INFORMS Cincinnati 1999 Sponsor:
Technology Management Section

Interdependence & Flexibility in Product Development

SA06.1 New Forms of Interdependence in Virtual Product Development Teams

• Nancy A. Staudenmayer; Duke University, Fuqua Sch. of Bus., Box 90120, Durham, NC 27708-0120; nstauden@mail.duke.edu
• Gerardine Desanctis; Duke University, Fuqua Sch. of Bus., Box 90120, Durham, NC 27708-0120;
• Sze Sze Wong; ;

Virtual organizations rely heavily on linkages with other entities in order to conduct their core work activities, distributing tasks across space, time and other boundaries. We explore the implications of more virtual ways of working for the types of interdependencies in product development. It draws upon survey and case study data.

SA06.2 Managing Development Flexibility in Uncertain Environments

• Stefan Thomke; Harvard Business School, Morgan Hall T63, Soldiers Field, Boston, MA 02163; sthomke@hbs.edu

As product complexity and the rate of market change have dramatically increased, firms find it increasingly difficult to forecast product requirements in their development processes. I propose to redefine the problem from one of improving forecasting to one of increasing product development flexibility and will report on related empirical findings.

SA06.3 Positioning & Launching Platform-Based Products

• Viswanthan Krishnan; University of Texas, Dept. of Mgmt., CBA 4.202, Austin, TX 78712; krishnan@mail.utexas.edu
• Saurabh Gupta; Texas Business School, CBA 4.202, Austin, TX 78712;

The benefits of product platforms have gained increasing research attention. Are there costs associated with platforms? How do platforms influence the product location and launch decisions? We answer these questions with an application-driven mathematical model. Our analysis helps characterize the appropriateness of platforms for different business situations and implications for managers.

SA06.4 The Real Options Value of Modular Product Architecture

• Christoph H. Loch; INSEAD, Blvd. de Constance, Fontainebleau, 77305 , France; christoph.loch@insead.fr
• Michael T. Pich; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77305 , France; michael.pich@insead.fr

We explore the implications of choosing a modular vs integrated product architecture. The choice of architecture constrains future changes to the product's attributes. We compare the flexibility of varying performance attributes independently of one another in the 2 architectures. An option 'value' results from the capability of meeting uncertain future market attribute targets.

Managing the Back-End of Product Development

SB06.1 A Formal Model of Engineering Fire-Fighting

• Roger Bohn; University of California, IR/PS 0510, 9500 Gilman Dr., La Jolla, CA 92093-0519; rbohn@ucsd.edu
• R. Jaikumar; ;

Folklore in many organizations is that engineers and other knowledge workers often engage in 'fire-fighting', rushing from crisis to crisis. We use a simple queueing model to show how it occurs. Once it begins, common coping methods exacerbate the problem. We include empirical data from a development and manufacturing facility.

SB06.2 Learning & Process Improvement during Production Ramp-Up

• Christian Terwiesch; University of Pennsylvania, The Wharton Sch., Dept. of OPIM, SH-DH 1317, Philadelphia, PA 19104; terwiesch@wharton.upenn.edu
• Roger Bohn; University of California, IR/PS 0510, 9500 Gilman Dr., La Jolla, CA 92093-0519; rbohn@ucsd.edu

We analyze interactions among capacity utilization, yields and process improvement (learning) during production ramp-up of a new product. We model learning in the form of deliberate experimentation. Experiments such as engineering trials lead to process improvements, but they also reduce capacity in the short run. This creates a trade-off between experiments and production.

SB06.3 Productivity Improvement in a Network of Learning Factories: A Learning Curve Analysis

• Michael A. Lapre; Boston University, School of Mgmt., 595 Commonwealth Ave., Boston, MA 02215; mlapre@bu.edu
• Luk N. Van Wassenhove; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77305 , France; luk.van.wassenhove@insead.fr

We use a total factor productivity learning curve analysis to study the acquisition and transfer of tacit and explicit knowledge in a network of 3 factories. From the empirical evidence, we derive implications for managing the nature of shared knowledge, stability in process conditions and control over resources for experimentation.

SB06.4 Using Flexible Criteria for Components to Reduce Overall Development Costs of End-Products

• Pat Hammett; University of Michigan, Study of Auto Transportation, Ann Arbor, MI 48109; phammett@umich.edu

In efforts to minimize process validation problems during the launch of a complex product, manufacturers traditionally require suppliers of components to meet rigid criteria for conformance to specifications. This paper shows how certain manufacturers of automotive bodies are using more flexible criteria to reduce the overall development costs of the end product.

Set-Based Design in Practice & Theory

SC06.1 Lessons from Implementing Set-Based Concurrent Engineering

• Allen C. Ward; Ward Synthesis, Inc., 3446 Gettysburg Rd., Ann Arbor, MI 48105; award@dundee.net

Set-based concurrent engineering posits that design teams can be more efficient by reasoning about and communicating sets of ideas rather than one idea at a time. I will discuss why, in practice, SBCE requires a different set of tools, skills, and attitudes to achieve these gains.

SC06.2 Design Methods in the Aerospace Industry: Looking for Evidence of Set-Based Practices

• Joshua I. Bernstein; MIT, Rm. 33-407, 77 Massachusetts Ave., Cambridge, MA 02139; jbern@mit.edu

This initial investigative study into set-based concurrent engineering (SBCE), sponsored by the Lean Aerospace Initiative at MIT, aimed to assess the 'set-basedness' of the aerospace industry. This research found no clear-cut instances of set-based concurrent engineering, though many set-based techniques are used throughout the industry.

SC06.3 Mathematics & Algorithms Supporting Set-Based Design

• William W. Finch; MIT, Rm. 3-455G, 77 Massachusetts Ave., Cambridge, MA 02139; wfinch@mit.edu

The set-based design paradigm is one in which engineers think about sets of designs, environments, operating conditions, etc. Automation of set-based design processes requires development of new mathematical tools. This research combines predicate logic and constraint satisfaction to develop design automation tools for design under uncertainty, robust design and concurrent engineering.

SC06.4 Reducing Technical Uncertainty in Product & Process Development through Parallel Design of Prototypes

• Ely Dahan; MIT, Sloan Sch. of Mktg., 38 Memorial Dr., E56-323, Cambridge, MA 02142; edahan@mit.edu

We model prototyping using Bernoulli experiments with known rewards, costs and success probabilities. We compare the performance of pure parallel, pure sequential and hybrid parallel-sequential experimentation policies. We present a prototype design methodology and show how the experimentation mode determines the preferred product.

Decisions & Communication in Product Innovation

SD06.1 Beyond the New Product Development Speed Limit

• Barry L. Bayus; University of North Carolina, Kenan-Flagler Business Sch., CB 3490, Chapel Hill, NC 27599-3490; barry.bayus@unc.edu

Time-to-market decisions are on the mind of most companies. Trade-offs between time-to-market, product performance and quality, and development costs require consideration. Normative and empirical analyses argue products should not be rushed to market unless a careful analysis of the benefits and costs indicate that this is indeed the best strategy.

SD06.2 Decision Making Effectiveness in Virtual New Product Teams

• Mitzi M. Montoya-Weiss; North Carolina State University, Dept. of Bus. Mgmt., 132J Nelson Hall, Box 7229, Raleigh, NC 27695-7229; mitzi_montoya-weiss@ncsu.edu

As NPD becomes increasingly global in scope, new product teams are often composed of individuals who are not co-located. Information technology enables dispersed team communication. This study compares the decision making behaviors of co-located and dispersed new product teams. The dispersed teams in this study communicate via Lotus Notes.

SD06.3 Challenges in Corporate & University R&D Collaboration

• Diane H. Sonnenwald; University of North Carolina, Sch. of Info. & Library Sci., 100 Manning Hall, CB 3360, Chapel Hill, NC 27599-3360; dhs@ils.unc.edu

Corporations and universities establish collaborative relationships to create innovative products and support university research. Such collaborations require employees and faculty to mutually develop the collaborative process. Results of interviews with collaboration participants are presented. Significant challenges that can impede collaboration are rooted in organizational environments, project goals, and interpersonal relationships.

SD06.4 Requirements Determination for Time-Paced Software Development

• Mohan V. Tatikonda; University of North Carolina, Kenan-Flagler Bus. School, McColl Bldg., CB 3490, Chapel Hill, NC 27599-3490; mohan_tatikonda@unc.edu

Releasing new products on a regular time periodic basis is gaining attention but is not well understood. A major commercial developer of mainframe and shrink-wrap software is examined to identify how results from annual installed-base customer satisfaction surveys can best be used to prioritize product requirements for subsequent releases.

Panel: Publishing Outlets for Technology & Innovation Management Research

SD23.1 Panel: Publishing Outlets for Technology & Innovation Management Research

• Abbie Griffin; University of Illinois, 350 Commerce West, Champaign, IL 61820; abbieg@uiuc.edu
• Linda Argote; Carnegie Mellon University, Grad. Sch. of Indust. Admin., Pittsburgh, PA 15213; argote@andrew.cmu.edu
• Robert M. Mason; Case Western Reserve University, Weatherhead Sch. of Mgmt., 10900 Euclid Ave., Cleveland, OH 44106-7235; rmm3@po.cwru.edu
• Michael K. Badawy; Virginia Tech., PO Box 3987, Merrifield, VA 22116-2931; mbadawy@vt.edu

This panel of 4 technology and innovation management journal editors will present information related to the editorial policies, topical interests and scope and domain of their respective journals. An ample opportunity for discussing issues and guidelines for potential authors will follow.

Issues in New Product Development

MA06.1 Product Line Strategy & Firm Performance: The Case of Radical Technological Change in the PBX Industry

• Neil R. Jones; University of Western Ontario, Ivey Sch. of Bus., London, Ontario, N6A 5B8 , Canada; njones@ivey.uwo.ca

We test the potential of product line strategy - how sequences of related development projects are managed - to explain variation in firm performance. Product development strategies that result in longer lived products and greater numbers of products introduced are shown to be associated with higher firm performance.

MA06.2 A Comparative Study of Product Development Process Design

• Alan D. MacCormack; Harvard Business School, Morgan Hall T39, Soldiers Field Park, Boston, MA 02163; maccormack@hbs.edu

We contrast the design of the new product development process across different environments. The evidence presented suggests that rather than being one best way to develop products, the process differs substantially, yet predictably, across environments. The data is consistent with a contingent view of the new product development process.

MA06.3 The Fundamental Attribution Error in Product Development Improvement Efforts

• Nelson Repenning; MIT, OM/System Dynamics Group, Sloan Sch., 30 Wadsworth St., Cambridge, MA 02139; nelsonr@mit.edu
• John Foster; ;

We investigate the transition problem in product development: how does an organization move from its current process to a more desirable one and why is this transition difficult? We focus on the interactions between the structures of such processes and the behaviors of the people within them.

MA06.4 The Architecture of Knowledge & Information Flows: A Comparison of New Product Development & Enterprise Resource Planning Systems

• Anil Khurana; Boston University, Sch. of Mgmt., 595 Commonwealth Ave., Boston, MA 02215;
• P. R. Balasubramanian; Boston University, Sch. of Mgmt., 595 Commonwealth Ave., Boston, MA 02215; bala@bu.edu

We present a framework for the architecture of knowledge management and show how an information systems environment could be modeled. We take 2 case examples, one from an Enterprise Resource Planning (ERP) system, and another from a New Product Development environment.

The Product Architecture - Supply Chain Interface

MA23.1 Architectures in 3D: Concurrent Product, Process & Supply Chain Development

• Morris A. Cohen; University of Pennsylvania, The Wharton School, Philadelphia, PA 19104-6366; cohen@upenn.wharton.edu
• Charles Fine; MIT, 30 Wadsworth St., E53-390, Cambridge, MA 02139; charley@mit.edu

MA23.2 Supply Chain Architecture: Definition & Testing

• Sharon Novak; MIT, 30 Wadsworth St., E53-390, Cambridge, MA 02139; snovak@mit.edu
• Charles Fine; MIT, 30 Wadsworth St., E53-390, Cambridge, MA 02139; charley@mit.edu

MA23.3 Investment in New Product Capacity: The Option Value of Subcontracting

• Jan A. van Mieghem; Northwestern University, Kellogg Sch. of Mgmt., MEDS Dept., 2001 Sheridan Rd., Evanston, IL 60208-2001; vanmieghem@nwu.edu

MA23.4 Supplier Involvement in Design in America: How Far Have We Come?

• Jeffrey K. Liker; University of Michigan, Dept. of IOE, 1520 McIntyre Dr., Ann Arbor, MI 48105; liker@engin.umich.edu
• Young Ro; University of Michigan, Dept. of IOE, Ann Arbor, MI 48109;
• Laurent Langlet; Renault;

Continuity, Change & the Product Development Process

MC06.1 Impact of Information Sharing on the Distortion of Demand

• Shanling Li; McGill University, Fac. of Management, 1001 Sherbrooke St. W, Montreal, Quebec, H3A 1G5 , Canada; li@management.mcgill.ca
• Afshin Afshari; McGill University, Fac. of Management, 1001 Sherbrooke St. W, Montreal, Quebec, H3A 1G5 , Canada;

We examine the impact of information sharing in a supply chain model. We propose a sophisticated forecasting technique that updates the demand information over the product life cycles. The demand information may be shared among retailer, manufacturer and supplier. The resulted performance is compared with no information sharing.

MC06.2 Drivers of New Product Development Success: Multivariate Results from PDMA's Best Practices Research

• Abbie Griffin; University of Illinois, 350 Commerce West, Champaign, IL 61820; abbieg@uiuc.edu

We present several multivariate analyses of success drivers in NPD using an extensive data set from 383 US firms. We also present a multi-stage model of NPD factors, analyzed using the same data set. The model and data cover NPD processes, organizations, tools and outcomes.

MC06.3 Discontinuous Change, Disruptive Technologies & the Challenge of Developing New Products

• Susan Walsh Sanderson; National Science Foundation, Innovation & Org. Change Prog., 4201 Wilson Blvd., Arlington, VA 22230; ssanders@nsf.gov

This research looks at the challenge of developing new products in the context of discontinuous change and disruptive new technologies. Studies of firms in the digital photography market help elucidate the elements that lead to success or failure in a dynamic industry.

MC06.4 Setup Strategies in Flexible Manufacturing Environments

• Valerie Tardif; University of Texas, Graduate Program in OR/IE, Dept. of Mech. Eng., Austin, TX 78712-1063; vtardif@mail.utexas.edu
• Wei-Liang Lin; University of Texas, Graduate Program in OR/IE, Dept. of mech. Eng., Austin, TX 78712-1063; wll@csr.utexas.edu

We consider different setup strategies in a flexible PCB assembly line experiencing uncertainty in demand. This is especially important at the beginning of a new product life cycle, when demand volume is low or medium and quite difficult to predict. We present computational results and insights obtained.

Technology Management in Rapidly Changing Environments

MC23.1 Managing High-Tech Knowledge Workers in Growing Markets

• Edward G. Anderson, Jr.; University of Texas, Mgmt. Dept., CBA 4.202, Austin, TX 78712; edanderson@mail.utexas.edu

MC23.2 Industry Clockspeed & the Pace of New Product Development

• Janice E. Carrillo; Washington University, CB 1133, One Brookings Dr.,, St. Louis, MO 63130-4899; carrillo@wuolin.wustl.edu

MC23.3 Managing Uncertainty in New Product Development of High Technology Products

• Shantanu H. Bhattacharya; INSEAD, Dept. of Tech. Mgmt., Blvd. de Constance, Fontainebleau Cedex, 77305 , France; shantanu.bhattacharya@insead.fr

MC23.4 Using Real Time Sales Information to Manage Inventory for Technology-Based Short Life Cycle Products

• Nikhil Jain; University of Cincinnati, Coll. of Bus. Admin., PO Box 210230, Cincinnati, OH 45221; nikhil.jain@uc.edu
• Hirofumi Matsuo; University of Texas, Dept. of Mgmt., Austin, TX 78712;

Panel: Discussion on Teaching Product Development

MD06.1 Panel: Teaching Product Development

• Abbie Griffin; University of Illinois, 350 Commerce West, Champaign, IL 61820; abbieg@uiuc.edu
• Anil Khurana; Boston University, Sch. of Mgmt., 595 Commonwealth Ave., Boston, MA 02215;
• Viswanthan Krishnan; University of Texas, Dept. of Mgmt., CBA 4.202, Austin, TX 78712; krishnan@mail.utexas.edu
• Susan Walsh Sanderson; National Science Foundation, Innovation & Org. Change Prog., 4201 Wilson Blvd., Arlington, VA 22230; ssanders@nsf.gov
• Stefan Thomke; Harvard Business School, Morgan Hall T63, Soldiers Field, Boston, MA 02163; sthomke@hbs.edu

Managing Innovation in Health Care

MD23.1 A Model for the Adoption of New Technology in Cardiac Surgery

• Richard Bohmer; Harvard Business School, Morgan Hall T-17, Boston, MA 02163; rbohmer@hbs.edu

MD23.2 The Effects of Team Stability & Teamwork Norms on the Successful Adoption of Innovation in Hospitals

• Amy Edmondson; Harvard Business School, Morgan Hall T-87, Boston, MA 02163; aedmondson@hbs.edu

MD23.3 High Performance Practices under Conditions of Task Interdependence: An Examination of Patient Care

• Jody Hoffer Gittell; Harvard Business School, Beker 185, Soldiers Field, Boston, MA 02138; jgittell@hbs.edu
• Jon A. Chilingerian; Brandeis University, Heller Sch. of Mgmt., Waltham, MA 02554; chilingerian@binah.cc.brandeis.edu

MD23.4 A Study of Process Improvement in Surgical Units & the Influence of Physician Best Practices in CABG Surgery

Panel: Publishing Strategies for Research in Engineering & Technology Management

TA03.1 Panel: Publishing Strategies for Research in Engineering & Technology Management

• Dundar F. Kocaoglu; Portland State University, Engineering Mgmt. Program, Portland, OR 97207-0751; kocaoglu@emp.pdx.edu
• R. Balachandra; Northeastern University, MS Dept., 314 Hayden Hall, Boston, MA 02115; balachand@neu.edu
• Frederick Betz; University of Maryland, Grad. Sch. of Mgmt. & Tech., University Blvd. at Adelphi, College Park, MD 20742-7043; fbetz@polaris.umuc.edu
• Alok K. Chakrabarti; NJIT, Sch. of Management, Unversity Heights, Newark, NJ 07102-1982; chakraba@megahertz.njit.edu
• Burton V. Dean; San Jose State University, Dept. of Org. & Mgmt., 1 Washington Square, San Jose, CA 95192-0070; bvdean@aol.com
• George F. Farris; Rutgers University, Grad. Sch. of Mgmt., 180 University Ave., Newark, NJ 07102-1897; gfarris@gsmack.rutgers.edu
• Cheryl Gaimon; Georgia Institute of Technology, DuPree Sch. of Mgmt., Atlanta, GA 30332-0520; cheryl.gaimon@mgt.gatech.edu
• Jeffrey K. Liker; University of Michigan, Dept. of IOE, 1520 McIntyre Dr., Ann Arbor, MI 48105; liker@engin.umich.edu
• Ilze Zigurs; University of Colorado, College of Business Admin., CB 419, Boulder, CO 80309-0419; zigurs@colorado.edu

The Editor-in-Chief and department editors of IEEE Transactions on Engineering Management will discuss the journal's philosophy and strategies in this panel discussion. A Q&A period will follow the brief presentations.

Information Technology Management within Operations

TA23.1 Measurement & Control of Knowledge Work Activities: Preliminary Results from a Field Study of IT Metrics Programs

• Rob Austin; Harvard Business School, Morgan Hall T91, Soldiers Field, Boston, MA 02163; raustin@hbs.edu

TA23.2 Enterprise Resource Planning Systems Adoption in Europe

• Enver Yucesan; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77305 , France;
• Luk N. Van Wassenhove; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77305 , France; luk.van.wassenhove@insead.fr

TA23.3 Determinants & Performance Implications of Electronic Coordination by Engineers in Buyer-Supplier Relationships: Test Contingency Hypothese

• Ben M. Bensaou; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77309 , France;

TA23.4 Coordination Requirements, Information Infrastructure & the Optimal Design of Organizations

• Eric Clemons; University of Pennsylvania, The Wharton Sch., 1315 Locust Walk, SH-DH 6366, Philadelphia, PA 19104; clemons@wharton.upenn.edu

TA23.5 The Performance Impact of ERP: An Empirical Observation

• Andrew McAfee; Harvard Business School, Morgan Hall T40, Soldiers Field, Boston, MA 02163; amcafee@hbs.edu

Results are presented from a natural experiment which had as its treatment the adoption of an ERP information system within a high-tech manufacturing facility. System adoption occurred without significant contemporaneous business process changes. After an initial dip, operational performance improved along a classic learning curve.

Innovation Networks

TC23.1 Collaborative Learning in Alliances: University Industry Interactions

• Suzanne Stout; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77305 , France; skstout@ibm.net
• David McKendrick; University of California, IR/PS 0519, 9500 Gilman Dr., La Jolla, CA 92093-0519;
• Michael T. Pich; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77305 , France; michael.pich@insead.fr

TC23.2 Innovation Networks

• Michael T. Pich; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77305 , France; michael.pich@insead.fr
• Suzanne Stout; INSEAD, Boulevard de Constance, Fontainebleau Cedex, 77305 , France; skstout@ibm.net
• David McKendrick; University of California, IR/PS 0519, 9500 Gilman Dr., La Jolla, CA 92093-0519;

TC23.3 Leveraging Supplier Innovation

• Tim Laseter; University of Virginia, Darden Grad. Sch. of Bus., PO Box 6550, Charlottesville, VA 22906; lasetertimothy@bah.com