A Four-Stage Framework for the Identification of Information Flow Inefficiencies in the Manufacturing Environment


Article Preview

A four-stage methodological framework useful to analyze the performance of a product development process, with a focus on the information flow inefficiencies is presented. The formal and informal communication exchange between interacting tasks is captured through the Dependency Structure Matrix (DSM) representation, while the properties of the information flow structure are explored calculating metrics of Social Network Analysis (SNA). Information exchange inefficiency of process tasks is calculated performing Data Envelopment Analysis (DEA).



Edited by:

Karol Velíšek, Peter Košťál and František Pecháček




C. lo Storto "A Four-Stage Framework for the Identification of Information Flow Inefficiencies in the Manufacturing Environment", Applied Mechanics and Materials, Vol. 309, pp. 335-341, 2013

Online since:

February 2013




[1] S.L. Brown, K.M. Eisenhardt. Product development: past research, present findings, and future directions. Academy of Management Review, 20 (1995), 343–379.

DOI: https://doi.org/10.2307/258850

[2] T. Fujimoto, K. Clark. Product development performance. Cambridge, MA: Harvard Business School Press (1995).

[3] A. Griffin, J.R. Hauser. Integrating R&D and marketing: a review and analysis of the literature. J. Product Innovation Management, 3 (1996), 191–215.

[4] V. Mahajan, Y. Wind. New product forecasting: Directions for research and implementation. Int. J. Forecasting, 4 (1988), 341–358.

[5] S.H. Thomke. The role of flexibility in the development of new products: an empirical study. Res. Policy, 26 (1997), 105–119.

[6] K.T. Ulrich, S.D. Eppinger. Product design and development. New York: McGraw-Hill (2000).

[7] V. Krishnan, and K.T. Ulrich, Product development decisions: a review of the literature. Management Sci., 47 (2001), 1-21.

[8] E.J. Hultink, A. Griffin, S. Hart, H.S.J. Robben. Industrial new product launch strategies and product development performance. J. Product Innovation Management, 14 (1997), 243–257.

DOI: https://doi.org/10.1111/1540-5885.1440243

[9] J. Kim, D. Wilemon, Sources and assessment of complexity in NPD projects, R&D Management, 33 (2003), 16–30.

[10] R.P. Smith, S.D. Eppinger, Identifying controlling features of engineering design iteration. Management Sci., 43 (1997) 276-293.

[11] R.L. Daft, K.E. Weick. Toward a model of organizations as interpretation systems. Academy of Management Review, 9 (1984), 284–295.

DOI: https://doi.org/10.5465/amr.1984.4277657

[12] J.G. March. Decisions and Organizations. Oxford: Basil Blackwell (1988).

[13] L.C. Freeman. Centrality in social networks: conceptual clarification. Social Networks, 1 (1979), 215-239.

[14] W.W. Cooper, L.M. Seiford, K. Tore. Introduction to Data Envelopment Analysis and its Uses. Springer (2006).

[15] S.P. Borgatti, M.G. Everett, L.C. Freeman. UCINET 6 for Windows: software for social network analysis. Harvard: Analytic Technologies (2002).

[16] R.D. Banker, A. Charnes, W.W. Cooper. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Sci., 30 (1984), 1078-92.

DOI: https://doi.org/10.1287/mnsc.30.9.1078

Fetching data from Crossref.
This may take some time to load.