Effective game plans lead to improved firm performance and bottom-line results. Metrics include reductions in stock-outs, delivery lead-time , missed shipments, partial shipments, and expediting efforts, and improvements in customer service. The lack of effective game plans is typically cited as a leading cause of poor system implementation. The following guidelines provide suggestions for improving the effectiveness of S&OP game plans.
Minimum Planning Horizon for Each Game Plan A saleable item s cumulative lead-time represents the minimum horizon for a game plan.
Reviewing and Updating Game Plans The process for reviewing and updating each game plan should be embedded into the firm s regularly scheduled management meetings focusing on demands and supply chain activities. An agreed-upon game plan reflects a balance of conflicting objectives related to various functional areas, such as sales, engineering, manufacturing, purchasing, and accounting.
Primary Responsibility for Maintaining Game Plans The person(s) acting as a master scheduler maintains the game plans and obtains management agreement. This role typically requires an in-depth understanding of sales and supply chain capabilities, as well as the political power to achieve agreed-upon game plans. The responsibility for providing sales order and forecast data typically belongs to the sales function, with a hand-off to the master scheduler.
Formulating Realistic Game Plans Realistic game plans require identification of capacity and material exceptions that would constrain the plans, and then eliminating the constraints or changing the plan. Identification of material-related exceptions typically starts with suggested actions on a planning worksheet, while capacity exceptions are identified using work center load analysis. In many cases, a realistic game plan must anticipate demands and demand variations via forecasts and inventory plans for stocked material.
Enforcing Near- Term Schedule Stability Near-term schedule stability provides one solution for resolving many conflicting objectives, such as improving competitive efficiencies in purchasing and production and reducing exceptions requiring expediting. It provides a stable target for coordinating supply chain activities and removes most alibis for missed schedules. Near-term schedule stability can benefit from inventory plans and realistic order promises about shipment dates. It involves a basic trade-off with objectives requiring fast response time and frequent schedule changes. The critical issue is that management recognizes the trade-offs to minimize near-term changes.
Making Realistic Sales Order Promises Realistic delivery promises represent the key link between sales commitments and supply chain activities. Delivery promises can be based on an item s existing inventory and scheduled receipts (via ATP logic), or on lead-times to purchase and/or manufacture the item (via CTP logic). The critical issue is to reduce and isolate the number of sales order exceptions requiring expediting. One solution approach involves splitting delivery across two sales order line items with different shipment dates.
Maintaining Valid Sales Order Shipment Dates Sales order shipment dates are used by planning calculations to communicate required supply chain activities. Changes in supply chain activities and/or demands sometimes require updates to indicate later shipment dates. In particular, past due shipment dates must be updated to reflect a current or future date.
Executing Supply Chain Activities to Plan Planning calculations make an underlying assumption that everyone works to plan, and the system provides coordination tools to communicate needed action. For example, it is assumed that procurement will ensure timely delivery of purchased material so that manufacturing can meet production schedules. It is assumed distribution will make on-time shipments because sales made valid delivery promises and procurement and production are working to plan. An unmanageable number of exceptions will impact this underlying assumption and the usefulness of coordination tools.
Reducing Exceptions Requiring Expediting The intent of near-term schedule stability, valid delivery promises and shipment dates, realistic game plans, and executing to plan is to reduce the number of exceptions to a manageable level. This improves the usefulness of coordination tools to meet the game plans.
The S&OP process translates business plans (expressed in dollars) into sales, production, and inventory plans ( expressed in units), and requires management information about planned and actual results for each game plan. Business analytics ”also termed business intelligence, data warehouses, and executive information systems ”provides one way to present this management information in dollars and units. The data reflects summarized information with drill-down to more detail. The results are presented in a variety of formats ” ranging from lists and tables to graphs and charts ”for financial and operational metrics. For example, the set of sales forecast data provides the basis for planned sales while shipment history defines actual sales. Business analytics can also highlight key performance indicators about operational metrics, such as on-time shipping percentages, production performance (about quality, delivery, and costs) and vendor performance.
Many firms require simulations to assess the impact of changing demands or supplies . Using multiple sets of forecast data to represent various scenarios, and a designated set of forecast data for planning calculation purposes, the management team can analyze the impact of changing demands on material and capacity requirements. The management team can also analyze the impact of using only sales order demand (and ignoring forecasted demand) by not designating a set of forecast data for planning calculation purposes.
One product line within the Batch Process company consists of many end-items built to order from a common manufactured item. The bill of material for each end-item identifies the common manufactured item and packaging components such as labels and bottles. In this case, the S&OP game plans for packaging components are expressed in terms of min-max quantities , while a component forecast drives production of the common manufactured item.
The Consumer Products company recently implemented a dedicated manufacturing cell for producing one product line. They wanted minimal reporting requirements and automatic generation of Kanban cards to coordinate production activities. Prior to cut-over for production in the new manufacturing cell on July 1, they used component date effectivities in the bills for each affected item to flatten the product structure. Routing information was only defined for the end-items, using a routing revision (with a July 1 start date) that defined run rates in the manufacturing cell . Purchased components were kept in floor stock bin locations with bin replenishment based on projected daily usage rates and a min-max reordering policy. Based on end-item demands and product structure information, planning calculations were customized to calculate projected daily usage rates for components and to generate Kanban cards for end-items and some intermediates. A customization provided an orderless approach to reporting end-item output. Only the item number and a completed quantity were reported , which then triggered auto- deduction of components from the floor stock bin locations.
The Distribution company sold several product lines to the commercial construction industry, where the products also required job-specific installation services. Each job involved multiple phases and tasks (with material and resource requirements) that were closely tied to progress on the construction site. For example, multiple steps were required for plumbing and electrical installation. Using the job functionality within Navision, and customizations to planning calculations and a job schedule board, the time-phased requirements for material and resources were synchronized with scheduled installation dates at the construction sites.
The Distribution company used statistical forecasting to calculate future sales demand in monthly increments based on historical data. This required historical data about previous years (prior to cutover to Microsoft Navision). In addition to shipments, the historical data included customer returns, credit memos, and selected inventory adjustments. Further refinements included the requested shipment date (to give a true picture of demand patterns) and information about sales of substitute items. Statistical forecast information was also needed to drive component forecasts for stocked components, where the historical data reflected item ledger entries about usage rather than shipments.
The Equipment company wanted to perform sales forecasting using a planning bill that specified mix percentages for equipment options. Building on the standardized functionality for make-to-order manufactured items, they modified planning calculations so that a sales forecast demand blew through the product structure for a make-to-order item and created component forecast demand for stocked components. The planning bill was also used during order entry as the basis for selecting options in a configuration. A further modification ensured the system automatically created an order-dependent bill with the selected options when the user generated production orders from the sales order. This approach resulted in matched sets of components, recognized planned changes in bills and routings, provided visibility of capacity requirements, and simplified the forecasting process (compared to entering individual component forecasts for stocked items).
The Equipment company produced a line of medical devices that required a manually maintained master schedule to reflect the planner s decision-making logic about production constraints. The medical devices required an expensive outside operation for sterilizing a batch of multiple end-items. The scheduling considerations included a cost-benefit analysis about amortizing the fixed fee for sterilization over the largest possible batch weight subject to a batch weight maximum while still building the product mix for customer demands and avoiding excess inventory. A manual schedule proved most effective for this case.
The Fabricated Products company often designed and built a one-time product to customer specifications. They used a simulated production order and order-dependent bills to define the product structure and to calculate estimated costs. This information was used to calculate a projected completion date based on forward scheduling logic. After receiving the sales order, this information was used to create a released production order and drive purchasing and production activities.
 See Maximizing Your ERP System for further explanation of planning bills (pp. 151 “156) and their use in forecasting (pp. 183 “187).
Chapter 8 provides further explanation about using a simulated production for one-time products.