Forecasting: Science or Art?

Demand forecasting is the holy grail of inventory control.  The value of demand forecasting is almost axiomatic – it doesn’t really require a complex modeling exercise to prove it.  However, corporate America is filled with stories about the forecasting pitfalls and problems.  While there have been great discussions about various algorithms used to forecast demand, let’s take a fresh look at forecasting.  Is forecasting science (defined algorithms) or art (human judgment and intuition)?

Forecasting algorithms are actually very well known mathematically, and it seems that graduate students in Operations Research (an area of applied mathematics) are coming up with new ones on a regular basis.  While a time-series type approach may work for established products, other algorithms would be needed where there is little historical information or where the forecasted event is intermittent. 

While algorithm performance varies depending on the situation  (for example, exponential smoothing and double exponential smoothing work differently), any of them will outperform simply allowing the ordering process to run on autopilot.  The mathematics behind these algorithms are quite elegant and provably correct.  However, given that is the case, why did Nike has such a spectacular demand forecasting failure in 2004?

Science Meets Art

In the case of Nike, court documents demonstrated clearly that Nike relied too much on software to do the forecasting, rather than on people who were clearly part of the supply chain process.  As one practitioner put it, you can always assume that the initial forecast will be wrong, especially if the algorithm is being fed bad or out-of-date data.  So, let’s look at a couple of ways that forecast accuracy can improve.

The law of large numbers basically says that the more you have of something, the more accurate are your forecasts.  Life insurance companies are so profitable because they have large pools of people, segmented by demographic and health factors, and can with a high degree of reliability predict how many will die in a given period.  Most insurance companies exclude acts of war because that is something they just can’t model in their forecasts.

So, the first step is to aggregate the demand into batches large enough to increase the forecast accuracy.  For example, let’s say there is a new product launch nationwide, and enough product needs to be ordered to meet demand.  What are some steps marketing can take?

There are factors which include seasonality, point in the product lifecycle, weather, and any number of other data.  It is difficult to provide an exhaustive list, but the point is that forecast accuracy improves as factors are introduced which more closely models reality.

Art Meets Organization

Aggregate demand is made up of segmented demand, and it’s a judgment call at to which customer segments make sense for a particular forecast.  Marketing will have already done research on the expected market for a new product, and the customer segmentation they expect to materialize in the marketplace. 

Sales will have a big say in whether the forecast is reasonable, since they will have to sign up for the number.  If marketing’s forecast says there is demand for 1,000 shirts, sales needs to commit to finding the demand and selling into it.  They will look at some of the same data as marketing, but will also work their sales channels to see if it can move that much merchandise. 

Once sales is done with it, finance and operations will need to weigh in to make sure they can finance the inventory and the infrastructure exists to move that many shirts.  A trade off is possible between the addressable market, the available market, and corporate resources.

As the foregoing implies, there is a process for getting to a forecast that ultimately turns into a sales order.  The technology and original forecast was balanced against the organization and what it could accomplish.  It is a balancing act, as has been discussed elsewhere. 

Placing the Factory Order

All of this activity leads to the factory order.  Once the order is placed, the challenge shifts from inventory size to inventory management.  That will be a discussion for another time. 

So, is forecasting an art or a science?  Every forecasting situation is different because each product or service is different.  Having said all that, forecasting is really art buttressed by science.  Put in place processes and so forth to help the people use the science to generate good forecasts.  The payoff is worth the effort.

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CRM: Building the Business Case

Customer Relationship Management (CRM) seems to have a somewhat checkered history of delivering the expected value.  Sometimes, the reasons for failure are the same as those for other IT projects – project management, budget issues, etc…  The issue I’d like to address here is expectations as it relates to the results of a CRM initiative. 

Early in the project, the sales or internal support team will begin to formulate a selling proposition (internal teams need to be good sales people too), which will include a business case for the project.  Often, either the business case is weak (“You will experience 5-10% improvement in operations”), or it is overly-optimistic (“You will have a 15% increase in passenger traffic on your airline”).  Both lack credibility.  Being credible requires a clear line between the expected benefits and project-linked improvements.

So, here are some suggestions that will help create stronger business cases that are compelling and set the foundation for success.

• Numbers.  The most believable aspects of a business case are always the numbers.  “Improving” and “improving by 7%” are very different.  Anyone can put a number on a PowerPoint slide, but fewer can back those numbers up with a detailed analysis. 


• Story.  The story associated with the numbers are probably more important than the numbers themselves.  Mark Twain said, "Figures don't lie, but liars figure."  That truth is not lost on clients, so the story must be credible.  What makes a story credible?  Read on.


• Integrity.  At some point, you’ve gotten good data to create a credible set of numbers, and you’ve put together a story about those numbers.  It now comes down to you – are you credible?  You must believe your own story, and you must be fluent with its presentation.  That flows from your own integrity.

These steps are not linear but rather occur simultaneously.  Your story will guide your search for relevant statistics, and those same statistic will guide the development of your story.  The project itself will put you in a box (e.g. project constraints), which in turn influences the set of numbers required.

For example, in a given customer engagement, I needed to put together a cost-savings business case based on an application change.  The first choice was deciding how to explain the business case.  Do you try doing a comparative analysis between the incumbent and new software solutions?  The problem with that approach is fans of either package start to compete with each other and it blunts focus.  Thus, the first rule is: keep the story simple.  I chose to focus on the relative development and operational costs.

Next, gather as much relevant data as possible.  Especially early in the cycle, getting reliable data is often difficult.  Clean data is great but often hard to produce.  Get as much clean data as possible. 

How many points of value should you create in your narrative?  The answer brings us to the second rule: compelling is better than exhaustive.  Demonstrating 80% of the value create is not 100%, but if telling the story with 80% only requires five value points, versus 25 for 100%, I’ll take the five every time.  The story becomes compelling when it becomes easily comprehensible. 

Finally, sequence the story as a series of reveals and get to your point quickly.  While you still have to step through the story, don’t take a long time to get there.  If you’re using PowerPoint, a few slides should be sufficient. 

There are a lot of “it depends” factors in this analysis: size of the investment required, audience, criticality of the benefits, etc…  Getting the numbers, crafting and presenting the story, and then acting with integrity are key to being believable and setting a realistic expectation early in the project.  If the project starts well, it will end well.  By the way, in case you were wondering, the story I told earlier resulted in the proposed solution displacing a good incumbent provider.  That outcome was achieved because the client believed the business case.

Optimizing Transportation Logistics

Optimization is an oft-used word, that has lost its meaning.  If all the trucks in a fleet are assigned, some would say that the schedule has been optimized.  In a sense, they’re right when they say it has been optimized, since there are degrees of optimization. 

However, the industry is being buffeted by the twin challenges of stubbornly high fuel prices and increasing government regulations of drivers, traffic, and engine emissions.  While a few tenths of a mile per gallon changes in engine efficiency don’t seem like much to policymakers, in fact that translates into a significant burden on the transportation sector.   

The trucking industry spent $111B on fuel in 2007, and about $135B in 2008.  Given the increase in regulation expected from Washington, increased inflation, and a slow economy means fleet owners will be squeezed as those fuel costs can only increase. 

While these external factors cannot be ignored, there is a way to make sure you’re wringing everything out of every ounce of fuel.  The best optimization must be done with software that uses advanced mathematics.  There are packaged software products from vendors such as I2, Manugistics, and Manhattan that do true optimization (by the way, this is not an exhaustive list).  While each situation varies, 3-8% savings is achievable.  That translates into roughly $4B to $10B in savings across the industry. 

However, implementing these applications takes executive commitment and determination.  Implementations can take several months of tuning after the implementation and integration into an existing operation.  There are guidelines for optimization that will help keep your effort going forward.  Just understand that these efforts can’t just be installed and forgotten. 

There is another topic that is very important to understand, and that is the issue of business rules.  The basic rule is that for every business rule (a.k.a. constraint) you introduce, you incur a cost.  One client had a business rule that would allow drivers to choose the load they would carry for the first load of their trip.  Given that it was how their business operated, the rule was introduced.  However, it introduced 0.5-1% in additional operating costs. 

The other thing to remember is that you need to model all your business rules.  If you have operational rules that you don’t model in the system, your dispatchers will constantly be overriding the system because it does not do what they expect.  While some of that is a training issue, it could also be a mismatch between the way your business operates and how your operation was modeled in the system. 

Transportation optimization is difficult work.  Manual systems are inadequate, and automated systems require expert guidance.  However, the effort is worth it in reduced operating costs and happier drivers.

Value of Integration and the Complex Sale

How can you sustain an integrated, unique value proposition during a complex sale, especially when the RFP process by design is commoditized?  For example, suppose you are selling call center services.  Call centers are often viewed as commoditized services, where basic capabilities are assumed and customers evaluate vendors primarily on cost.  So, you receive an RFP that asks for 100 seats of English language for customer care and that there is no restriction on where you can source the labor.  Well, what do you do?

In a sense, it’s a trick question.  At this stage in the process, you should get ready to accept a commodity-type deal.  Creating an integrated value proposition on a complex sale happens before the RFP has been issued.  After all, an RFP is the embodiment of manifest demand.  Somebody has already decided on the “complete” solution and approach, and all they need now is to procure specific capabilities to realize the overall vision.  While most RFPs include a “Value Add” section, that at best gives your proposal a few points in the evaluation criteria but is not decisive. 

Let’s assume you did not make that mistake, you’ve engaged early, and have established an integrated value proposition.  To return to our earlier example, let’s assume that the 100 seat customer care call center is part of an ordering business process which includes e-commerce as well as order fulfillment.  You have clearly established the value of integration.  You have crafted a compelling top-level value proposition.  What’s next?

At this stage, the procurement team will get involved.  They will work with a department who has a budget who does not care about an integrated value proposition (which by definition extends across multiple departments).  There may a master budget, but getting involved with that process could make a complex sale even more complex.  How can you sustain the top-level value proposition while you sell at the department level?

First of all, someone within the organization needs to own the top-level value proposition.  There is added complexity for this type of sale, where you must sell at two levels: the upper part of the organization that owns the top-level value proposition and the department that owns the budget for the current procurement activity (such as the call center).  The scoring criteria should be adjusted to reflect that integrated value is a key evaluation criteria.  

Selling in this way isn’t easy, but it is a trend in the market and it does help you create stronger value propositions and create higher-margin deals.  Plus, it raises the likelihood that you will win.

Creating Value in the Value Chain

As companies have sought to reduce costs or increase revenues, service suppliers have focused on their part of the value chain to increase their offering's inherent value.  Business Process Outsourcing (BPO) vendors have made their particular contributions through becoming very efficient, whether it be 3PL order fulfillment suppliers, Finance and Accounting (F&A) providers, or others.  Off-shoring is one way these companies have become efficient through delivering labor arbitrage savings.  Further, these offshore service providers have become very good at breaking down processes and then taking over those activities that can be done cheaply and effectively in their service factories.

Software vendors have taken a different tack.  While there are still many single-function software offerings, increasingly attacking more parts of a given business process can be served by one or more integrated offerings.  Siebel has done very well by taking a number of service chain functions and integrating them together, such as warranty and customer relationship management.  While there are still pure-play warranty software products, customers are starting to feel the value of integration that a product such as Siebel offers is greater than the value of a series of best-of-breed solutions.

Here is the real question - if that is the case, have we as an industry arrived at the point where we can only expect incremental overall improvements with the lion's share of the expected benefits realized through integrated software solutions?  While software products yield great advantages and need to be part of an overall solution, my answer to how increased value is realized is not just a particular piece of software, but rather how that software can become part of a mosaic of platforms, software, and BPO solutions focused on a business-focused outcome.

Let's take the perspective of our customers for a moment.  If we as service providers implement the exact same solution for each of our customers, we have by definition removed one lever they could use to create unique competitive advantage.  Ever increasing complexity in software solutions tends to move our customers toward the same end state and same value equation.   Put another way, our customers will invest a lot of money to gain an advantage on their competitors, only to find that their investment has only allowed them to keep pace.

Service providers need to take the time to craft a unique solution for each customer based on the building blocks available through standardized offerings.  That is the "secret sauce" that successful vendors will bring to their customers.  Success will be measured through improved performance of our customers, and more business for service providers.