Abstract
Purpose: The purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered. Design/methodology/approach: A mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy. Findings: This study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided. Practical implications: This study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors. Originality/value: This study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.
Original language | English |
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Pages (from-to) | 399-426 |
Number of pages | 28 |
Journal | Journal of Enterprise Information Management |
Volume | 34 |
Issue number | 1 |
Early online date | 11 Jun 2020 |
DOIs | |
Publication status | Published - 28 Jan 2021 |
Keywords
- HRM
- Imperfect quality components
- Inspection error
- Optimal production quantity
- Supplier selection
- Sustainable supply chain
ASJC Scopus subject areas
- General Decision Sciences
- Information Systems
- Management of Technology and Innovation