I have to ask, is it really “demand shaping” or is it more “demand sensing”. Is not “demand shaping” what marketing and advertising does? It’s true that supply chain has a supporting role, in terms of letting marketing know how much a product can be produced for, how many units can be produced, and how fast the units can be in consumers hands. However, what supply chain really does, in a company that runs like a well-oiled machine, is sense the demand that has been created, and the demand that is in flux, and adapts to the situation.

So what is “demand sensing”? According to the article, which calls it “demand shaping”, it is a demand-driven, supply-constraining customer-centric approach to planning and execution that aligns process with customer demand at strategic and tactical levels and with an organization’s capabilities which helps optimize use of resources, reducing excess inventory and improving inventory turns. More specifically, at the strategic level, the emphasis is on aligning customers’ long-term demand patterns to long-term resource and capacity constraints and at he tactical level, the focus is on understanding demand patterns and then influencing customers’ demand toward available supply, using the levers of price, promotion and products/services bundling.

How do you sense demand? As the article points out, you need three key capabilities:

  • demand pattern recognition
    who is buying what, when, and in what quantity
  • supply supportability analysis
    how much can be made, when, and how fast can it be delivered
  • optimal demand steering
    if demand patterns suddenly change, and you do not have enough of product A, can product B be used as a substitute and can customers be steered to that product instead

The first skill is obvious – you need to manage inventory appropriately so you aren’t holding too much, and generating excessive inventory carrying charges, or holding too little, and selling out before supply can be replenished. The second skill is less obvious, but easily understood – you need to know how much you can make, and how fast it can be made, to appropriately plan your inventory level.

The third skill is what takes “demand sensing” to a whole new level, to the point that it is almost “demand shaping”, but not quite, and hence the source of confusion. It is, as it’s called, “demand steering”. The Dell example the authors use is the best. By maintaining real-time visibility into its supply chains, Dell knows its inventory levels now and in the immediate future on an hourly basis. If a customer configures an order for a 60GB drive on their web-site, and Dell knows they don’t have enough stock to configure the system immediately, then Dell informs the user of a delayed ship date and presents the customer with an opportunity to replace it with an 80GB drive at a discount – steering the customer towards another product that can meet their needs, even if it is more expensive, but Dell takes a discount on margin to make the sale and keep the customer.

The key to success, as the article points out, is to make sure that all three processes are part of a single, integrated loop. A supply supportability analysis is run on a regular, automated, basis; inventory is updated on a near real-time basis; and short-term forecasts are updated at least daily. Each of these numbers is compared on an automated basis, and as soon as forecasts exceed inventory and obtainable supply, an alert is sent to a planner who determines whether there are alternative products that can be used to meet the need or if marketing and sales needs to be informed that they need to take actions to steer demand on their end. Then, customers are steered towards the alternative products through the appropriate channels – in real-time.

The article also does a good job at overviewing what is required for a demand sensing framework. The elements it outlines are:

  • inter and intra organizational connectivity
  • the ability to capture, structure, and comprehend data from customers and channels
  • advanced business intelligence to identify demand patterns
  • optimization
  • common processes
  • a common data model
  • common performance metrics
  • available-to-process capabilities
  • exception management
  • electronic negotiation and collaboration

The best thing about the framework is that these are basic capabilities and processes a good organization should already have in place. It’s just a matter of tying them together and using them wisely!

Forecasting, Part III

In Forecasting, Part I, I pointed out that accurate forecasting is a complex and challenging problem but that it is generally still possible to create good forecasts through the proper combination of judgmental and statistical methodologies. Specifically, manually adjusted statistical forecasts by an expert who has “inside” information, is aware of “one-time” events, and / or who is responsive to the latest environmental changes can often (dramatically) improve forecast accuracy, provided human bias does not creep in. (Thus, only practitioners with domain knowledge should adjust statistical forecasts using a structured process and only do so when there are known changes in the environment that the statistical model wasn’t really built to handle.)

Then in Forecasting, Part II, I pointed out an article in Purchasing that noted that when it comes to commodity forecasting, judgmental forecasts by experts have the best accuracy on record, demonstrating that expert human judgment applied to good statistical models with solid historical data that also take into account market intelligence and global economic trends are the way to go.

Now I am going to draw your attention to a recent white paper, sponsored by Supply Chain Consultants, by Tom Wallace and Bob Stahl titled Forecast Less and Get Better Results that demonstrates that the conventional wisdom that companies need to project forecasts and plans far into the future at a highly granular level is not necessarily right. Specifically, it points out that detailed forecasts and plans are normally only needed inside of what the authors call the Planning Time Fence or the point in the future when the cumulative lead time to acquire the material and build the product is only a short time away. They argue that outside of this planning time fence, you should only be concerned with aggregate volumes.

Specifically, they argue that up until it’s time to plan a production run, you should only be concerned about forecasting the aggregate volumes required for raw materials beyond the average planning time fence. After all, if you’re a large fast food chain, chances are you can predict with a fairly high degree of accuracy how many burger patties you are going to need over the next year, even if you can’t predict exactly how many Big Burgers, Bob Burgers, or Bo Burgers you are going to sell in any specific week. And if you are a toy manufacturer, chances are you can predict roughly how much plastic you are going to require over the next quarter, even if you don’t know precisely how many units of Dolly House or Trixie Truck you are going to be asked to manufacture. Attempting to forecast to a granular level too far in advance will just mean you’ll constantly be revising your forecasts and wasting time and resources, instead of focussing on what’s truly important for sourcing – the raw material aggregate volume, since that’s where your leverage is.

I’m sorry, but “I’m not omnipotent” just doesn’t cut it anymore!