An application of a smart production system to control deteriorated inventory. (English) Zbl 1531.90031
Summary: Deteriorating products require different handling procedures. Handling procedures includes prevention of the natural deterioration rate of the product. The production of deteriorating products requires prevention technology for those products to use for a long time. Overproduction of deteriorating types of products causes more trouble in preventing deterioration. This study uses a smart production system to control the production of deteriorating products. A controllable production rate controls the production of deteriorating products, and preservation technology reduces the deterioration rate of products. Preservation technology helps extend the life of products, but it requires a specific temperature controlled environment to work at maximum efficiency. Transportation of these products uses refrigerated transportation to maintain the quality during the transportation time. The purpose of using all these features for deteriorating products is to reduce the deterioration rate, which helps to reduce waste generation from production. Besides, imperfect products from the production system pass through a remanufacturing process to support the waste reduction process. A sustainable supply chain management model under the above-stated strategies is described here. Classical optimization is used to find the global optimum solution of the objective function. Then, the total cost of the supply chain is optimized using unique solutions of production rate, number of deliveries, delivery lot size, system reliability, and preservation investment. Global optimum solutions are established theoretically, and few propositions are developed. Some special cases, case studies, and a comparison graph are provided to validate the results. The beta distribution provides the minimum total cost of the system than uniform, gamma, triangular, and double triangular distribution. Smart production allows 72% system reliability with negligible imperfect products. Besides, the proposed policy gains 22.72% more profit than the existing literature. The model is more realistic through convex 3D graphs, sensitivity analyses, and managerial insights.
MSC:
| 90B05 | Inventory, storage, reservoirs |
| 90B06 | Transportation, logistics and supply chain management |
| 90B30 | Production models |
| 90C30 | Nonlinear programming |
| 90C31 | Sensitivity, stability, parametric optimization |
Keywords:
smart production system; system reliability; supply chain management; remanufacturing; preservation technologyReferences:
| [1] | M. Abuhelwa, W.A. Salah and M.J.K. Bashir, Potential energy production from organic waste and its environmental and economic impacts at a tertiary institution in Palestine. Environ. Qual. Manag. (2023). DOI: 10.1002/tqem.21960 · doi:10.1002/tqem.21960 |
| [2] | S. Adak and G.S. Mahapatra, Effect of reliability on multi-item inventory system with shortages and partial backlog incorpo-rating time dependent demand and deterioration. Ann. Oper. Res. 315 (2022) 1551-1571. |
| [3] | P. Alavian, E. Yongsoon, S.M. Semyon and L. Zhang, Smart production systems: automating decision-making in manufacturing environment. Int. J. Prod. Res. 58 (2020) 828-845. |
| [4] | F. Angizeh, H. Montero, A. Vedpathak and M. Parvania, Optimal production scheduling for smart manufacturers with appli-cation to food production planning. Comput. Elec. Eng. 84 (2020) 106609. |
| [5] | A. Debnath and B. Sarkar, Effect of circular economy for waste nullification under a sustainable supply chain management. J. Clean. Prod. 385 (2023) 135477. |
| [6] | M. Ben-Daya, R. As’ad and K.A. Nabi, A single-vendor multi-buyer production remanufacturing inventory system under a centralized consignment arrangement. Comput. Ind. Eng. 135 (2019) 10-27. |
| [7] | A.I. Malik, B. Sarkar, M.W. Iqbal, M. Ullah, I. Khan and M.B. Ramzan, Coordination supply chain management in flexible production system and service level constraint: A Nash bargaining model. Comp. Indust. Eng. 177 (2023) 109002. |
| [8] | H. Cañas, J. Mula, F. Campuzano-Bolarín, and R. Poler, A conceptual framework for smart production planning and control in Industry 4.0. Comput. Ind. Eng. 173 (2022) 108659. |
| [9] | J. Chaab and O.C. Demirag, Effects of consumer loyalty and product web compatibility on cooperative advertising and pricing policies in a dual-channel supply chain. RAIRO: OR 56 (2022) 2557-2580. · Zbl 1498.90018 |
| [10] | S.Y. Chang, and T.Y. Yeh, A two-echelon supply chain of a returnable product with fuzzy demand. Appl. Math. Model. 37 (2013) 4305-4315. · Zbl 1270.90011 |
| [11] | U. Chaudhari, A. Bhadoriya, M.Y. Jani and B. Sarkar, A generalized payment policy for deteriorating items when demand depends on price, stock, and advertisement under carbon tax regulations. Math. Comput. Simul. 207 (2023) 556-574. · Zbl 1540.90021 |
| [12] | S.C. Das, A.M. Zidan, A.K. Manna, A.A. Shaikh and A.K. Bhunia, An application of preservation technology in inventory control system with price dependent demand and partial backlogging. Alex. Eng. J. 59 (2020) 1359-1369. |
| [13] | Y. Emamian, I.N. Kamalabadi and A. Eydi, Developing and solving an integrated model for production routing in sustainable closed-loop supply chain. J. Clean. Prod. 302 (2021) 126997. |
| [14] | C.B. Gabler, V.M. Landers, R. Agnihotri and T.R. Morgan, Environmental orientation on the frontline: a boundaryspanning perspective for supply chain management. J. Bus. Logist. (2023). DOI: 10.1111/jbl.12328. · doi:10.1111/jbl.12328 |
| [15] | P. Gautam, S. Maheshwari, A. Hasan, A. Kausar and C.K. Jaggi, Optimal inventory strategies for an imperfect production system with advertisement and price reliant demand under rework option for defectives. RAIRO: OR 56 (2022) 183-197. · Zbl 1483.90016 |
| [16] | J. Heydari, K. Govindan and R. Sadeghi, Reverse supply chain coordination under stochastic remanufacturing capacity. Int. J. Prod. Econ. 202 (2018) 1-11. |
| [17] | B. Sarkar and R. Guchhait, Ramification of information asymmetry on a green supply chain management with the cap-trade, service, and vendor-managed inventory strategies. Elect. Comm. Res. App. 60 (2023) 101274. |
| [18] | J. Huo, M. Zhang, D. Wang, A.S. Mujumdar, B. Bhandari and L. Zhang, New preservation and detection technologies for edible mushrooms: a review. J. Sci. Food Agric. (2023). DOI: 10.1002/jsfa.12472. · doi:10.1002/jsfa.12472 |
| [19] | I. Kazancoglu, M. Ozbiltekin-Pala, S.K. Mangla, Y. Kazancoglu, and F. Jabeen, Role of flexibility, agility and responsiveness for sustainable supply chain resilience during COVID-19. J. Clean. Prod. 362 (2022) 132431. |
| [20] | M. Kerin and D.T. Pham, A review of emerging industry 4.0 technologies in remanufacturing. J. Clean. Prod. 237 (2019) 117805. |
| [21] | G. Li, X. He, J. Zhou and H. Wu, Pricing, replenishment and preservation technology investment decisions for non-instantaneous deteriorating items. Omega 84 (2019) 114-126. |
| [22] | S. Saha, B. Sarkar and M. Sarkar, Application of improved meta-heuristic algorithms for green preservation technology man-agement to optimize dynamical investments and replenishment strategies. Math. Comp. Simul. 209 (2023) 426-450. · Zbl 1540.90011 |
| [23] | U. Mishra, J.Z. Wu and B. Sarkar, A sustainable production-inventory model for a controllable carbon emissions rate under shortages. J. Clean. Prod. 256 (2020) 120268. |
| [24] | U.M. Modibbo, M. Arshad, O. Abdalghani and I. Ali, Optimization and estimation in system reliability allocation problem. Reliab. Eng. Syst. Saf. 212 (2021) 107620. |
| [25] | B. Mridha, S. Pareek, A. Goswami and B. Sarkar, Joint effects of production quality improvement of biofuel and carbon emissions towards a smart sustainable supply chain management. J. Clean. Prod. 386 (2023) 135629. |
| [26] | B. Sarkar, H. Seok, T.K. Jana and B.K. Dey, Is the system reliability profitable for retailing and consumer service of a dynamical system under cross-price elasticity of demand? J. Retail. Consum. Serv. 75 (2023) 103439. |
| [27] | U. Mishra, J.Z. Wu and B. Sarkar, Optimum sustainable inventory management with backorder and deterioration under controllable carbon emissions. J. Clean. Prod. 279 (2021) 123699. |
| [28] | S. Ouaret, J.P. Kennè and A. Gharbi, Stochastic optimal control of random quality deteriorating hybrid manufactur-ing/remanufacturing systems. J. Manuf. Syst. 49 (2018) 172-185. |
| [29] | B. Pal, A. Mandal and S.S. Sana, Two-phase deteriorated supply chain model with variable demand and imperfect production process under two-stage credit financing. RAIRO: OR 55 (2021) 457-480. |
| [30] | D. Yadav, R. Kumari, N. Kumar and B. Sarkar. Reduction of waste and carbon emission through the selection of items with crossprice elasticity of demand to form a sustainable supply chain with preservation technology. J. Clean. Prod. 297 (2021) 126298. |
| [31] | M. Pervin, S.K. Roy, P. Sannyashi and G.W. Weber, Sustainable inventory model with environmental impact for non-instantaneous deteriorating items with composite demand. RAIRO: OR 57 (2023) 237-261. · Zbl 1515.90020 |
| [32] | M. Rahaman, S.P. Mondal, S. Alam and S.K. De, A study of a lock fuzzy EPQ model with deterioration and stock and unit selling price-dependent demand using preservation technology. Soft Comput. 26 (2022) 2721-2740. |
| [33] | M. Rahmani, A. Romsdal, F. Sgarbossa, J.O. Strandhagen and M. Holm, Towards smart production planning and control; a conceptual framework linking planning environment characteristics with the need for smart production planning and control. Ann. Rev. Control 53 (2022) 370-381. |
| [34] | T. Ramesh, U. Hariram, A. Srimagal and J.K. Sahu, Applications of light emitting diodes and their mechanism for food preservation. J. Food Saf. (2023) e13040. |
| [35] | C. Rout, A. Paul, R.S. Kumar, D. Chakraborty and A. Goswamia, Cooperative sustainable supply chain for deteriorating item and imperfect production under different carbon emission regulations. J. Clean. Prod. 272 (2020) 122170. |
| [36] | T. Roy and K.S. Chaudhuri, A production-inventory model under stock-dependent demand, Weibull distribution deterioration and shortage. Int. Trans. Oper. Res. 16 (2009) 325-346. · Zbl 1171.90320 |
| [37] | L. Sahoo, Transportation problem in Fermatean fuzzy environment. RAIRO: OR 57 (2023) 145-156. · Zbl 1514.90260 |
| [38] | M. Sarkar and B.D. Chung, Flexible work-in-process production system in supply chain management under quality improve-ment. Int. J. Prod. Res. 58 (2020) 3821-3838. |
| [39] | B. Sarkar, M. Sarkar, B. Ganguly and L.E. C´rdenas-Barr´n, Combined effects of carbon emission and production quality improvement for fixed lifetime products in a sustainable supply chain management. Int. J. Prod. Econ. 231 (2021) 107867. |
| [40] | M. Ullah, I. Asghar, M. Zahid, M. Omair, A. Alarjani and B. Sarkar, Ramification of remanufacturing in a sustainable three-echelon closed-loop supply chain management for returnable products. J. Clean. Prod. 290 (2021) 125609. |
| [41] | A.S.H. Kugele and B. Sarkar, Reducing carbon emissions of a multi-stage smart production for biofuel towards sustainable development. Alex. Eng. J. 70 (2023) 93-113. |
| [42] | M. Ullah and B. Sarkar, Recovery-channel selection in a hybrid manufacturing-remanufacturing production model with RFID and product quality. Int. J. Prod. Econ. 219 (2020) 360-374. |
| [43] | M. Sebatjane, The impact of preservation technology investments on lot-sizing and shipment strategies in a three-echelon food supply chain involving growing and deteriorating items. Oper. Res. Perspect. 9 (2022) 100241. |
| [44] | M. Sharma, A. Dhir, H. AlKatheeri, M. Khan and M.M. Ajmal, Greening of supply chain to drive performance through logical integration of supply chain resources. Bus. Strategy Environ. (2023). DOI: 10.1002/bse.3340 · doi:10.1002/bse.3340 |
| [45] | D. Tan, M. Suvarna, Y.S. Tan, J. Li and X. Wang, A three-step machine learning framework for energy profiling, activity state pblackiction and production estimation in smart process manufacturing. Appl. Energy 291 (2021) 116808. |
| [46] | M. Ullah, B. Sarkar and I. Asghar, Effects of preservation technology investment on waste generation in a two-echelon supply chain model. Mathematics 7 (2019) 189. |
| [47] | C. Wang and L. Jiang, Inventory policy for deteriorating seasonal products with price and ramp-type time dependent demand. RAIRO: OR 49 (2015) 865-878. · Zbl 1333.90013 |
| [48] | B. Sarkar, M. Tayyab, N. Kim and M.S. Habib, Optimal production delivery policies for supplier and manufacturer in a constrained closed-loop supply chain for returnable transport packaging through metaheuristic approach. Comp. Indust. Eng. 135 (2019) 987-1003. |
| [49] | Y. Yan, F. Yao and J. Sun, Manufacturer’s cooperation strategy of closed-loop supply chain considering corporate social responsibility. RAIRO: OR 55 (2021) 3639-3659. · Zbl 1484.90018 |
| [50] | P. Zhao, Q. Deng, J. Zhou, W. Han, G. Gong and C. Jiang, Optimal production decisions for remanufacturing end-of-life products under quality uncertainty and a carbon cap-and-trade policy. Comput. Ind. Eng. 162 (2021) 107646. |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
