The Impact of Forecasting Improvement on Return on Shareholder Value
John T. Mentzer; Journal of Business Forecasting

Improving Salesforce Forecasting
Mark A. Moon and John T. Mentzer; Journal of Business Forecasting

Benchmarking Sales Forecasting Management
John T. Mentzer, Carol C. Bienstock, and Kenneth B. Kahn; Business Horizons

Next Steelwedge Webcast -- State-of-the-Art Sales Forecasting Management
September 13, 1pm Eastern, 10am Pacific
Principal Speakers -
Professor J. Tom Mentzer, University of Tennessee
Todd Jones, Sr. Director Sales Operations, QLogic
Register here  for this webcast

Forecasting and Consensus Planning in a Rapidly Changing Environment
June 15, 2005, 1pm Eastern, 10am Pacific
Prrincipal Speaker -
Christine Pfefferle, Director of Global Demand & Order Management, Tellabs  
Click here to view recording

Extending SAP for Global, Integrated S&OP ,
May 25, 2005
Stu Reekie, Air Products
Click here to view recording

By Anil Gupta, The Applications Marketing Group

In most organizations, when you ask sales management to describe their sales forecasting process, you will get complex white board diagram.
And, quickly, you will realize that the complexity is not in the data or in the process, but as a result of using spreadsheets to drive a process that
requires significant collaboration, data rollups/drilldowns and data adjustments/reconciliation.
This solution brief reviews the forecasting process issues faced by sales management and how technology can streamline the process, leading to higher accuracy and better decision making. For many companies it is the difference between handily beating investor's expectations and falling short.

I. There are four key forecasting process shortcomings:

Shortcoming #1 - Spreadsheet Hell

Sales forecasting process starts with each account executive creating
bottom-up sales forecast spreadsheet and forwarding it to regional sales management. A regional sales manager rolls up forecasts from each sales executive, reviews the combined forecast for the region, makes adjustments
in individual forecasts and sends them back to the account executives for their review/feedback. When the regional forecast of dollars and units is finalized,
it is forwarded to a national sales manager, who goes through the same process of rolling up forecast from each region, reviewing the combined forecast, making adjustments in individual regional forecasts and sending them back to the regional sales managers for their review/feedback. When
the national sales forecast is finalized, it is typically sent to the global sales management for a similar review and consolidation process. Each step in the process entails multiple versions of a forecast spreadsheet traveling back and forth, requiring manual rollups & drilldowns, substantial massaging and extensive reconciliation (since rows and columns get added to original templates for additional data, comments and assumptions). So there is no single source for sales forecast. The entire process becomes a “spreadsheet hell” for sales operations.

Figure 1: Sales forecasting process creates a spreadsheet hell for sales operations teams

Shortcoming #2 : Unable to Track Changes

Limited analytics capabilities in most SFA systems, coupled with the use of spreadsheets in the sales forecasting process make it very difficult for sales operations to get answers to the following key questions without a lot of manual reconciliation and number crunching:

•  Which opportunities moved up or down in close date and how do they impact sales forecast?

•  What are the changes in forecast at product level by each opportunity?

•  What were the changes to any information in my last sales forecast and what were the reasons for those changes?

Shortcoming #3 : Week Analytics

Sales Management and Sales Operations need total visibility into key performance indicators in order to streamline the forecasting process and improve its performance. Sales forecasting process at most companies is driven by home grown applications or spreadsheets. The homegrown applications typically lack an analytics framework. Spreadsheets typically lack a historical perspective and have extremely limited capabilities to create and report on performance metrics. In addition, spreadsheets do not allow a user to configure exception-based alerts that enable Sales Ops, Finance, and Operations to more quickly react to unanticipated changes. As a result, sales operations organization is not able to easily get performance analytics to drive and streamline the process. In addition, sales management is in the dark on bubbling issues that need to be managed proactively. Key analytics needed include--

•  Aggregate forecast and history

•  Changes to forecast or margin

•  Sales forecast accuracy analysis

•  Actual to forecast performance

•  Margin contribution

Shortcoming #4 : No clear mix and margin visibility

Sales forecasting processes in most organizations do not provide clear visibility into product-level forecast. However, this information is needed by the finance organization to understand expected margins from the sales forecast and is required by the operations organization for their supply planning process. Without getting such information from the sales forecasting process, most planners end up creating a mix scenario based on their best guess, leading to poor visibility into ‘expected margins' and ‘demand-mix'.

II. Role of CRM/Sales Force Automation Systems

CRM/SFA systems enable sales managers to enforce a sales methodology, get very comprehensive visibility into the opportunity pipeline and its status and see the various current and planned sales activities at each account. The pipeline information in the SFA system frequently serves as a primary input into the sales forecasting process. However, SFA systems are not inherently designed for creating, driving, reviewing and approving a sales forecast. As a result, most account executives end up extracting pipeline information from their SFA system into a spreadsheet and using it as a baseline to create a detailed sales forecast for the month, quarter and the year.

III. An Ideal Sales Forecasting Solution

The issues, processes and roles addressed above can be addressed by a composite application, specifically designed for sales forecasting. Key capabilities of such a composite application include:

• A Dynamic, Collaborative Sales Forecasting Process
• Provides a mechanism for account executive to create a bottom-up sales forecast by product and customers, containing both – revenue and units.
• Provides a workflow for sales management to aggregate forecast, review, make changes, get buy-in on revisions from the team and publish it to their management. The workflow should automatically move the process forward with notifications for process exceptions (such as not providing the forecast or approving the change by a certain time etc.)
• Creates a Common Plan of Record which contains current and historical sales-forecasting and related information. The Common Plan of Record enables every stakeholder to review and analyze the same forecast from their respective perspectives. (see figure 2)
• Contains reporting with drill-downs and aggregation to review forecast and history at aggregate and detailed level by various dimensions.
• Integration with CRM / SFA systems
• Provides ability to extract sales pipeline information from SFA systems
• Applies configurable business rules to the extracted data to automatically clean, filter and adjust sales pipeline opportunities (for bias) and populate them within sales forecasting module
• Analytics-based architecture to provide analysis to sales management with drilldowns and aggregation to enable them to drive and improve the sales forecasting process
• Aggregate forecast and history by customer, by region, by SKU, product family etc.
• Changes to forecast/margin by account executive, customer, region, SKU, family etc.
• Sales forecast accuracy analysis by account executive, region, SKU, product family
• Actual to forecast performance MTD, QTD or YTD by account executive, region, SKU, family etc.
• Margin contribution by account executive, customer, region, SKU, product family
• Sales Operations/Sales Manager and Executive dashboards
• Key data reporting: Forecast Consumption, Backlog etc.
• Audit Trail of all activity on the system including creation, changes and approvals
• Adoption of a new application is a critical issue for field sales. Hence it is desirable to have a very intuitive Excel-like walkup user-interface, so field sales can easily adopt the application without a long drawn out training.

Figure 2: Single plan of record enables each organization to view the sales forecast from their perspective

IV. A brief review of the Steelwedge sales forecasting system

Steelwedge Software has developed a world-class sales forecasting solution, complete with process workflow, pre-built integration with popular SFA systems to import pipeline information and an integrated deep and relevant analytics. Leading companies have selected the Steelwedge solution to drive their sales forecasting process.

The system uses Enterprise-enabled Excel as a front-end to ensure easy adoption across the enterprise. In addition, it is fully integrated with the rest of modules within its planning suite including demand planning and forecasting, product lifecycle planning, revenue and margin planning, sales and operations planning, supply planning and performance management.

As a result, customers can implement sales forecasting capabilities today, but can easily leverage it to drive demand planning or S&OP, while maintaining a common plan of record across the enterprise. A common plan enables cross-functional alignment by ensuring all stakeholders are looking at the same plan from their perspective and by providing a basis for performance analytics.

Figure 3: Steelwedge Planning and Performance Management Suite

Traditional approaches to sales forecasting have not only led to “spreadsheet hell”, but have provided the sales operations teams with very little analytics to drive and improve the process. In addition, finance, operations and marketing organizations have not been able to gain the needed forecast visibility to predict margins, create high level supply plans and determine the impact of new product introductions on revenue/market share. However by leveraging next generation sales forecasting applications, companies can address these issues and convert sales forecasts into high leverage strategic plans leading to improved performance.

Anil Gupta is a principal at The Applications Marketing Group. He has specific expertise in ERP, supply chain and analytics applications. He is also a research advisor to Ventana Research, an industry analyst firm, in IT performance management. Anil has been a VP of Strategy and/or Marketing of enterprise software companies such as Baan, Niku, Evolve and Oracle.