Strategic forecasting, e-commerce, SAP R/3, ERP

Enterprise Resource Planning (ERP) has evolved rapidly on two fronts: (1) support for strategic planning in specific industries, (2) support for e-commerce. PricewaterhouseCoopers, for example, is extending the SAP Banking Strategic Enterprise Management set of solutions to include risk, profit, and strategy analyzers. SAP continues to add components to SAP R/3 to support e-commerce. This is an evaluation of the extent to which formal analytical methods of strategic forecasting are being and should be included in these advances.

International Symposium on Forecasting 1999
Capital Hilton Hotel
Washington, DC
June 29, 1999

To what extent have SAP and its software partners improved the strategic forecasting process, particularly in support of e-commerce and of e-business? The potential of Enterprise Resource Planning (ERP) systems for improving the strategic forecasting process is great. Prior to ERP, divisions or departments of businesses maintained their own information systems. Critics of this approach referred to islands or silos of information. SAP refers to information management as the deciding competitive factor. In the current business environment of short innovation cycles, worldwide competition, and high costs, SAP claims that businesses must compete over the long term by both business re-engineering and technology re-engineering [Buck-Emden and Galimow (1996)]. Business re-engineering involves the analysis of net-value-added chains to optimize business processes [Hammer and Champy (1993), Hammer (1995 and 1996)]. Technology re-engineering involves the use of the most modern data processing technology to optimize enterprise-wide information management.

At the top of SAP’s strategic system is the Management Cockpit, a trademarked name of SAP AG but delivered by N.E.T. Research, of Brussels, Belgium.

The purpose of the Management Cockpit is to provide management with a single room where they can see all mission-critical success factors simultaneously. In that way, managers may learn to recognize over time more relationships between the factors. In the Management Cockpit, there are 4 walls: (1) Black: for main success factors and financial indicators (2) Red: for market performance (3) Blue: for the performance of internal processes and employees (4) White: for the status of strategic projects.

The Management Cockpit, created by brain surgeon and professor of management, Dr. Patrick M. Georges, comes as a pre-packaged hardware, software, and service system. The underlying assumptions of the Management Cockpit are:

1. Executive decision-makers should concentrate on the essentials: critical success factors.
2. The data must be both financial and non-financial.
3. Data must be displayed in a consistent manner.
4. Executives need real-time data.
5. Simultaneous viewing of critical success factors will aid in understanding the interrelationships between critical success factors.
6. Executives should be able to drill down to detailed data contained in SAP’s SEM (Strategic Enterprise Management) and BW (Business Information Warehouse).
7. Executives should be able to integrate non-SAP transaction data and external information through SAP SEM.

SAP’s Strategic Enterprise Management (SEM) is the basis for the development of future analytical applications closely integrated with an ERP system. For detailed white papers and other information on how to use value-based management methods for translating strategies into action, see:

Some of the current SAP SEM components are:

SEM-BCS (Business Consolidation)

SEM-BPS (Business Planning and Simulation)

This component will support the integration of strategic and operational business planning processes. Included with this component are rolling forecasts, dynamic and linear business models, simulation of scenarios, risk analysis, and resource allocation.

SEM-CPM (Corporate Performance Monitor)

Balanced Scorecards, Value Drivers, and the Management Cockpit work with this component.

SEM-BIC (Business Information Collection)

SEM-SRM (Stakeholder Relationship Management)

An integral part of SAP’s approach to SEM is the use of automatic forecasting. Automatic forecasting is not a substitute for the strategic planning and budgeting processes. Bottom-up budgeting might include biased data from those whose performances will be evaluated. Automatic forecasting systems provide a means for using data mining, neural networks, and genetic algorithms to detect global trends not recognized by managers.

In 1998, SAP introduced the SAP Business Information Warehouse (BW). SAP’s BW is not included with SAP R/3 basis. SAP R/3 users may satisfy their data warehousing needs using the products of other software companies. SAP’s BW is an open system, capable of extracting data from nearly any source. BW includes business application programming interfaces (BAPIs) and with a file load interface for flat files. Two certified partners are offering extraction and staging area capabilities: ETI and Informatica.

The data that you need for strategic forecasting might be stored in several systems, in different formats, and on multiple platforms. The traditional solution has been to hire teams of database programmers to write the code to bring all of this data together. ETI has developed a Data System Library (DSL) and ETI EXTRACT for moving data in or out of SAP R/3 and BW.

Informatica’s PowerCenter 1.5 is a metadata-driven software system for business intelligence and analytic applications. Informatica’s MX2 application programming interface (API) program is COM-based, UML-compliant, and supports multidimensional metadata structures for online analytical processing (OLAP).

The syskoplan approach to data warehouse solutions using SAP R/3 and BW is:

SAP BW supports Microsoft’s OLE DB for OLAP interface.

IBM Corporation, a SAP alliance partner, offers data warehouse solutions using SAP’s BW or full external data warehouse systems. IBM’s solution includes an SAP Warehouse Data Model for mapping to SAP data elements.

Georgia-Pacific Corp. is an early adopter of BW because of the ease of using SAP R/3 transaction data for analyses. Farmland Industries, Inc., is developing its own data warehouse for use until future versions of BW have more automated extraction routines. For similar reasons, AlliedSignal has opted not to use BW. Instead, AlliedSignal will use Influence Software’s packaged data marts.

In order to have a strategic forecasting system, you must be able to access R/3 data in at least one of three ways:

1. Direct access to production data. Users of SAP R/3 chose one of the major database management software systems to use with SAP R/3. So, you could query the latest data using the DBMS. To avoid hurting the performance of the production system, you should not use this way for large volumes of data or for large numbers of users. BusinessObjects is a company supporting this method by using Rapid Deployment Templates (RDTs) for SAP R/3.
2. Extract the data into corporate data warehouses or departmental datamarts. To avoid performance and security problems with online transaction processing (OLTP) systems, you may want to extract production data into a corporate data warehouse or departmental datamart. Working offline, you have the advantage of a fixed set of data. If you are testing different models on data, then you do not want your data changing continuously. ETI, Prism, and Carleton offer extraction software. While an organization could write its own extraction software using ABAP/4, there would be large costs of maintaining customized systems every time data structures change.
3. Query data in SAP’s Open Information Warehouse.

On April 20, 1999, SAP AG announced a partnership with The Balanced Scorecard Collaborative, Inc. to develop the SAP Strategic Enterprise Management (SAP SEM) solution. The Balanced Scorecard will be integrated also with the SAP Management Cockpit. The Balanced Scorecard approach, introduced by Harvard Business School professor Robert Kaplan and management consultant David Norton, is a method for balancing the needs to view both current operating performance and the drivers of future performance. The balanced scorecard uses 4 perspectives: (1) financial (2) customer (3) internal business processes (4) learning and growth. These 4 perspectives represent a balance between short- and long-term objectives, desired outcomes and the performance drivers of those outcomes, and between objective and subjective measures. For each of the perspectives, a firm will use 4 to 7 separate measures. So, a typical Balanced Scorecard might contain 25 measures serving as the instruments for viewing a single strategy. The Balanced Scorecard is not a substitute for the hundreds or thousands of measures a firm must use for diagnostic purposes (management by exception). Rather, the Balanced Scorecard measures must be strategic measures designed to measure sustained competitive advantage.

The choice of financial measures will depend upon the life cycle of products. For rapid growth products, a firm will use growth objectives. For mature products, a firm will emphasize maximizing cash flow. There are 3 financial themes in business strategies: (1) revenue growth and mix, (2) cost reduction/productivity improvement, (3) asset utilization/investment strategy.

The originators of the Balanced Scorecard viewed a strategy as a set of hypotheses about cause and effects. They regarded specifying relationships as a mix of subjective and of objective estimates. As a result, Kaplan and Norton (1996a, 1996b) recommended that the Balanced Scorecard for a business’ value creation process be captured in a systems dynamics model. For details on systems dynamics, they suggested Forrester (1961), Roberts (1978), and Kofman, Repenning, and Sterman (1994). Forecasters unfamiliar with system dynamics might prefer to start with a book comparing econometrics, Box-Jenkins time series analysis, and system dynamics [Pindyck and Rubinfeld (1997)].

There are many additional books and articles on the Balanced Scorecard or on performance measurement [Harvard Business School (1998), Olve, Roy, and Wetter (1999), Hronec (1993), Lynch (1995), Brown (1996), Sumanth (1997), Harbour (1997), Hodgetts (1998), Frost (1998), Kaydos (1998), Mitchell, Coles, and Metz (1999)]. In terms of strategic forecasting, the main theme of this literature is that there is a difference in the selection of performance measures for strategic planning and for operations. While a firm might use automatic time series methods to forecast inventory demand for tens of thousands of items in inventory, a firm will use causal methods to study the interrelationships between a small number of performance measures for strategic planning.

SAP offers different versions of its software for different industries. SAP Banking is an example of an industry-specific solution. Strategic Enterprise Management (SEM) has helped banks to eliminate inconsistent data and numerous reconciliation points in their old systems. The original SAP Banking SEM solution included: Risk Analyzer, Profit Analyzer, and Strategy Analyzer. PricewaterhouseCoopers has joined with SAP in a project to develop extended applications for SAP’s SEM set of solutions for SAP Banking. In addition to Balanced Scorecard reporting, they will be adding: Risk-Adjusted Return on Capital (RAROC), free cash flow valuations, and value-based reporting. Of particular interest to strategic forecasters will be the enhanced business planning and forecasting capabilities for the Asset Liability Management (ALM) capability.

These moves by SAP and by PricewaterhouseCoopers have implications for the future of strategic forecasting. ERP systems are becoming more industry specific over time. As a result, future strategic forecasting methods may become more industry specific over time.

On April 19, 1999, SAP AG announced that it had formed an alliance with Powersim Corporation to enhance the SAP SEM solution using systems dynamics simulation software. SAP AG made a minority equity investment in Powersim Corporation because the management of SAP AG regards systems dynamics as one of the leading technologies for strategic planning systems. Bernard Teiling, assistant vice president and head of business process integration for Nestlé SA, reported that Nestlé is using this SAP and Powersim solution. SAP is able to form these alliances because of its SAP Business Framework and Business Application Programming Interface (BAPI) technology.

Powersim Corporation, headed by Ulf Gustavsen, has offices in Herndon, Virginia, San Francisco, California, and in Isdalstø, Norway. Powersim solutions for strategic planning can be counter intuitive. For example, an oil company discovered that it could increase its profits from an oil field with rapidly depleting amounts of oil by slowing the extraction process and by investing in additional equipment. Powersim connects its models to other software applications using an open API. People in remote locations can use simulations via Internet Web sites. Powersim’s software runs on Microsoft Windows 95, 98, and NT.

Major components of Powersim’s software are:

1. Powersim Constructor is a modeling tool used to create simulations.
2. Powersim Solver is used for model analysis, optimization, and risk management.
3. Powersim Metro Server is a network server used on the Internet and on intranets.
4. Powersim Engine is used for building stand-alone simulation applications.

SAP R/3 has included operational ABC (activity based costing) in its CO (controlling) application. SAP chose to expand ABC capabilities even further through a strategic alliance with ABC Technologies. The "R/3-OROS Bridge" provides integration with ABC Technologies’ analytic ABC. This means that the whole range of ABC methods is available to SAP R/3 users, including strategic cost management, simulation, activity-based cost management [Cokins (1996), Kaplan and Cooper (1997)], and activity-based budgeting [Brimson, Antos, and Player (1999)].

The Alcar Group, using the shareholder value approach to strategic financial planning, provides integration support for use with SAP R/3.

Demand Solutions (Europe) integrated its forecast management and supply chain management software with SAP R/3. The system tracks forecasted sales and monitors forecast accuracy. An illustrated application of the DS Forecast Management module is the generation of production and purchasing plans:

While forecasting has been a component of SAP’s expansion of its system to support electronic commerce, there have been some more urgent issues. Electronic catalog purchasing components have been a top priority. Some of SAP’s future plans include: (a) convergence billing (one statement to a customer including charges from more than one company or subsidiary) (b) Web-enabling the entire process from procuring the customer to receiving the payment (c) supporting payment collection by third parties. Logistix is working with SAP to find ways to solve scaling problems. Companies must have ways of handling both many hits at a Web site and of improving their operations to be able to fulfill large increases in orders.

SAP’s Business Information Warehouse (BW) can be used to supply predefined reports. The Advanced Planner Optimizer (APO) integrates forecasting capabilities with supply chain management. SAP has introduced liveCache to speed the execution of forecasting and supply chain planning optimization functions.

Pandesic LLC, owned jointly by SAP and Intel, develops e-business systems for clients using SAP R/3. Pandesic distinguishes technical and business process scalability. By adding additional web and application servers, Pandesic can scale its system to handle daily millions of shopping page views and thousands of customer orders. Examples of business process scalability would include having a system capable of shipping thousands of orders per day from a warehouse and of handling hundreds of returns. Pandesic uses Microsoft Windows NT and Microsoft SQL Server as parts of its system. For details of Pandesic’s system, you may request them to email you copies of their technical white papers or other detailed information.

Enterprise Resource Planning (ERP) systems, as exemplified by SAP R/3, have improved organizations through the use of business re-engineering and client/server technology. From this new base, SAP has started to develop the Management Cockpit and Strategic Enterprise Management (SEM). Automatic forecasting augments the strategic planning and budgeting processes by enabling the use of data mining, neural networks, and genetic algorithms. SAP’s Business Information Warehouse (BW) provides the means for assembling and analyzing data from many sources within and outside the firm. Strategic forecasting systems have improved in those firms with systems able to access data via direct access to production data, extraction into data warehouses, and via queries using SAP’s Open Information Warehouse.

Of the hundreds or thousands of performance measures, a firm must identify those measures crucial for achieving sustained competitive advantage. SAP has selected the Balanced Scorecard approach. PricewaterhouseCoopers has joined SAP in extending SEM at the industry level, starting with the banking industry. In an alliance with Powersim Corporation, SAP has embraced systems dynamics as its leading method for strategic planning systems. Other software firms, such as Demand Solutions (Europe) are enhancing SAP’s forecasting capabilities by making their software systems SAP R/3-ready.

SAP has made major efforts to extend SAP R/3 to support e-commerce. While early e-commerce efforts have concentrated on issues such as electronic catalog purchasing and on scalability, SAP has developed BW, Advanced Planner Optimizer (APO), and liveCache to support strategic forecasting needs. In addition, SAP and Intel started Pandesic LLC to develop e-business systems for clients.

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Brown, Mark Graham, Keeping Score: Using the Right Metrics to Drive World-Class Performance, Productivity, Inc., 1996.

ISBN 0814403271

Buck-Emden, Rüdiger, and Jürgen Galimow, SAP R/3 System: A Client/Server Technology, Addison-Wesley, 1996. ISBN: 0201403501

Carr, David K., Best Practices in Reengineering: What Works and What Doesn’t in the Reengineering Process, McGraw-Hill, 1995.

ISBN: 007011224X

Cokins, Gary, Activity-Based Cost Management: Making It Work, McGraw-Hill, 1996. ISBN 0786307404

Doane, Michael, SAP Blue Book: A Concise Business Guide to the World of SAP, Doane Associates Press, 1998.

ISBN: 1575791250

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ISBN: 1575790300

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ISBN: 1575790297

Forrester, J. W., Industrial Dynamics, MIT Press, 1961. ISBN 0915299887

Frost, Bob, Measuring Performance, Measurement International, 1998. ISBN 0788014072

Hammer, Michael, and James Champy, Reengineering the Corporation: A Manifesto for Business Revolution, Harper Business, 1993. ISBN: 088730687X (paperback)

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ISBN: 0887307299

Harbour, Jerry L., The Basics of Performance Measurement, Productivity, Inc., 1997. ISBN 0527763284

Harvard Business School, Harvard Business Review on Measuring Corporate Performance, Harvard Business School Press, 1998.

ISBN 0875848826

Hodgetts, Richard M., Measures of Quality and High Performance: Simple Tools and Lessons Learned from America’s Most Successful Corporations, AMACOM, 1998. ISBN 0814403778

Hronec, Steven M., Vital Signs: Using Quality, Time, and Cost Performance Measurements to Chart Your Company’s Future, AMACOM, 1993. ISBN 0814450733

Hunt, V. Daniel, Process Mapping: How to Reengineer Your Business Processes, Wiley, 1996.

ISBN: 0471132810

Kaplan, Robert S., and Robin Cooper, Cost & Effect: Using Integrated Cost Systems to Drive Profitability and Performance, Harvard Business School Press, 1997. ISBN 08758477889

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Kaplan, Robert S., and David P. Norton, "Linking the Balanced Scorecard to Strategy," California Management Review, Vol. 39, No. 1, Fall 1996, pp. 53 – 79.

Kaydos, W. J., Operational Performance Measurement: Increasing Total Productivity, Saint Lucie Press, 1998. ISBN 1574440993

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Olve, Nils-Goran, Jan Roy, and Magnus Wetter, Performance Drivers: A Practical Guide to Using the Balanced Scorecard, Wiley, 1999. ISBN 0471986232

Pandesic LLC, "The Pandesic E-business Solution," Version 3.0, Solution White Paper, December 1998.

Pindyck, Robert S., and Daniel L. Rubinfeld, Econometric Models and Economic Forecasts, McGraw Hill, 1997. ISBN 0079132928

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