In Search of One-Number Forecasting

Transcription

1 Tactical Guidelines, K. Peterson, L. Geishecker, B. Eisenfeld Research Note 12 May 2003 In Search of One-Number Forecasting Internal, collaboration-based forecasting is key to cost, customer service and financial reliability. Enterprises that integrate their disparate forecasting systems to improve visibility will increase their revenue predictability. Core Topics Customer Relationship Management: Creating Business Value for CRM ERP II, Supply Chain & Manufacturing: Finance and Accounting Functions Strategies, Applications and Technologies; Supply Chain Management Strategies, Applications and Technologies Key Issues How will SCP and SCE support current and emerging business models, and key enterprise business initiatives? What is a CRM strategy, and how does it relate to and integrate with other enterprise business strategies, processes and operations? How will financial business applications support current and emerging business models and key enterprise business initiatives? Tactical Guidelines To develop a one-number collaborative forecast, enterprises should do the Begin with an assessment of the opportunities for improvement Determine corporate strategies and objectives and use them to drive the forecasting process Empower a collaborative, crossfunctional team to review the forecast and gain consensus across all departments Use collaboration technologies (rather than hard-coded integration) for ERP, SCP and CRM sales inputs Use continual measurement to monitor and improve the forecasting process Enterprises have a need, as well as the opportunity, to develop a one-number forecast. However, there is also confusion regarding how to accomplish this within the enterprise, because enterprises have invested in a variety of forecasting systems, including supply chain planning (SCP) solutions, customer relationship management (CRM) systems (see Note 1) and enterprise resource planning (ERP) suites. Although the processes of sales, marketing, finance and operations forecasting are theoretically and strategically interwoven, in practice, they have traditionally been disconnected activities, existing in silos of information (see Note 2). Because accurate forecasting is a cross-functional activity without clear data on which to base the resulting estimate, many functional business unit (BU) managers resort to a "crystal ball," rather than fixing forecasting processes. However, enterprises that overcome this challenge and collaboratively integrate disparate forecasting systems to improve forecasting visibility will improve their revenue predictability. Here, we define the problem, describe the opportunity for collaborative forecasting and offer guidelines for achieving the elusive, but achievable, one-number forecast. Each functional department developing a forecast has a different focus, organizational bias and intent, with the supporting technologies varying by data model semantics (see Table 1). From an enterprisewide perspective, considering the prevalence of forecasting within the enterprise and the available types of technology, these obstacles should be easy to overcome. However, nothing could be further from the truth, because the deployment of enterprisewide forecasting is inhibited by the larger issues of BU and departmental political agendas, divergent or inconsistent incentive plans, and the lack of data visibility and a common unit of measure (or standard conversions) among Gartner Reproduction of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The reader assumes sole responsibility for the selection of these materials to achieve its intended results. The opinions expressed herein are subject to change without notice.

2 BUs. If each functional group is allowed to operate in isolation without forecast reconciliation, the enterprise will experience costly surprises in demand and supply. As a result of such events as out-of-stocks, excess inventory with costly obsolescence and cost overruns, these surprises translate into missed revenue opportunities and profit reductions. Table 1 Data Model Semantics Basis Unit of Measure Bias Sales Opportunity Forecast: typically from a CRM sales package A "roll up of sales opportunities in the sales pipeline, as forecast by sales representatives and sales management. The selling unit of measure: unit for pricing and discounting. This may vary by sales channel, and often by region. The bias of the sales forecast is driven by the sales reward system and is only as accurate as the sales representative's ability to realistically anticipate closing deals within the "next" sales forecast period. Financial Forecast: typically from an ERP/ERP II package or specialized financial forecasting application The annual budget and budget revisions: Deviations are tracked by value and usually product categories The financial unit of measure: a common unit of measure used for standard costs. The bias of the financial forecast is driven by standard cost definitions. Product Forecast: typically a marketing activity from SCP Product forecasts are based on statistical modeling using corporate (order or shipment) history or point-of-sale (POS) data, judgment forecasting, causal factors and promotional data. Forecasts are mapped to multiple levels item, family, category, target market. The demand unit: an item, by channel, by location in a demand hierarchy, by family, brand, category. The bias of the product forecast is driven by data dependency on historical shipments. Sensitivities include the accuracy of this data, the relevancy of the data to predict future demand and the organizational understanding of promotions. Operations Forecast: used by operations groups based on the SCP technologies and procurement planning Forward-looking forecasts are adjusted by incoming customer orders and mapped to respective bills of materials (BOMs) for sourcing. Theproductionunit: theitemtobe produced or shipped by location. The bias of the manufacturing forecast is the minimum quantity and changeover flexibility residual in manufacturing. Source: Gartner Research (May 2003) In the absence of an accurate collaborative-forecasting process, the enterprise is "driving blind" making customer commitments (and often stock market revenue projections), then scrambling to fulfill them, or missing revenue targets because of an inability to fulfill those commitments. From a financial measurement perspective, these pieces are missing from the bigger picture. Enterprises that initiate collaborative-forecasting processes internally can improve revenue predictability by 10 percent to 25 percent and decrease inventory carrying costs by more than 30 percent within three years of deploying these processes. 12 May

3 Note 1 CRM Sales Here, the term "CRM sales" is used to describe the sales applications within CRM that contribute to forecasting. These include CRM sales suites, direct sales and consumer goods sales automation. Examples of such systems include the Amdocs (Amdocs/Clarify CRM Sales, ClearSales) CAS (CP isales) E.piphany (E.piphany Sales) J.D. Edwards (J.D. Edwards Customer Life Cycle Management solutions) MEI (UniverSell) Onyx Software (Onyx Employee Portal and Onyx Partner Portal) Oracle (Oracle Sales Online, Oracle TeleSales) PeopleSoft (PeopleSoft Sales) Pivotal (Pivotal Sales) SAP (mysap CRM sales suite) Siebel Systems (Siebel esales and Siebel econsumergoods) To ensure the successful forecasting of demand for collaborative sales activities, the creation and tracking of financial budgets, the sourcing of materials, the development of manufacturing and distribution schedules, and sales account management, a process must be established to reconcile the data into a onenumber forecast and overcome cross-functional political agendas. The concept of the information feedback loop an integral underpinning of a corporate performance management (CPM) system (see "Manage Corporate Performance to Outperform Competitors") is critical to this process. Enterprises have achieved success using a consensus-driven, integrated and interdependent set of processes based on a set of comprehensive guidelines. Assessing the Opportunity for Intraenterprise Forecast Improvement This method uses a cross-functional approach, with top-down and bottom-up forecasts reconciled to tie operations to strategy. The process should support interdependent, collaborative forecasting with a change in the one-number forecast translated and reflected in the operational forecast of others. The frequency should match the dynamics of the industry, with reviews in a dynamic business environment on a weekly or bimonthly basis (for example, for seasonal, highly perishable products) and reviews in a more-static environment on a monthly basis. However, accountability for an accurate one-number forecast must be at the top of the organization, such as at the COO, CEO or CFO level. To understand the opportunity a one-number forecast can present, enterprises should complete an organizational checklist by evaluating four items: 1. Determine the current level of integration among the enterprise's forecast systems and compare it to the level of integration that should exist. 2. For each functional process, identify the time periods (such as 30-day, 60-day, 90-day and one-year horizon) required for key decisions. For each channel and major product category, determine the The order duration the period from when the order is received until expected shipment The sales duration the period in which the sales force is actively engaged in the sales cycle and can forecast the opportunity 12 May

4 Note 2 Forecast Types and Descriptions Sales Opportunity: This forecast is a "best guess" of customer demand for an enterprise's products/services during the sales horizon (or period of selling duration). It is based on selling-environment assumptions customers, economy and the salesperson's/partner's insights into customers' needs and demands. Based on evaluation of these factors and data gained during the sales process, an assessment of customers' propensity to purchase is made. For each opportunity in the pipeline, a closure probability is assigned within a given sales horizon. Generally, the forecast is the total of revenue assigned to opportunities forecast to close during the period. This becomes what is considered "the sales forecast" and is typical functionality found in CRM sales solutions. Financial/Accounting: This forecast is developed for the budgeting process and usually covers the period of an enterprise s longest contract. It is typically created by financial groups and formulated in currency or amounts (often monthly periods), based on underlying aggregate product volume assumptions. Many enterprises confuse strategic plan, budget and forecast. Forecasts are usually tied to anticipated sales/revenue and feed the corporate budget, which is taken to the lowest level of detail cost or profit center. The financial forecast should be a "drop down" of the strategic plan, reflecting corporate goals and objectives at the enterprise level. Many enterprises incorporate financial forecasts into their longer-term CPM initiatives. Product: Developed by marketing/corporate strategy groups, this uses statistical modeling or exception-based collaboration tools to predict volumes. Output is adjusted using "forecast judgment" event marketing intelligence; new product introductions; changes in category, price, and demographics; and promotional calendars. Hierarchical models (such as brand) enable statistical modeling at higher levels of aggregate, with product mapping to lower levels of the hierarchy for specific products. Product forecasts are often one or two years, with weekly forecasting. If there is a great deal of variance by day of the week, enterprises deploy daily forecasts. Operational: This translates product demand into operational load. During development by a corporate operations group, a product forecast is consumed by incoming customer orders and translated to manufacturing load for raw material sourcing and requirements visibility of subcomponent assemblies. Manufacturing demand is translated into time buckets with offset lead times/load translation factors based on normalized yield, lot ordering needs, lot code expiration, changeover times and minimum run quantities. The marketing product duration the period of the marketing event calendar for promotions and promotion funds planning The operational horizon the period for contracting with manufacturing plants or contract manufacturing for operational load The contract horizon the period required to source critical raw material contracts 3. Analyze forecast accuracy based on these key time parameters. It may be necessary to look at accuracy by time horizon based on product hierarchy, including brand, category and specific item; geography, such as sales territory; and sales channel (for example, field sales, distributor broker and the Web). Identify the opportunity areas and develop resolutions (such as agreeing that the timing on purchasing contracts may shift, based on the visibility of an accurate forecast). 4. Perform a gap analysis to determine the areas of strategic importance. Overlay the data from item No. 2 onto item No. 3 and identify the key areas of focus for the collaborativeforecasting effort. Using the completed assessment, enterprises should begin the process of collaborative, one-number forecasting by implementing the following procedure. Determine Corporate Strategies and Objectives Enterprises should take strategic and enterprisewide views of desired results, as well as establish key metrics and opportunities for the critical time horizons for decisions. An overall CPM strategy can bring these views together in one perspective. Strategic SCP products (see Note 3) may be useful for establishing and understanding the relationship of forecast error to cost variance and revenue projections through "what if" modeling. Agreement on these strategies and objectives should be reached with the executive sponsor. These strategies may be at an aggregate level (such as category, brand or region), with a need to connect sales forecasting and product forecasting through a collaborative approach to a more-granular level (for example, product or geography channel) to drive the operational plan (see Figure 1). 12 May

5 Figure 1 Connecting Forecasts to Corporate Goals and Objectives User Type Strategic Strategy Formulation Enterprise Goals and Strategies Financial/Accounting Forecast Performance Metrics Communication Operational Transactional Feedback Execution, Operational Targets Sales Forecast Product Forecast Transaction Systems Operations Forecast Correction Improvement Source: Gartner Research (May 2003) Note 3 SCP Strategic Planning SCP what-if analysis tools include the Invensys/Baan CAPS Logistics Supply Chain Designer and Product Coordinator J.D. Edwards Strategic Network Optimization i2 Technologies Supply Chain Strategist Logility Value Chain Strategy and Value Chain Designer Manugistics NetWORKS Strategy Empower a Collaborative, Cross-Functional Review Team Cross-functional forecast teams should be formed to develop a one-number forecast. Team members should include representatives from the finance, sales, operations and marketing departments. This process will use each function's respective technologies (such as CRM sales, SCP or financial planning) as a starting point; however, the forecast will be determined using a collaborative technology on at least a monthly review cycle (with consideration of a weekly review in more-dynamic businesses). This forecast should then be used for all forward-looking business decisions, including sales and operations planning (S&OP) meetings. Use Collaborative Technologies The visibility of forecast variance between systems is a key input to the consensus process. Collaborative applications enable users to view and correctly calculate unit-of-measure translation (for example, sales opportunity forecasts made in revenue amounts being translated to the corresponding product or service equivalents for use in the SCP forecasts) or the relative importance of exception variations (for example, variance in a product line may matter more to the sales organization than to the operations group). Multiple forecasting systems (such as ERP, SCP and CRM sales), used in conjunction with a collaborative process to improve the accuracy of the one-number forecast, will drive higher accuracy than the deployment of a single ERP, SCP and 12 May

6 CRM sales system for the enterprise. If the enterprise decides to use only one technology, rather than deploying a morecollaborative approach, the technology should be selected and appropriately aligned to support the forecasting process (see Table 2). Table 2 Best Practices When Only One Type of Forecasting System Can Be Deployed Business Condition Sales cycle equal to operational decisions Short sales cycle relative to operational decisions Short order duration, short sales cycle relative to time to manufacture goods Enterprise demand closely tied to supply of several strategic partners Heavily promoted item demand based on marketing spending and account acceptance Perishable product with a short life cycle Long-term fixed contracts with little deviation Best Practice if Forecasting Is Restricted to the Use of One System Use of CRM sales applications within a CRM business strategy Use of demand-planning modules within SCP Use of demand-planning modules within SCP Use of exception forecasting through collaborative planning, forecasting and replenishment (CPFR)-type processes on a weekly basis Use of CRM sales applications within a CRM business strategy Use of POS data through a vendor-managed inventory (VMI) Use of ERP modules Source: Gartner Research (May 2003) With all types of business scenarios, a collaborative technology based on inputs from ERP, SCP and CRM sales packages is preferred. These collaboration techniques will give a better output than a single system or a hard-coded integration between systems. This collaboration can be achieved by one of four methods. Note 4 Collaborative Products for One-Number Intraenterprise Forecasting Collaborative, time-phased technologies to rationalize disparate forecasts include the Adexa Collaborative Demand Planner Demantra Demand Planner i2 Demand Planner J.D. Edwards Demand Consensus Logility Voyager XPS product set Manugistics NetWORKS Collaborate Oracle Demand Planner Syncra's XT/CT products Use of Collaboration Products: Using this technique, three forecast inputs can be mapped and compared via a time-phased collaboration tool (see Note 4), with exception management to alert key planners on deviations based on interest (product variance level) and role definition. These alerts enable visibility to problems, promoting dialogue and understanding to develop a one-number forecast. The team should develop exception thresholds for the time-phased demand for example, forecast exceptions for the long-range strategic horizon can and should be larger than exceptions in the order duration or supply chain freeze period. Trend Lines With Alerts: CRM sales and ERP forecasts can be mapped into SCP forecasting products and compared via trend lines. When a deviation occurs (for example, a previously agreed-on metric or threshold range), an automatic, forwardlooking alert can be sent to managers so that overrides to the 12 May

7 optimization output can be used in the development of the forecast, and management can then be notified in advance. Integration of Sales Forecast as an Input to ERP and SCP: Because financial and operational budgets and forecasts usually have longer duration (and are often on different planning cycles), sales forecasts can be used as an input into the respective period of sales activities into SCP with marketing overrides for promotional planning. Visibility of a Constrained Plan to CRM Sales: Enterprises can operate using a constrained plan. In this scenario, the constrained forecast is published to the sales force to limit sales on products (such as new introductions in tight supply) or for use in determining profitable order parameters. This data can also be used to guide more-profitable revenue activities using CRM sales templates. Use Measurement to Monitor and Improve the Forecasting Process Acronym Key BOM bill of materials BU business unit CPFR collaborative planning, forecasting and replenishment CPM corporate performance management CRM customer relationship management ERP enterprise resource planning POS point of sale S&OP sales and operations planning SCE supply chain execution SCP supply chain planning VMI vendor-managed inventory To be successful with one-number forecasting processes, an enterprise must measure forecast accuracy. It should also monitor manufacturing and logistics scheduling compliance to the operational forecast for potential supply issues. CPM tools and processes should be integrated to enable bi-directional feedback and consequent action between the participants. This process should be used to further refine the process and encourage adherence to the process. Bottom Line: Enterprises that develop processes to collaboratively manage multiple forecasts and develop a common, shared one-number forecast will achieve a 10 percent to 25 percent improvement in forecast accuracy. Such organizational agility is essential to improving such operations as manufacturing efficiencies and customer service, which will result in improvements in the fiscal predictability of earnings. 12 May