20 Sales and operations planning
We treat forecasting as a predominantly statistical exercise for most of this book. However, forecasting is as much an organizational exercise as a statistical one. Information is spread throughout the organization. The forecast has many stakeholders that require it as input to their planning processes. Thus, understanding demand forecasting requires understanding the statistical methods used to produce a forecast as well as how an organization creates a forecast and uses it for decision-making.
20.1 Forecasting organization
Managing this process is the realm of Sales & Operations Planning (S&OP). Since S&OP has been around for a while, best practices for such processes are now established (e.g., Lapide, 2014). There is an input and an output side to this process. On the input side, a good S&OP process supports sharing relevant information about the demand forecast. The emphasis here is on marketing and sales to share upcoming product launches, acquisition of new customers, planned promotions, and similar information with those responsible for forecasting. At the same time, other functions, such as operations, need to share relevant input data in the planning process, such as the inventory position, capacity available, etc. On the output side are coordinated plans based on the same input. Marketing develops a plan for promotions and demand management. Operations develops production and procurement plans. Finance develops cash flow plans and uses numbers coordinated with the other department to communicate with investors. Human resources develops a personnel plan based on the same forecast data.
Effective S&OP can coordinate the entire organization. Decision-makers will base their plans on all available information. If this process does not go well, functional areas hoard information, individual members influence forecasts, and the organization’s functions lack coordination. The result will be highly inaccurate forecasts and, consequently, promotions without sufficient capacity and predictions shared with investors that bear little resemblance to actual plans, thus damaging the firm’s credibility.
An S&OP process is a monthly process within an organization to enhance information sharing and coordinate plans. It usually involves a cross-functional team from marketing, operations, finance, sometimes human resources, and the dedicated forecasting team, if it exists. There are typically five different steps in an S&OP process. Beginning with Data Gathering, representatives from the various functions share relevant information and develop a standard set of business assumptions that goes into the forecast. In the actual Demand Planning stage, the team finalizes promotion and pricing decisions and agrees on a consensus forecast. Sales uses the consensus forecast to develop sales targets. Afterward, operations prepares inventory, production, capacity, and procurement decisions based on the consensus forecast during the Supply Planning stage.
Further, if the firm expects shortages, planners develop rationing and prioritization policies; they may also consider significant risk events, develop contingencies and share them with all team members. Finally, in the Pre-Executive Meeting, senior management can adjust any outcomes of the S&OP process. The finance function sometimes has veto power at this step to enable better cash flow planning and investor communications. Finally, top management discusses and finalizes all relevant plans during the Executive Meeting.
20.2 Organizational barriers
We must overcome two essential barriers to make this process work: incentives and organizational boundaries.
The first barrier stems from the fact that incentives across different functions are not aligned (see Section 16.3), and often no one is accountable for the quality of forecasts. Incentives for marketing or sales managers can lead them to lowball the forecast since they understand that their targets are often set depending on the forecast. Thus, lowering the forecast is an easy way for them to make their targets more obtainable. Or they may be incentivized to inflate the forecast since they understand this will push operations to create more inventory. More inventory decreases the chances of a stock-out happening and increases sales-related bonuses.
Operations managers are often compensated based on costs; one way to keep less inventory is to lowball the forecast and, thereby, lower production volumes. Finance will also chip in here since they will use the forecast to manage investor expectations. Decision-making and forecasting become mixed up. The forecast no longer coordinates activities but is a toy of organizational politics.
The second barrier is a result of different functional backgrounds and social identities. For example, people in marketing may have studied other topics than people in operations; they may have also had a different entry route into the organization. These experiences shape their way of perceiving and communicating organizational realities. Such unique thought worlds lead to challenges in managing a cross-functional team involving both groups.
Different groups may forecast in distinct units. Whereas finance forecasts in dollars of revenue, marketing may predict market shares, whereas operations is interested in product units. While we can convert these units, they represent a natural barrier to overcome. S&OP processes should standardize the conversion of these units.
Last but not least, having different functions always implies different social identities. Representatives from marketing will feel a natural allegiance to their function, as will the representatives from operations. While such social identification usually creates more trust within the group, it creates distrust between groups. Successfully managing a cross-functional S&OP team thus requires establishing standardized communication norms and breaking down functional barriers to build trust between the representatives of different functions.
One approach to overcome these barriers is to create a forecasting group that is organizationally separate from all other participating functions; this promotes accountability for forecast accuracy and a social identity focused on forecasting. It also creates professionalism and allows compensating people based on forecasting performance. A survey on forecasting practice found that 38% of responding organizations have introduced a separate group for forecasting, and 62% of those forecasting groups owned the forecasting process (McCarthy et al., 2006). We can compensate forecasters in such a group based on the accuracy of forecasts without creating asymmetry in their incentive systems; incentivizing forecasting in all other areas requires meticulous calibration to offset the existing incentives in these areas to over- or under-forecast (Scheele et al., 2017). If creating a separate forecasting function is impossible, the people participating in S&OP planning should de-emphasize their functional association. For example, it may be possible to remove the participating employees from their functional incentive systems and reward them according to firm performance or forecasting performance instead.
Another essential aspect of rational S&OP is to demystify the forecasts; all those involved in the forecasting process should be familiar with the data, software, and algorithms used in creating forecasts. Assumptions should be documented and transparent to everyone involved, and individuals should be held accountable for their judgment. As we emphasize in Chapter 16, in the long run, it is always possible to tell whether adjustments made to a statistical forecast were helpful or hurtful for forecasting performance through Forecast Value Added analysis.
An exciting avenue to resolve incentive issues in S&OP is to use past forecast accuracy of forecasts from different functional areas to determine the future weight given to these different functional forecasts when calculating a consensus. Under such a system, functions that bias their forecast will quickly lose their ability to influence the consensus forecast, incentivizing them to avoid biasing it.
A good case study of transforming an S&OP process is given by Oliva and Watson (2009). The authors study an electronics manufacturer that started with a dysfunctional forecasting process; the company had three different functions (Sales, Operations, and Finance), creating three different forecasts; the only information sharing happened in non-standardized spreadsheets and hallway conversations. The company proceeded to generate a process that started by (1) creating a separate group that was responsible for the statistical side of forecasting, (2) creating a common assumptions package where each function would contribute crucial information about the development of the business, (3) allowing the different functions to generate separate forecasts based on the same information, but then (4) integrating these forecasts using a weighted average, where the weight attributed to each function depended on past accuracy, and (5) limiting revisions to this initial consensus forecast to only those instances where actual data could be brought up to support any modifications to the forecast. The result was a stark increase in forecasting performance. Whereas company forecasts had an accuracy (1-MAPE) of only 50% before this re-design, this accuracy jumped to almost 90% after re-structuring the S&OP process.
Key takeaways
- Forecasting is as much a social and an organizational activity as a statistical one.
- Employees’ functional backgrounds may incentivize them to influence the forecast in ways that are not optimal for the business. Consider changing their incentives to align with getting an unbiased forecast.
- It may be very beneficial to separate forecasting out from traditional functional areas organizationally.
- Similarly, employees’ backgrounds may influence how they think and communicate about forecasts. Keep this in mind and work toward open communication.