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A quantified Kraljic Portfolio Matrix: Using decision analysis for strategic purchasing

Published on Oct 1, 2017in Journal of Purchasing and Supply Management3.09
· DOI :10.1016/j.pursup.2017.10.002
Robert T. Montgomery1
Estimated H-index: 1
(USAFA: United States Air Force Academy),
Jeffrey A. Ogden1
Estimated H-index: 1
(UNT: University of North Texas),
Bradley C. Boehmke4
Estimated H-index: 4
(AFIT: Air Force Institute of Technology)
Abstract
Abstract Since its inception, the Kraljic Portfolio Matrix (KPM) has been widely used as a diagnostic and prescriptive purchasing tool. One of the primary weaknesses of the KPM is the qualitative nature of the model, which results in a subjective method for weighting and positioning suppliers or commodities in the various quadrants. In an effort to help overcome this widely cited weakness, this research proposes an objective, quantitative decision analysis approach to position products and services within the KPM. Single attribute value functions are used to quantify the strategic purchasing attributes of the KPM. A multi attribute value function is then used to quantify and rank-order products and services within the KPM based on the two strategic purchasing objectives. We empirically demonstrate this approach using data from an organization with a $10 billion purchasing portfolio. Furthermore, we illustrate how a cost-benefit view of the results help decision makers quickly and easily identify the products and services most conducive to the implementation of commercial best practice purchasing strategies. A sensitivity analysis advocates the robustness of our approach.
  • References (75)
  • Citations (6)
References75
Newest
#1Bradley C. Boehmke (AFIT: Air Force Institute of Technology)H-Index: 4
#2Alan W. Johnson (AFIT: Air Force Institute of Technology)H-Index: 7
Last.Mark A. GallagherH-Index: 6
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#1Sai S. Nudurupati (MMU: Manchester Metropolitan University)H-Index: 16
#2Arijit Bhattacharya (Brunel University London)H-Index: 15
Last.Nicholas Caton (MMU: Manchester Metropolitan University)H-Index: 1
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#1Iris Heckmann (CIT: Center for Information Technology)H-Index: 4
#2Tina Comes (University of Agder)H-Index: 12
Last.Stefan Nickel (CIT: Center for Information Technology)H-Index: 32
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#1Anne Touboulic (Cardiff University)H-Index: 6
#2Daniel Chicksand (Aston University)H-Index: 14
Last.Helen Lisbeth Walker (Cardiff University)H-Index: 26
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Cited By6
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#1Arad Ghanbarizadeh (UT: University of Tehran)
#2Jafar Heydari (UT: University of Tehran)H-Index: 17
Last.Ali Bozorgi-Amiri (UT: University of Tehran)H-Index: 11
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#1Sangho Chae (ASU: Arizona State University)H-Index: 2
#2Carlos Mena (ASU: Arizona State University)
Last.Robert Wiedmer (ASU: Arizona State University)
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#1Davide Aloini (UniPi: University of Pisa)H-Index: 10
#2Riccardo Dulmin (UniPi: University of Pisa)H-Index: 9
Last.Pierluigi Zerbino (UniPi: University of Pisa)H-Index: 2
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#1Felipe Sanchez Garzon (University of Lorraine)
#2Manon Enjolras (University of Lorraine)H-Index: 2
Last.Laure Morel (University of Lorraine)H-Index: 8
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#1Karen A.F. Landale (NPS: Naval Postgraduate School)
#2Aruna Apte (NPS: Naval Postgraduate School)H-Index: 8
Last.Javier Salmerón (NPS: Naval Postgraduate School)H-Index: 12
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