Steelwedge Software

S&OP continues to be the Holy Grail for manufacturing companies pursuing break through financial and operational performance.  One of the key requirements for any successful S&OP process is reporting and analytics.  While many technologies are available to provide flexible reporting, few applications are available that embrace Out-of-the-Box reporting and dashboards designed specifically for S&OP. 

Steelwedge has worked with leading manufacturing companies to develop a rich set of S&OP dashboards, reports and metrics that represent the Best Practices. While certain companies and industries have their own unique reporting requirements for S&OP, the following reporting categories are critical to any successful S&OP Process.

1. Revenue & Margin Tracking

2. Forecast Accuracy

3. Inventory Tracking

4. Customer Service Levels

5. Demand Supply Balancing

Revenue and Margin reports that project into the future and track actuals against plan, are one of the most fundamental reporting requirements for any successful S&OP process.  These reports are typically available in a variety of formats that include: quarter-to-date and year-to-date actuals versus targets, year over year comparisons of monthly, quarterly and year-to-date totals, rolling 3 month totals, and cumulative revenue and margin totals versus plan. 

Forecast & Accuracy is another key reporting requirement that compares actual demand versus forecast. The most common measure used for measuring forecast accuracy is Mean Absolute Percent Error (MAPE).  In addition, companies measure MAPE at different time lags to track how plans change and accuracy improves over time.  Reports that capture planning assumptions at different time lags are commonly referred to as Waterfall Reports. Understanding forecast error over lead time is important in managing risk through the use of safety stock and flexible capacities.  Finally, companies also track forecast bias to help mitigate and provide visibility to consistent under or over forecasting results.

Inventory Tracking is another key report in the S&OP process.  Companies typically look at inventory from a variety of measures including: overall investment expressed in units and dollars, days or months supply, turnover, and inventory age reports. Most companies compare actual inventories against specific inventory target levels established to meet their desired customer service requirements at the lowest investment and cost.

Customer Service Levels can be measured in a variety of ways including: order fill rates, unit fill rates, order fulfillment lead time, order fulfillment reliability, backorder levels and duration.  Increasingly customer service has become a lever for competitive advantage and therefore a key metric to track as part of the S&OP process.

Demand and Supply Balancing Reports are required to address the most fundamental challenge in S&OP around managing critical resources to resolve key constraints or shortages and excess supply.  In this regard, companies typically look at a variety of demand supply scenarios including unconstrained, constrained, and optimized plans. Critical decisions around supplier contracts, allocation, overtime, plant closures and expansions are evaluated as part of these reports.

In addition to the functional reporting categories mentioned above, S&OP reporting also requires several unique capabilities including: multiple aggregations and views, scheduled and exception based reporting and automated “email” distribution. The ability to create different aggregate views of reports that span from planners to executives, Product Line to Business Unit, and across functional areas including sales, marketing, operations and finance is a key requirement for S&OP reporting.  While this requirement is well understood, it remains to be one of the biggest challenges in implementing a successful S&OP process. Additionally, generating exception based reports that compare actuals against plan and alert planners and executives to performance that falls out of thresholds is a key requirement to ensure the most critical issues are being resolved in the day tot day planning process. Finally, creating an environment to automatically distribute scheduled and exception based reports through email is critical to enabling the most advanced S&OP process.

The Steelwedge application was designed as a single platform to support S&OP reporting across different levels of planning and across functional areas within an enterprise.  The solution was architected provide a collaborative sales forecasting solution and an overlay onto existing functional applications needed to enable the S&OP process. Additionally, built in email enabled workflow allows for secure distribution of reports throughout the enterprise.

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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

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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. 

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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.    

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