Managing Your Supply Chain Using Microsoft Axapta: A Book Excerpt Part Four: Guidelines and Case Studies

Featured Author - Dr. Scott Hamilton - March 26, 2004

Guidelines Concerning S&OP Game Plans

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. Metrics also include 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 sales and operations planning (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, and additional months provide visibility for purchasing and capacity planning purposes. An additional six to twelve month planning horizon is typically recommended. This minimum planning horizon should be reflected in the item's time fences, such as the coverage and forecast time fences.

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. Periodic revisions to game plans should be reflected in updated forecasts.

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. The primary responsibility for game plans should be reflected in the planner (and buyer) responsibility assigned to items.

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, 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. Finite scheduling can also contribute to a realistic game plan.

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. The freeze time fence represents one approach to support near-term schedule stability, since planned orders will not be placed in the frozen period.

Making and Maintaining Realistic Sales Order Promises. Realistic delivery promises represent the key link between sales commitments and supply chain activities. The system support delivery promises during order entry, and also through planning calculations that suggest delayed delivery based on projected completion dates. A critical issue is to reduce and isolate the number of sales order exceptions requiring expediting. One solution approach for meeting a delivery promise involves splitting delivery across two sales order line items with different shipment dates.

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 that Require 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.

This is Part Four of a four-part excerpt from the book Managing Your Supply Chain Using Microsoft Axapta: A Book Excerpt by Dr. Scott Hamilton.

The book can be ordered on amazon.com.

Part One began the discussion of Sales and Operations Planning.

Part Two detailed Understanding Planning Calculations.

Part Three will propose Guidelines and Case Studies.

Reprinted with permission from McGraw-Hill

Business Analytics and S&OP

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 known as business intelligence, data warehouses and executive information systems—provides one way to provide 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, and vendor performance.

Case Studies

Case #25: S&OP Simulations. The All-and-Anything company required simulations to assess the impact of changing demands and 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 could analyze the impact of changing demands on material and capacity requirements. For example, the planning calculations were first performed using infinite capacity planning to anticipate overloaded periods. After adjusting available capacity and consideration of alternate routings, the planning calculations were performed again using finite capacity and material to highlight unrealistic delivery dates.

Case #26: Aggregate Forecasts by Item Group. The All-and-Anything company had thousands of stocked end-items, and wanted to minimize the effort to maintain forecasts for individual items. Items were grouped together for forecasting purposes, with a mix percentage assigned to each item, so that aggregate forecasts could be entered for each group of items. This approach reduced the number of forecasts to be maintained, from thousands of individual items to a few dozen product families.

Case #27: Common Component Forecast for Process Manufacturing. One product line within the Batch Process Manufacturing firm consisted of many end-items built to order from a common manufactured item. The bill of material for each end-item identified the common manufactured item and packaging components such as labels and bottles. In this case, the S&OP game plans for packaging components were expressed in terms of min-max quantities, while a purchase forecast was defined for the common manufactured item.

Case #28: Kanban Coordination in Consumer Products Manufacturing. The Consumer Products company was using a dedicated manufacturing cell for producing one product line. The cell's daily production rate (representing the mix of all items produced by the cell) provided the basis for daily rates for end-items and components, which were then used to calculate Kanban quantities and automatically generate Kanban cards for manufactured items. Purchased components were kept in floor stock bin locations with bin replenishment based on electronic Kanbans to suppliers. They used an order-less approach to reporting production output with auto-deduction of component materials.

Case #29: S&OP Process Improvements. The Consumer Products company wanted to improve their sales and operations planning process using a collaborative planning tool. This tool enabled them to view past performance and time-phased projections of forecasts, bookings, orders, shipments, production plans and inventory levels. It supported what-if analyses in terms of unit volume, revenue and profits. It also provided structure to their S&OP process, such as tracking action items and measuring progress towards operational excellence.

Case #30: Statistical Forecasting. The Distribution company used statistical forecasting to calculate future sales demand in monthly increments based on historical data. This required historical data about previous months. 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. Statistical forecast information was also needed to drive purchase forecasts for stocked components, where the historical data reflected transaction history about usage rather than shipments.

Case #31: Planning Bills for Make-to-Order Custom Equipment. The Equipment company currently forecasts a group of stocked components for a configurable equipment item. Several customizations were being considered to extend the concept of planning bills. One customization involved adding a mix percentage field to each component in the bill of options for a configurable item. A second customization involved planning calculations so that forecasts for a configurable item would be exploded through the bill of options (and though the phantom, production and vendor component types) to create purchase forecasts for normal component types. This planning bill approach would simplify the forecasting process, recognize planned changes in bills and routings, provide visibility of capacity requirements, and result in matched sets of stocked components.

Case #32: Manual Master Scheduling for Medical Devices. 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.

Case #33: Manual Forecast Consumption and Make-to-Order Products. The Fabricated Products company produced several standard products (such as wire harnesses and printed circuit boards) on a contract manufacturing basis for large customers. They only produced end-items to actual customer demand using stocked components. The limited forward visibility of these sales orders meant that sales forecasts were used to drive replenishment of long lead-time materials. The master scheduler avoided any confusion in forecast consumption logic by using a manual policy. He developed sales forecasts in a spreadsheet based on close coordination with the customers, and periodically imported the data as a "drop in forecast".

Case #34. S&OP for One-Time Products. The Fabricated Products company designed, quoted and built hundreds of one-time products to customer specifications. They used configurable items to model one-level and two-level custom products, and to define the unique bill and routing for each configuration. This detailed information provided the basis for calculating estimated costs, a suggested sales price (based on a cost-plus-markup approach) and an estimated delivery date. Each quotation typically required quantity breakpoints (such as 100,500 and 1000 units) for the configuration. The one-time products were built from common raw materials replenished on the basis of min/max quantities.

See www.Softbrands.com for additional information about using their Demand Stream product for Kanban coordination. See www.entegrate.com for additional information about their Lean Enterprise product.

See www.oliverwight.com for additional information about their sales and operations planning tool.

Executive Summary

The ability to run the company from the top requires a Sales and Operations Planning (S&OP) process that formulates an S&OP game plan for each saleable product. The nature of each game plan depends on several factors such as the visibility of demands, delivery lead-time, and a make-to-stock versus make-to-order production strategy.

The starting point for each game plan requires identification of all sources of demand such as sales orders and forecasts, and forecast consumption logic determines how the combination of these demands drive supply chain activities. Planning calculations help formulate and analyze S&OP game plans, especially in using multiple sets of data for simulation purposes.

Several scenarios illustrated how to formulate game plans for a distribution product and several types of manufactured products, as well as project-oriented and multi-site environments. Special cases included a partially defined make-to-order product.

Guidelines were suggested to improve S&OP game plans, such as how to formulate realistic game plans, enforce near-term schedule stability, and make realistic delivery promises. The case studies highlighted variations in S&OP game plans, such as S&OP simulations, Kanban coordination, planning bills, and statistical forecasting.

This concludes Part Four of a four-part excerpt from the book Managing Your Supply Chain Using Microsoft Axapta: A Book Excerpt by Dr. Scott Hamilton.

The book can be ordered on amazon.com.

Part One began the discussion of "Sales and Operations Planning".

Part Two detailed "Understanding Planning Calculations".

Part Three will propose "Guidelines and Case Studies".

Reprinted with permission from McGraw-Hill

About the Author

Dr. Scott Hamilton has specialized in information systems for manufacturing and distribution for three decades as a consultant, developer, user, and researcher. Hamilton has consulted worldwide with over a thousand firms, conducted several hundred executive seminars, and helped design several influential ERP packages. He previously co-authored the APICS CIRM textbook on How Information Systems Impact Organizational Strategy and recently authored Managing Your Supply Chain Using Microsoft Navision. Hamilton is currently working closely with Microsoft partners involved with manufacturing and distribution, and can be reached at ScottHamiltonPhD@aol.com or 612-963-1163.