In a demand-driven environment where the focus is on meeting customer expectations, accurate demand forecasting can be achieved when a collaborative process integrates various forecasting systems. By adding performance analytics to measure the iterative plan and understand trends, companies managing their supply chain can become even smarter about anticipating shifts in demand. The end result of improved forecast accuracy is reduced inventory costs, better customer service and improved fill rates.
Often finance, marketing, sales and production departments have separate forecasting methods, technologies and agendas. Finance focuses on internal cost control, marketing relies on external statistics, sales makes optimistic projections based on past orders and production tries to mediate the expectations of the other three while somehow regulating the supply chain.
The blunt truth is that any business forecasting demand in silos is just guessing. The price of inaccuracy is high: surplus is a wasted resource, and shortfall is a wasted opportunity. To survive, production must be accurately predicted at the stock-keeping unit (SKU) or line item level to allow for rapid response to demand fluctuations up or down within supply constraints.
The secret to collaborative demand forecasting lays in synchronizing systems and point of view. No single application is specifically designed for this purpose; the solution will be integrating existing systems to work in concert. Some systems may be in place, and some may need to be added. Here are the basics:
At the core of any forecasting system should be a data repository that captures information from enterprise systems, such as enterprise resource planning (ERP) and customer relationship management (CRM) packages. Analytic tools should sit atop the core and generate multidimensional performance scorecards for customers based on key performance indicators (KPIs) and business rules. Baseline production plans should be monitored and controlled with an exception management system, which provides performance metrics from many perspectives customers, channels, markets, products, cross-selling results, promotional effectiveness and external market trends. A rules-based system should alert operational teams to exceptions, provide answers as to root cause and point to adjustments. Finally, the entire solution, ideally, is based on a Web-based platform that securely extends the supply chain to include supplier and customer-distributor data.
The pursuit of return on (previous) investments (ROI) leads some supply-chain-centric businesses to lean too heavily on application suites or specialized solutions already installed. SCM, ERP, CRM and business intelligence (BI) all have excellent features and benefits when applied to the problems they were designed to solve. But none is built specifically for collaborative demand management. Any and/or all of these systems must be coordinated by a consensus methodology a process that brings finance, marketing, sales and production together to agree on a single forecast. The lynchpin here is an operational perspective each group must seek the best production numbers for meeting demand with the narrowest margin of error.
Recently research firm Gartner claimed: "Enterprises that collaboratively integrate disparate forecasting systems & will improve revenue predictability by 10 to 25 percent and decrease inventory carrying costs by more than 30 percent over a three-year period." Do the math for your own organization; a tool that can help you produce one agreed upon forecast can be a very lucrative investment.
