When one says “forecasting,” most people tend t o think about algorithms.
Indeed. in some instances algorithms can be used t o forecast. However, fore- castzng zs a process rather than an algorithm or a set of algorithms. Algorithms are just part of the broader process that consists of various phases presented in the following sections.
Analysis o f decision-making processes. The first step of a forecasting process is to analyze the decision making process one wants t o support. This sets the basic output of the forecasting process (definition of product. time bucket, and market demand refers to. and choice of forecasting horizon(s) and fre- quency of updates). It is actually fairly hard to prescribe how this task shall be performed. However, we have t o realize that any mistake in this initial phase has substantial consequences. A guiding principle is t o look at the in- formation one needs t o make decisions and make sure that the forecasting process provides it.
If the forecasting process is too detailed. the output is too inaccurate (see previous section). On the other hand, if the forecasting process is too aggre- gate the output is generic and hardly helps the decision maker. For example.
consider company Gamma from example 3.4. Probably, aggregate figures on consumption of paper in the USA are hardly the input that inventory planners expect in order t o decide how many reams of paper should be sent to store 346 tomorrow.
Gathering information. This is the second phase of the forecasting process.
Once the output of the forecasting process is properly defined. we shall in- vestigate what pieces of information are available t o generate it. Forecasting, like any other statistic, is conditioned upon (i.e.. depends on) a n information set. In other words, the quality of the final forecast depends, among other things. on the quality and quantity of data and information used to generate such a forecast. Thus finding the right set of information to forecast demand can be as important or even more important than the selection of the appro- priate forecasting algorithm. Indeed, even the best algorithm cannot possibly operate successfully without key pieces of information.
Example 3.6 Figure 3.1 shows the demand pattern of a food product in a large Italian grocery chain. The graph shows wide variations as demand jumps from 10 to 240. The root cause of such bumps are trade promotzons.
It is rather apparent that the manufacturer of this product cannot possi- bly forecast demand accurately with no information on trade promotions, no matter what the forecasting algorithm is. Indeed, there is no clear pattern in promotions and thus an algorithm cannot predict when they will occur in the future and forecast their impact on demand. However. the retailer and the manufacturer agree on the promotions well in advance of their start. Indeed.
both the retailer and the manufacturer enjoy the beneficial increase of demand.
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 s $ 65 F 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125
v1
1 6 II I 0 21 26 II 36 41 46 51 56 S 61 (D g 66 In 71 16 81 86 91 96 101 106 Ill 116 121
N 0 0
-
"1 0-
0 0 "I 0 0THE VARlABLE TO BE PREDlCTED 97
Thus the retailer asks the manufacturer to cut the wholesale price (i.e., the price the manufacturer charges the retailer) temporarily. So the manufac- turing company knows when the promotions are going t o occur few a weeks before they start. The manufacturer needs to make this precious information available to the forecasters. Unfortunately, those that collect the information from the retailers typically belong to the sales departments. while the per- sons in charge of forecasting belong t o other departments (e.g.% logistics or manufacturing), and in many companies information does not flow smoothly across departmental boundaries. The benefits of such an information can be
appreciated by looking a t Figure 3.2. I]
The key pieces of information to predict future demand depend on the specific forecasting problem one faces. Thus we cannot provide an exhaustive list of variables one might want t o consider. However, we can discuss issues and variables that are often overlooked and do require some careful attention.
Forecasting tries to predict the future behavior of an exogenous variable. in our case, future demand.4 Hence. it is very important t o use demand rather than sales as the input to the forecasting process. Actually. sales depend on true customer demand (that is a truly exogenous variable one tries to predict) and on the availability of products (that is a lever for the supply chain manager). Product availability censors demand. In most situations a company can only sell the products that are currently available in the warehouse or in the store. When 30 cans of beer are available in a supermarket. we cannot sell more than 30 cans. If sales are used to forecast future demand, a low demand forecast might turn out t o be a self-fulfilling prophecy. Low sales might reduce the forecast. which then leads planners t o reduce inventories.
Finally, low inventories might further reduce sales5
Example 3.7 For example, a leader in the production of dry pasta in Italy uses time-series models (see section 3.5 in this chapter). When a new kind of pasta was launched. the company decided to postpone the launch in a given region because the company wanted t o consume inventories of a preexisting item that the new one was going t o cannibalize. The automatic forecasting and replenishment system immediately started t o record zero sales for the new product in that region, thus predicting no demand and suggesting to ship zero units of the new kind of pasta. The vicious circle was interrupted only when the product manager spotted the anomaly in sales. investigated the issue. and
finally discovered what was going on.
0
‘Please note t h a t demand is not completely exogenous for a company, since many levers such as price can influence it. However, in our context we can assume demand to b e exogenous, as logistics and supply chain managers are supposed t o meet demand. In other functions such as marketing and sales. demand is actually the variable t h a t one tries t o control through pricing, promotions. new products. etc.
j N o t e t h a t this process might be very dangerous in case of products with low margin, as companies tend t o provide low service levels and a relatively large portion of demand can be lost (see section 5.2).
THE VARIABLE TO BE PREDlCTED 101
Moreover, a stockout of a given product can perturb the sales pattern of other products since some customers might be willing t o substitute the product they were looking for with a surrogate. In some industries such as business to business. e-commerce, or catalogue sales. it is relatively easy to capture the gap between sales and demand as one can keep track of customers orders In other instances. like '.brick and mortar" retail chains. this is more complex.
as customers do not formally place orders. In this case too, though. one can use statistics to estimate the potential customer demand out of censored sales data (ainong others see [l5] and [20]).
Analysis o f demand. The third phase of the process is the analysis of demand.
In this phase one shall study and identify demand patterns. As we further discuss in the next sect,ions, all quantitative forecasting techniques make some assumptions on demand behavior and pattern. Thus one should first analyze demand t o figure out its actual behavior and then look for a forecasting tech- nique that fits it. For example, we might investigate the demand to check whether it is stationary. it shows seasonal fluctuations, or it is influenced by phenomena such as weather conditions, promotions. or fashion. S!'7e should understand first the drivers of demand. and then we can design (or choose) an appropriate forecasting model that, is able t o read past demand behavior and predict the fut,ure one.
Selection of forecasting technique and fine tuning o f parameters. The fourth phase of the process consists of (i) the select,ion of t,he appropriate forecasting model and (ii) the fine tuning of its paramet,ers. In simple cases, one can just, select a forecasting niodel off the shelf, i.e., adopt an existing model as it fits very well. Commercial software provides several standard forecasting techniques t o choose from.6 Very often, though, real-life problems require more complex or at t,he least ..ad hoc" solutions. This is the reason why one shall fully understand assumpt,ions, mechanics. and applicability of standard forecasting techniques. If one does not fully understand the details of st,aridard techniques. he/she is bound to use them as they are and cariiiot adapt them to the unique features of any given demand. Sforeover, tlie effectiveness of inany models depends on the selection of proper values of the parameters,
Usually. forecasters judge the quality of a model or a set of parameters by looking at their ability t,o generate small errors. In the next sect,ion vie discuss several metrics for forecasting errors. Notice that the selection of a model (or set of parameters) should be based on its ability t,o forccast future demand. Unfortunat,ely, future demand is not, known yet. This makes tlie selection of the "best," model tricky. Often one looks at what u-odd 1iaT.e been the performance of the forecasting niodel (or set of parameters), had it been used in the past. This is typically the only way out,. but, we are implicitly
6For a list of software providers see www. forecastingeducation. corn
assuming t ha t the basic demand pattern will not change: The best model to predict past demand will still be the best model for future demand as well. In case we expect a significant change in demand - say we expect a stationary demand to start growing - this approach might lead us to poor performance.
In these cases, we might want to select a model simply because it logically fits the demand pattern we expect to observe in the future.
Forecast generation Once the model is selected and parameters are set. we can start using them to generate demand forecasts. During this phase. d a t a are processed and forecasts are used to make decisions.
Measuring forecasting errors While we continuously generate demand fore- casts. we shall keep track of errors. By doing so. one can spot any inconsis- tency between the model and current demand behavior. which in real contexts is dynamic and thus requires periodic tunings. Moreover, the quality of fore- casts is a relevant input for the distribution and production planning process.
As chapters 5 and 6 discuss in detail. uncertainty (as measured by forecasting error) changes the very nature of decision-making and planning problems. Un- der uncertain conditions we shall deliberately acknowledge that very different scenarios might come true. Also. forecasting errors can be used to judge the quality of a forecasters’ job and, through appropriate incentives. lead him/her t o improve it over time.
Often this phase of the forecasting process is overlooked. The basic logic is tha t right or wrong. the story is over once we have observed demand. Many companies do not record forecasts in their systems. They simply record the purchase, production, or distribution plans. Some companies think that if 200 units were manufactured and 200 units were sold. the forecast quality was good. This simplistic vision overlooks a basic difference between a forecast and a plan. The forecast is the expectation of the future behavior of a variable which is a t least partially exogenous. A plan is the response the company believes to be optimal in the face of all possible future levels of demand.
Thus the demand forecast and the plans t o meet it are logically very different and should be treated as such. As we discuss in further detail in chapter 5 . producing 100 units while we expect a demand for 100 units can be a very bad decision. though an apparently reasonable one.
Also, even when forecasts are recorded, they are often overwritten as they are updated. Thus, only the most recent, and usually most accurate. forecasts are left in the databases. The following example shall make the concept clearer.
Example 3.8 Let us assume th at a company forecasts demand and plans inventories with a monthly time bucket. Also, let us assume that the company forecasts and plans 12 months into the future with a rolling horizon. i.e..
every month it forecasts demand and plans inventories for each of the next 12 months. At the end of year 2006 the company updates forecasts for January-
METRlCS FOR FORECAST ERRORS 103 November 2007 and creates a brand new one for the month of December 2007.
The forecast for December is going t o be updated in January 2007. February 2007. and so on. Often companies tend to overwrite the original forecast for December 2007 with more recent ones. Thus in databases we tend to find forecasts with very short horizons and thus relatively small errors. This often leads companies to overestimate their ability to forecast demand and underestimate the uncertainties they face. For example, consider a company that wants to forecast the total turnover for a fiscal year and during the year constantly keeps on updating the forecast to get an accurate figure. By the end of the year the figure is going to get very accurate by definition. as we are basically looking back a t past sales rather than predicting future ones.