Part 5 of 6 in the “Rethinking Digital Business Planning” series
At this point in our series on digital business planning, it may be useful to review the ground covered thus far. Digital business planning should be seen as a holistic approach to supply chain planning that uses trusted data, process integration, automation, and collaboration to meet the demands of complexity in the digital economy.
The goal is to improve customer experiences and outcomes by bringing supply chain planning into the heart of the organization. The way forward requires companies to take control of their data and use it to speed planning cycles, facilitate quick interactions, improve responsiveness, and profitably serve diverse customer segments.
Foundational to digital business planning is the sales and operations planning (S&OP) process. As covered in the S&OP blog, digital business planning transforms a once insular process into one that touches all planning constituencies. Partners inside and outside the organization participate in the process based on a single source of trusted data and flexible collaboration.
Response and supply planning
Digital business planning sees response and supply planning as a vital extension of the S&OP process. Designed to help organizations respond more effectively to demand fluctuations and supply disruptions, it mixes multi-level, supply, and rough-cut planning with embedded analytics and exception management to better prioritize allocations based on promises to deliver and improve response management.
As explained in our last blog, digital business planning emphasizes multi-echelon inventory optimization as a way to plan more effectively across all distribution centers in a network. In addition, it urges the use of demand-driven MRP (DDMRP) with advanced analytics to develop better profiles of buffer stock positions throughout the enterprise. Together, these techniques help to reduce the risk of stock-outs while optimizing inventory carry costs.
Demand planning and sensing
In this blog, we add the notion of integrating demand planning and demand sensing into the larger planning process in order to deliver what customers want most. While demand planning involves traditional time-series methods that are based on historical trends and seasonal patterns, demand sensing brings live or recent data into the mix.
Together, these processes span the entire planning spectrum – from more strategic planning such as sales and operations planning to operational demand planning. Statistical models allow planners to develop accurate mid-term forecasts while demand sensing allows planners to react to near-term demand changes as they occur. Demand sensing leverages machine learning algorithms to identify patterns in the demand data and adjust the short-term forecast based on current demand signals.
Collaboration – and a platform to facilitate it – are critical. The statistical forecast is just a starting point. Planners need to work across groups such as marketing, sales, and supply chain in order to incorporate their insights to ensure an accurate forecast. Integration with backend systems for ERP and other functions helps facilitate this collaboration through easier sharing of critical data.
In the end, sales effectiveness is the goal. Digital business planning is a holistic means by which organizations can collaborate with teams throughout the enterprise to maximize sales effectiveness by removing bias from forecasts and creating plans that minimize inventory while ensuring the ability to meet customer demand.
For more information, see the IDC infobrief “Digital Business Planning is at the Heart of Supply Chain Transformation.”