A sustainable supply chain coordination model with an investment in green technology and electric equipment

Abstract

This paper proposes a new mathematical model to examine a solution to a sustainable supply chain problem by investing in green production technology and electrical equipment for transportation and warehousing. This study integrated an inventory model involving a single vendor, a single distributor, and multiple retailers. The carbon emissions involving emissions from a vendor and retailers are incorporated in this paper. An algorithm solution is proposed to minimise the total cost to determine the optimal decision variables, i.e. the lot size, number of deliveries, safety factor, lead-time, and emissions. A numerical example is illustrated to validate the proposed model and provide insight into the relevant industry. The life cycle cost assessment of the electrical equipment is also discussed for managers to inform their decision-making. From the comparison results, the integrated scheme comprising electric-powered with green investment is lower than without green investment, fossil-fuel with green investment, and fossil fuel equipment only with a total cost saving of 0.68%, 3.28%, and 3.59%, respectively; lower-emissions of 39.69%, 42.22%, 27.46%, respectively; and lead-time reduction of 1.80%, 25.23%, 23.78%, respectively.

KEYWORDS:

• A sustainable integrated inventory model
• green production technology investment
• electrical equipment
• carbon emission
• life cycle cost assessment

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ivan Darma Wangsa

Ivan Darma Wangsa received his B.Eng. degree in Industrial Engineering from Universitas Islam Indonesia (UII). He obtained his M.Sc. degree in Industrial Engineering and Management from Bandung Institute of Technology (ITB) in Indonesia. He earned his Doctoral degree in Industrial and Systems Engineering from Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. Ivan D. Wangsa has 10 years of work experience and consulting in HSSE Department at the State-owned Enterprise of Indonesia. He also holds Certified Professional in Logistics Management (CPLM) from ISCEA (USA) and Certified Associates in Supply Chain (CASC) from ISCQA. His research interests include inventory modeling, logistics and supply chain management, sustainable management, and safety engineering.

Iwan Vanany

Iwan Vanany is a Professor in the Department of Industrial and Systems Engineering at Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. His research interests are in food supply chain management, business process management, and halal operations and supply chain. He has published in International Journal of Information System and Supply Chain Management, Meiji Business Journal, Supply Chain Forum: An International Journal, International Journal Logistics Systems and Management, Journal of Islamic Marketing, International Journal of Lean Six Sigma, British Food Journal, IEEE Transaction and Engineering Management, Food Control, and others. He teaches business process reengineering, supply chain management, enterprise resources planning (ERP),- logistics system, productions and planning control, transportation, and warehouse management, and purchasing management.

Nurhadi Siswanto

Nurhadi Siswanto is a faculty member and the Head of Department of Industrial and Systems Engineering at Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. He earned his bachelor’s degree in Industrial Engineering fromInstitut Teknologi Sepuluh Nopember, Surabaya, Indonesia, a master’s degree in industrial engineering from Purdue University, West Lafayette, IN, USA and a Ph.D. from University of New South Wales, Canberra, Australia. His research interests include operation research, large-scale optimization, simulation, vehicle routing, inventory routing and modeling ofmaritime transportation. He has taught courses in Operations Research, Decision Analysis, Discrete Event Simulation, Simulation Modeling and Quantitative Modeling.