The pitfalls of Demand Planning
Good demand planning is essential for the operational and financial performance of an organization. How do you avoid the most common pitfalls?
When we talk about demand planning, we refer to all functionalities within the organization that contribute to streamlining operations versus market demand.
The digitization of recent years seemed to deliver on the promise of breaking down silo formation and organizational barriers by reducing human interaction. Yet many companies struggle with these things to get their demand planning in order.
Pitfall 1. Lack of commitment.
In many organizations, a forecast is still viewed with the necessary skepticism. “the planning is not correct, because….” is a common view within companies. The result of this is that through actual human intervention the actual production is adjusted instead of improving the forecast, resulting in a self-fulfilling prophecy. At management and executive level there is often not enough guidance to avoid this pitfall.
There is also often a lack of commitment to break through organizational barriers. Demand planning often lingers at mid management level within a specific silo. At this level, one tries to align the different metrices, which causes the following problem. The bullwhip. By considering the supply chain as a sequence of metrices, variations are automatically reinforced and your demand planning quickly gets out of sync. Not infrequently, this is done within individual spreadsheets instead of an integrated demand planning system.
Pitfall 2. Past performance is not a guide to future performance.
Many statistical forecasts, regardless of their method, are based on historical developments. This goes wrong for 2 reasons. the previously mentioned internal bullwhip effect, but also the fact that historical data quickly becomes obsolete. Especially in a market that is constantly subject to rapid changes in demand.
It is precisely the challenge at S&OP to make predictions about current developments in the market through the smart use of Big Data. Statistical data can be used to research market segmentations and to create a baseline forecast. But at SKU level, for example, it is totally unsuitable.
Pitfall 3. Lack of cooperation
Developments that directly affect demand are not properly discussed or communicated within the organization beforehand. Consider, for example, a new marketing campaign or the introduction of new products in the market. For the Operation, a completely new situation arises that needs to be addressed ad hoc. The Operation cannot follow the change in demand and the desired result of the marketing action will not be realized as a result. The result is mutual reproaches within the organization.
The organizational structure is often the problem. Organized vertically with many functional silos. New ideas and concepts are developed within each silo without involving the rest of the organization from the start. People prefer to put a ready-made story on the table before consulting the organization.
Pitfall 4. Lack of mature forecast methods.
As stated earlier, historical data is a poor predictor of future developments in a rapidly changing market. Many organizations lack an incremental change in their forecast process. From historical to Prescriptive analyzes.
Historical data can be used for a first baseline of information and segregation of markets. Within this subdivision, organizations should take the following steps to avoid the pitfall.
Descriptive analysis or data management is perhaps the most essential step. Not only the collection and management of the data, but especially the method of recording and how the data is made accessible to the organization. [tagging, categories etc] Providing data with the correct characteristics and parameters is fundamental for the usability of the data.
Monitoring analysis. Once it has been determined which data you want to capture and manage, constant monitoring and updating of information is essential. Not only the data itself, but especially the changes in the data sets.
Diagnostic analysis. The collected data is analyzed for variations compared to base line data and compared with the expected developments and which deviations and to what extent can be determined.
Predictive analysis uses the diagnosis information to determine the new expected outcome. In short, what is the effect of a change in the schedule, changes in demand.
Prescriptive analysis then answers the question of which corrective actions should be taken. Scenario comparisons and other process control algorithms are used to achieve the best possible choice.
Pitfall 5. Lack of innovation.
Innovation is a sudden discovery. Innovation is an incremental process in which an organization takes a small step every day to improve itself. Equivalent to ecosystems that continuously try ‘new’ things to see if it works. This is precisely the strength of startups that make many attempts in a relatively short time to serve the market with a minimum of process control and hardly any overhead. This makes it possible to switch and adjust very quickly, if you have gambled on the wrong horse.
In this process, a 6S approach is often used to determine the maximum freedom of movement. Compared to the optimal flow, a UCL and LCL are established within which functions within the organizations are allowed to move freely. Responsibility, both operational and financial, has been fully delegated to the lowest possible level. If the upper or lower limit is exceeded, horizontal cooperation is required to return within the boundaries or to leave the chosen path.
It is important that the parties involved within these frameworks agree on the ranges in terms of performance, horizon of planning and risk, the shared risks and the exception conditions that are used.