TY - JOUR
T1 - Autonomation policy to control work-in-process inventory in a smart production system
AU - Dey, Bikash Koli
AU - Pareek, Sarla
AU - Tayyab, Muhammad
AU - Sarkar, Biswajit
N1 - Publisher Copyright:
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - The necessity of optimum safety stock is really essential for any smart production system. For this reason, the effect of autonomation policy makes a big difference with the basic traditional automation policy. Basically, for a long-run production system, a process may transfer to an ‘out-of-control’ state from an ‘in-control’ state due to labour problems, machinery problems, or any kind of energy problems. During this ‘out-of-control’ state, machines produced imperfect items instead of perfect items. As a result, an inspection is required to identify the imperfect ones. Until now, this inspection has been utilised by human beings through the traditional automation policy and inspection errors may occur. To perform an error-free inspection, an autonomation policy is examined in this model to detect imperfect items from the production process, which makes the process smarter. The defective rate is random and follows a certain distribution. A budget and a space constraints are adopted, which makes the model non-linear with a constraint problem. Contradictory to the existing literature, the demand is price- and quality-sensitive together in a smart production system. To solve this non-linear problem with an optimised value of backorders, number of delivery lots, safety factors, and collection rate, a non-linear optimisation technique (Khun–Tucker optimisation technique) is employed. A numerical example and sensitivity analysis are provided to illustrate the model. The result finds that the optimum autonomation policy can save work-in-process inventory at the optimum value of the decision variable in the proposed model.
AB - The necessity of optimum safety stock is really essential for any smart production system. For this reason, the effect of autonomation policy makes a big difference with the basic traditional automation policy. Basically, for a long-run production system, a process may transfer to an ‘out-of-control’ state from an ‘in-control’ state due to labour problems, machinery problems, or any kind of energy problems. During this ‘out-of-control’ state, machines produced imperfect items instead of perfect items. As a result, an inspection is required to identify the imperfect ones. Until now, this inspection has been utilised by human beings through the traditional automation policy and inspection errors may occur. To perform an error-free inspection, an autonomation policy is examined in this model to detect imperfect items from the production process, which makes the process smarter. The defective rate is random and follows a certain distribution. A budget and a space constraints are adopted, which makes the model non-linear with a constraint problem. Contradictory to the existing literature, the demand is price- and quality-sensitive together in a smart production system. To solve this non-linear problem with an optimised value of backorders, number of delivery lots, safety factors, and collection rate, a non-linear optimisation technique (Khun–Tucker optimisation technique) is employed. A numerical example and sensitivity analysis are provided to illustrate the model. The result finds that the optimum autonomation policy can save work-in-process inventory at the optimum value of the decision variable in the proposed model.
UR - http://www.scopus.com/inward/record.url?scp=85079407568&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079407568&partnerID=8YFLogxK
U2 - 10.1080/00207543.2020.1722325
DO - 10.1080/00207543.2020.1722325
M3 - Article
AN - SCOPUS:85079407568
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
ER -