We welcome Noha Tohamy of AMR Reaserch on August 26 for our next Best Practice Leardership Forum Web Seminar. Hope you can join us as Noha summarizes the results of the resent study where Steelwedge has been identified as the Pure Play S&OP vendor. For more information and to register simply click the link below.
http://www.steelwedge.com/news/index.php?z=events
An effective S&OP program depends on solid, accurate demand forecasts. Best practice companies do three things well: statistical, top-down and bottom-up forecasting. Many companies are doing one or two of these, but few are doing all three well. Of course, some companies do none. Let’s just say these companies have a huge upside improvement potential.
A statistically generated forecast should use a “best fit” approach to select the mathematical algorithm that minimizes error (using mean absolute percentage error (MAPE)). The statistical engine should select the best algorithm for each time-phased data series or set of regression data. The resulting forecast should serve as a starting point for bottom-up and top-down forecasts.
Bottom-up forecasts are accumulated from many contributors. A distributed sales force may have hundreds or thousands of contributors. Each contributor has a specific area of expertise such as a specific customer, product or geographic area. The contributor enters her forecasts for her specific area of responsibility. Forecasts from all contributors are summed to capture an overall bottom-up forecast.
Conversely, a top-down approach applies a more centralized view. A small number of forecasters will look at various inputs and generate forecasts. Influencing factors may include market data, economic indicators, and general product and customer trends. Here, too, the statistically generated forecast is a good number from which to start.
The beauty of top-down and bottom-up forecasts is their ability to look at the world from differing vantage points. The folks in the “ivory tower” know important information, but they don’t know everything. The folks in the field have keen insights into their unique areas, but they only see their small piece. The challenge is to capture the small pieces without tainting the field forecaster’s view. In other words, don’t tell the field forecasters the top-down targets. When field forecasters are told what their forecasts are expected to be, they tend to send back values right in line with the top-down values. Such tainted bottom-up forecasts miss the point of gathering field intelligence.
An effective marriage will capture top-down and bottom-up forecasts separately. A management by exception S&OP tool will make comparisons quickly to enable users to analyze critical differences and refine the ultimate consensus driven forecast.
The concept of a Planning Bill of Material (PBOM) has been around for many years to manage the relationships between independent and dependant demand items. Planning BOMs are used to forecast demand for components and options available for configured products. For example, a configured laptop may have an option for a wireless card that is either specified at order entry or purchased as part of an end item off the shelf. In this case, the attach rate is a function of how often the wireless card is purchased when a laptop is purchased. Another example might be the RAM option for a computer. In this example, RAM is included in all laptops, however, the consumer has an option to select a certain quantity of RAM (e.g. 128, 512, 1.2…). In the case of RAM, the attach rate represents the percentage of time each quantity of RAM is selected when a laptop is purchased and the attach rate for the different options of RAM must add to 1.
One of the advantages using PBOM is the ability to streamline and reduce complexity in the planning process so that rather than plan for multiple end items and components, planners can focus there attention on forecasting more aggregate level platforms or product groups and use attach rates to forecast the component level details associated with the platforms. Another benefit of using a PBOM is to improve the overall accuracy of the forecast. In many cases the actual demand level for component/options may be relatively low which makes it difficult to accurately forecast demand. Forecast accuracy at the aggregate level tends to be higher compared to component/option levels since volumes are relatively larger which results in higher accuracy using statistical methods. Once the aggregate level is determined, attach rates can be used to predict the component/option level demand which improves the forecast accuracy for the component/options or mix.
While the use of PBOMs offer several advantages for planning component/option level detail, there are several limitations to the approach. (1) The accuracy of the component and option level forecast is greatly impacted by planned attach rates (2) Attach rates tend to be static and not reflect trends in the market (3) Manual tracking and managing of the attach rates can be unwieldy (4) Updating attach rates based on trends and actual demand data is typically not feasible. Systems that employ Planning BOMs expect a user to maintain the percentage (ratio) relationships between the Parent items and Component items in order to accurately assign demand values. With thousands of records to maintain, this is a huge effort, typically behind schedule, and overly generalized, resulting in poor product mix accuracy.
One of the key differentiators for the Steelwedge application is its Statistical Bill of Materials or SBOM. While the SBOM builds on all the advantages of the PBOM, the S-BOM automatically calculates the attach ratios as frequently as nightly for every new sales order line booked, using By managing these ratios at the detail level, very accurate aggregations can be presented at the customer, region, product family, etc. levels that drive a very accurate product mix prediction, and the ability to achieve higher customer service levels while maintaining lower overall inventory levels. Manual overrides at any level can be used to manage new product/option/component introductions, end-of-life, supersession, and cannibalization. In addition, the SBOM looks at trends and dynamically updates and plans for trends in the future using time-phased assumptions versus the typical static assumptions used in a PBOM.
Steelwedge jointly developed its SBOM technology based on customer input in industries that manage complex products. In addition, to helping companies that manage complex products, the SBOM technology can help customers streamline their planning process and improve accuracy by allowing planners to focus on planning at a group or category level and using the SBOM to determine detailed level SKU forecast and product mix decisions.
Integrated Business Planning is poised to become the next big thing beyond S&OP. One of the key themes for IBP is linking Operational Plans to Financial Plans. This linkage enables companies to understand how financial budgets and assumptions can impact operational plans and execution and visa versa. One of the fundamental requirements for linking financial and operational plans is the ability to translate between units and revenues/margins. This is certainly easier said than done. As companies are faced with variable customer-specific pricing structures, rapidly changing prices, cost and promotional activities, simply using a static average selling price and COGS does not provide the level of accuracy needed to understand the true relationship between units and revenue.
One of the key differentiators in the Steelwedge solution is the ability to translate plans between units and renveues. Steelwedge provides the ability to manage pricing information at various levels in a product hierarchy required to capture the true relationship between operational plans and revenue projections. In addition, Steelwedge provides the ability to model pricing and cost information in a time-phased manner. Once the operational plans are translated into revenue and margin projections, finance, operations and sales can begin to compare and reconcile plans on a periodic and exception basis.
To achieve the vision of IBP, companies must have the approporiate enabling technology to perform the basic building blocks for planning. Accurate unit to revenue conversions is one of many building block that must be done well to realize the vision of IBP.
In a recent MarketScope for S&OP Report by Gartner, Steelwedge was positioned as one of three top S&OP vendors and as solution that delivers needed functionality for both sales forecasting and S&OP processes.
The Gartner MarketScope Report for Sales and Operations Planning profiled a total of eleven vendors in the S&OP space based on proven customer implementations, demonstrated S&OP functionality, and global coverage. Each vendor was assessed based on comprehensive evaluation criteria including Market Understanding, Product Strategy, Innovation and Customer Experience. Each vendor was given an overall rating. Steelwedge was one of the three top vendors rated Positive in the study. The other leading vendors? Not SAP, not JDA, not Logility, not TXT…
“Steelwedge is pleased to be listed as one of the top vendors in Gartner’s S&OP MarketScope Report by Gartner — coming on the heels of being called the ‘Leading Pure Play S&OP Vendor’ by AMR, we feel our hard work and dedication to helping companies implement world-class S&OP solutions is getting the recognition it deserves”, said Glen Margolis, CEO of Steelwedge.