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APPENDIX W3-A: Ready-Made Decision Support Systems

Hewlett Packard (HP) Quality Decision Management

HP Quality Decision Management is an application software package for analyzing manufacturing processes and product quality. The package provides control and Pareto charts that help production and quality assurance engineers identify and prioritize statistically significant product defects and manufacturing process problems. Engineering departments can use data collected online to generate scattergrams, histograms, and tabular reports.

The package differs from conventional MIS application packages because of many added-on DSS capabilities. For example, a menu and prompt/response approach allows engineers without programming experience to configure data collection transactions, specify report and graph formats, archive data, and perform system maintenance functions. The database is designed for workstation-oriented production environments. The system provides data collection, validation, and storage to the database. It also allows analysis, sensitivity analysis, and simulation capabilities. Engineers can statistically analyze the data and output the results in tabular or graphical format.

The HP Quality Decision Management System may be used in the following application areas:

• Incoming inspection: Displays of inspection instructions, vendor rating reports, control charts of defect rates, and vendor quality
• Product test: Manual and automatic on-line data collection, test procedure display, statistical monitoring of defect levels, and decision support graphics and reports.
• Statistical process and product monitoring: On-line data collection from incoming inspection, manufacturing process and test areas, statistical graphs and reports to monitor manufacturing process quality, correlation between product defect data, and defect cause data (in a real-time environment).

HP is using PC-based software to better understand how to profitably make complex products with short life cycles. This kind of planning software has also been called fast MRP II decision support. It can provide answers to a range of questions involving materials and capacity management, production planning, and other important measures of manufacturing success. At HP PC product plants in the United States, Mexico, and Asia, planning systems help managers decide whether special orders can be taken on profitably. As the end of a product cycle nears, the software can help minimize surplus material by determining the optimal affectivity date for engineering changes. Decision-support software can be either an add-on or a replacement to master-production scheduling or capacity-requirement planning in MRP II.

Risk Estimates at Banks

Enterprisewide Risk Information Center (ERIC) is a tool that enables banks to maintain more control over potential losses by linking risk information systems into a single decision support system running continuously throughout a 24-hour business day. Developed by Tandem Computers Inc., Ernst & Young LLP, and Algorithmics, ERIC was released in the summer of 1996.

Intelligent DSS Improves Decisions

Avantos Performance Systems and Arlington Software Corp. released applications designed to automate decision making by providing a rule-based DSS for organizing and weighing options for any decision, from the purchase of new IT equipment to hiring new employees. Arlington's Which & Why for Windows is designed to be used by departments or divisions that must justify expenditures or procedural decisions. Avantos' DecideRight for Windows focuses on helping users reach decisions quickly.

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Appendix W3-B: Sample of International DSS Applications

Hertz Corporation (U.S.A. Multinational)¹

Car rental companies offer customers various combinations of car types, rental periods, and pickup and return locations, as well as temporary insurance and refueling options. Hertz developed its yield management system (YMS) to help decide about the offering of these combinations of products and their profitability over time. The YMS integrates information management technology, sophisticated operations research techniques, and information from existing Hertz decision support models that solve the closely related problems of pricing, fleet planning, and fleet deployment.

¹Condensed from W. J. Carroll and R. C. Grimes, "Evolutionary Change in Product Management: Experiences in the Car Rental Industry," Interfaces, Sept. 1995.

Royal Dutch Airlines Co. (Netherlands, Multinational)²

A DSS was developed for the aircraft maintenance department of the Royal Dutch Airline Co. (KLM) at Schiphol Airport in the Netherlands. The DSS can be used for the following tasks: estimating future workload based on the timetables of KLM and other companies, on maintenance norms, and on contracts with other companies; evaluating the quality of the matching between a given workload and workforce; and determining the size and composition of teams for a global matching between workload and workforce. The DSS is currently being used to determine the number of engineers and their qualifications that will be required in future periods.

²Condensed from E. Dijkstra et al., "A DSS for Capacity Planning of Aircraft Maintenance Personnel," International Journal of Production Economics, Oct. 1991.

Taipower (Taiwan)³

The logistics system of the Taiwan power company, Taipower, was faced with the problems of coal allocation and fleet deployment of dedicated coal carriers. The coal allocation problem, which involves high uncertainty of shipping operations, required the construction of a dynamic fleet assignment DSS model to dispatch vessels and maximize ship usage. The integrated coal allocation and fleet assignment DSS not only guides Taipower Marine Operations Management in annual schedule planning, but also helps operating personnel to allocate vessels to the schedules and to make rapid adjustments with substantial cost improvements.

³Condensed from G. H. Tzeng et al., "Taipower's Coal Logistics System: Allocation Planning and Bulk Fleet Development," International Journal of Physical Distribution and Logistics Management, Vol. 25, No. 8, 1995.

Export Development Corp. (Canada, Multinational)⁴

At the Export Development Corp. (EDC), a new computer decision support system called GAMBLE assesses insurance risk for Canadian exports and offers immediate, automated approvals. GAMBLE has reduced, turnaround times, more than doubled productivity, and established an almost paperless process. The short-term insurance unit now underwrites more than $6.6 billion (Canadian) on more than 15,000 companies in 145 countries, insuring against commercial and political risk. Before the installation of GAMBLE, approval turnaround was 11 days on average, with up to 30 hand-offs between employees before approval. About 60 percent of approvals now take one day, and complex ones push up the average to 4.4 days. The average number of hand-offs is down to four. Insured volume increased by more than 125 percent from 1990 to 1993, with credit cases up 120 percent. Staff has risen to only 58 from 51 (13.7 percent).

⁴Condensed from H. Schachter, "Risk and Rewards," Canadian Business, Nov. 1994.

Orell Fussli (Switzerland)⁵

A strategic DSS has been successfully implemented in the banknotes and securities division of Orell Fussli, a Swiss printing company. The system incorporates conceptual models and provides explanations, cases, and guidance on how to use the models effectively. It helps train managers in strategic concepts and facilitates strategic thinking. It has been used to help develop and validate product-market strategies. The system is menudriven and consists of three major sections: tools, tutorial, and application. The application section consists of two stages (product-market analysis and product-market implementation analysis) and six steps that require both system and user participation.

⁵Condensed from S. Belardo et al., "A Strategic DSS at Orell Fussli," Journal of MIS, Spring 1994.

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Group Exercise

Real Estate DSS: Write a home price estimator for a community (hint - use Excel). What method or model did you use? Where did you obtain your data? How could you make such a system into a commercial product?

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