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Constructing supplier learning curves to evaluate relational gain in engineering projects

Gosling, Jonathan ORCID:, Abouarghoub, Wessam ORCID:, Naim, Mohamed ORCID: and Moone, Brian 2019. Constructing supplier learning curves to evaluate relational gain in engineering projects. Computers and Industrial Engineering 131 , pp. 502-514. 10.1016/j.cie.2018.05.008

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Learning curve models analyze how experience relates to performance over time, and rates vary widely across industries, organisations, and processes. Very little empirical research addresses learning curves for suppliers in engineering projects. Hence, we consider learning within strategically managed buyer-supplier relationships, focusing on the potential for ‘relational gain’ in terms of learning performance for longer term strategic suppliers (LTSP). A mixed-method approach is adopted employing 2 phases. First, empirical performance data is collected as well as contextual information regarding the managerial actions behind the learning curve. Second, the empirical data is analysed using the time constant learning curve model. Different supplier relational categories are mobilised to assess any relational gain in the rates of learning. The findings give learning parameters across different categories, showing that LTSPs outperform other less collaborative categories across a range of learning and performance dimensions. Suppliers who are not formally part of the firm’s development programme, but are requested by clients or have specialist capabilities, do not perform well at the outset of a project but accelerate rapidly along the learning curve. There is a need for project based organizations to both strategically manage long term relationships with suppliers, as well as quickly integrate new suppliers. Better performing learning curves act as benchmarks to show the relative relational gains to be achieved by investment and integration approaches.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: Elsevier
ISSN: 0360-8352
Date of First Compliant Deposit: 18 May 2018
Date of Acceptance: 9 May 2018
Last Modified: 10 Nov 2023 18:00

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