Demand forecasts drive manufacturing processes and planning, such as for raw materials orders, production schedules, and human and machine capacity planning. If your demand planning system for manufacturers is inaccurate, your production cycle and supply chain execution will also be off.
Product and service demands are increasingly hard to predict, and buyers wield unprecedented knowledge and control through digital media and other mechanisms. Seasonality, halo, and cannibalization effects only exacerbate the challenge. To compete, manufacturers must become more nimble and customer-focused.
Other challenges include:
- New product introductions (hard to predict adoption)
- Lengthy lead times for raw materials
- Difficulty integrating time-series forecasting with workforce knowledge
- Disparate and unintegrated data
- Spreadsheet limitations
Manual spreadsheet modeling can not handle today’s manufacturing process challenges. You end up with a bunch of different forecast versions, all with their own unique and untraceable errors. Also, you overspend on labor hours dedicated to formatting documents, or manual what-if analyses. You’re not integrating Marketing, Procurement, Demand Planning, and Finance activities, which is the point of Integrated Business Planning (IBP).
With the right cloud-based planning suite, manufacturing organizations can gain a consolidated view of demand and supply across facilities, product families, and lines of business. This vantage point takes into account promotions, advertising, new product introductions, seasonality, competitor actions, and all of the other drivers that shape both short- and long-term forecasts. Every team in the organization sees and contributes to forecasts and plans on a single, unified platform. They model scenarios, review projections, discuss what-ifs, and align goals with expectations. The returns are dramatic: increased revenues, profitability, and customer satisfaction, all while reducing safety stock and other inventory-related expenses.
Application capabilities should include:
- Advanced-analytic forecasting algorithms: Identify patterns, seasonality, anomalies, correlations, and more.
- Cognitive method-selection technology: Automatically chooses optimal forecast methods and parameters, increasing accuracy and letting business users spend more time on results.
- Adjustments and overrides: Let Sales and other business users layer in promotions, ad campaigns, and other business activities or events to help shape or refine forecasts.
- Automated new product introductions: Support for cannibalization, supersession, comparable forecasting, new store openings, and group forecasting.