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Companies use a variety of tools to predict where their business currently is and where it is headed. By “reading the tea leaves”, planners attempt to ensure correct stock levels, optimize inventory control and inventory value, and provide a framework for manning labor and operations.
But the complexity of modern-day supply chains means that forecasting and planning should be more than just reading the leaves. It must be a well-thought-out and dynamic system.
Many companies still utilize spreadsheets, disparate computer software, and a segmented and a siloed decision-making process for managing their supply chain. As a result, the critical understanding of how forecasting and demand planning should be used is clouded, making the process more difficult and less efficient.
Inefficient systems have often led to the terms forecasting and demand planning being used interchangeably. But despite being interrelated, the two are different processes. Grouping them together indicates a failure to understand that they are part of a structure that stretches vertically through an optimized planning system.
Another way to look at forecasting and demand planning is to understand where they fit in relation to business strategy. Forecasting is an important part of several variables used in demand planning. And demand planning is a holistic review of demand that is part of a company’s overall Sales and Operations Planning (S&OP).
Statistical forecasting has several disadvantages when used alone to predict demand. Firstly, even though it involves mathematical equations to parse historical data, that doesn’t necessarily mean the data it is parsing is enough to make the right decision. And it excludes other variables that need to be considered, some of which may be mathematical, and others that aren’t.
Secondly, it involves many open-ended assumptions. By using statistical forecasting alone, assumptions must be made that conditions will remain the same over time. In highly volatile markets this immediately makes using statistical analysis alone inadequate. But even in stable markets, the possibility of disruptions such as labor unrest, natural disasters, and events such as COVID-19 crisis has introduced uncertainty in the most stable markets as well. The examples, and others, touch on the variables that should be considered in demand planning – variables that statistical analysis cannot provide.
Finally, statistical planning is time-consuming and costly. Because the requirements to build the mathematical model involve a lot of data, time and effort must be spent to amass these data points. And even this can throw off the quality of the forecast if data points are missing or data input is wrong based on human errors.
To remove the disadvantages posed by statistical forecasting alone, companies can utilize a disciplined and collaborative demand planning system. This involves accurate statistical forecasts as well as quantitative analysis. It also includes identifying and systemizing external information such as:
While this sounds like a daunting list, it’s easier than ever to include these and other variables when implementing a demand planning system. Through the use of demand planning software, these variables, as well as the quality of the statistical forecast, can be operationalized under one platform.
By using demand planning software, companies can leverage a volume of data that could not be managed by human analysis alone. This allows them to aggregate data from many sources to improve forecasting accuracy. In demand planning software, data is easily visualized, and advanced algorithms can provide analysis of demand with a high level of accuracy, speed, and volume not possible before. This software includes tools such as ABC analysis, “what-if” scenario building, and inventory optimization using large data sets and near-real-time data inputs to deliver an accurate picture of demand. Software also builds collaboration by improving communication with un-siloed data that is accessible and actionable by those who need it.
Demand and supply planning software offered by Plex DemandCaster offers companies a comprehensive platform that moves demand planning from a reactive to a proactive footing. It allows them to move away from the time-consuming and spreadsheet-laden environment of statistical analysis and into a world where demand uncertainty is reduced using state of the art software to deliver accurate and reliable demand plans.