Effective demand management is critical to the financial performance and health of an organization. Demand management is boundary spanning and, as such, needs to be independent of the functional management organization. When we refer to demand management, we are referring to every functional group within the organization contributing to and relying on managing demand.
The traditional organizational structure is vertical and groups functions in a way that creates the very silos that serve as a constraint to optimizing performance. Breaking down the silos is impossible, and Ive written about it in my book, Supply Chain Transformation: Practical Roadmap to Best Practice Results (Wiley, 2012). Sales wont be reporting to Purchasing anytime soon, for example, although accurate demand forecasts are critical for both functions.
Statistical forecasts are based on historical data, and are not representative of the existing and future conditions (causal factors) that influence demand. The departments responsible for creating, marketing, and selling, do everything they can to change history. Therefore, the statistical forecast is only useful when determining a basis for segmentation, analyzing patterns, and creating baseline forecasts. As a result, statistical forecasts about stock keeping units (SKUs) at the location level, for example, will always be inaccurate. To improve accuracy, organizations need to study the causal factors that are likely to increase or decrease demand, across the business, to determine and manage variability from baseline demand, for each item, at each location.
A Case for Digital Demand Management
Digitizing your business will give you the data you need to sense, shape, and respond to demand changes in real time. This will also allow you to apply scientific principles and derive more accurate demand forecasts. Going digital doesnt break down silos, but it does help to horizontally integrate your organization by linking vertical functional activities with horizontal processes. Digitization also facilitates connected collaboration and helps apply process control techniques to manage and improve demand forecasts by determining an acceptable upper and lower range of flowpath variability from forecast.
Traditional sales and operations planning (S&OP) and integrated business planning (IBP) processes are too fragmented and unable to foster the execution level internal and external collaboration needed by modern businesses. As a result, theyre unable to execute the new digital demand management processes required to support and develop better supply chains and more mature analytics capabilities.
Using Data to Turbocharge Analytics and Forecasts
The connected age calls for a new corporate demand program management function. One that can effectively tap into the new digital sources of data from external partners and from various fragmented functions, apply advanced predictive and prescriptive analytics methods, and provide more accurate, actionable demand forecasts.
Why is this a competitive imperative? Analytics, especially machine learning, are strongly rooted in historical data. If you havent collected the data to compile a historical basis for predictive and prescriptive analytics, you are behind. The time lost is a hurdle you simply cant leap over, and catching up will take at least a year or two.
I will be speaking about Digital Demand Management: Taming the Bullwhip Effect at the JDA Focus Conference in Las Vegas, April 25-27, 2017. See me there if you want to learn more about how TCS and JDA work together to deliver digital demand management capabilities to organizations like yours.