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Customized orders management in connected make-to-order supply chains

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Abstract

This paper solves the order acceptance and scheduling (OAS) problem of customized products in make-to-order (MTO) supply chains. A new integrated framework that links supply chain operations is presented to overcome uncertainties in order variations and maximize the agility and responsiveness of those systems. A novel mixed integer programming mathematical model is proposed to optimize order acceptance, production planning, maintenance, and transportation decisions. The products are produced based on job-shop scheduling plans while considering the real-time access to available supply and distribution resources. To validate the efficiency of the proposed framework, the model is tested with a four-layer supply chain. Then, a wide range of experiments is implemented to study the effects of different factors such as order uncertainty, costs, maintenance, and customer satisfaction. The results proved that the proposed integrated order acceptance and scheduling can save up to 30% of supply chain expenses with efficient management of the supply, production, and distribution capacities. The results also showed that defining reasonable target customer satisfaction plays an important role in the success of these complex service systems. In addition, a scalability test proved the efficiency of the proposed model for decision making in large systems.

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Data availability

Data sets generated during the current study are available from the corresponding author upon reasonable request.

References

  • Abedi A, Zhu W (2020) An advanced order acceptance model for hybrid production strategy. J Manuf Syst 55:82–93

    Article  Google Scholar 

  • Alami D, ElMaraghy W (2021) A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model. J Manuf Syst 59:81–97

    Article  Google Scholar 

  • Chaurasia SN, Kim JH (2019) An artificial bee colony based hyper-heuristic for the single machine order acceptance and scheduling problem. In: Decision Science in Action. Springer, p 51–63

  • Chaurasia SN, Singh A (2017) Hybrid evolutionary approaches for the single machine order acceptance and scheduling problem. Appl Soft Comput 52:725–747

    Article  Google Scholar 

  • Che Z, Chiang C-J (2010) A modified pareto genetic algorithm for multi-objective build-to-order supply chain planning with product assembly. Adv Eng Softw 41(7–8):1011–1022

    Article  Google Scholar 

  • Chua FLS, Vasnani NN, Pacio LBM et al (2018) A stackelberg game in multi-period planning of make-to-order production system across the supply chain. J Manuf Syst 46:231–246

    Article  Google Scholar 

  • Emami S, Sabbagh M, Moslehi G (2016) A lagrangian relaxation algorithm for order acceptance and scheduling problem: a globalised robust optimisation approach. Int J Comput Integr Manuf 29(5):535–560

    Article  Google Scholar 

  • Fenech C, Perkins B (2015) The Deloitte consumer review. Made-to-order: The rise of mass personalisation. Deloitte Development LLC, pp 1–20

  • Gharehgozli A, Rabbani M, Zaerpour N et al (2008) A comprehensive decision-making structure for acceptance/rejection of incoming orders in make-to-order environments. Int J Adv Manuf Technol 39(9–10):1016–1032

    Article  Google Scholar 

  • Gravel M, Price WL (1988) Using the kanban in a job shop environment. Int J Prod Res 26(6):1105–1118

    Article  Google Scholar 

  • Guo Z, Wong WK, Leung SYS (2013) A hybrid intelligent model for order allocation planning in make-to-order manufacturing. Appl Soft Comput 13(3):1376–1390

    Article  Google Scholar 

  • He L, Guijt A, de Weerdt M et al (2019) Order acceptance and scheduling with sequence-dependent setup times: a new memetic algorithm and benchmark of the state of the art. Comput Ind Eng 138(106):102

    Google Scholar 

  • He Z, Guo Z, Wang J (2019) Integrated scheduling of production and distribution operations in a global mto supply chain. Enterp Inf Syst 13(4):490–514

    Article  Google Scholar 

  • He N, Zhang D, Li Q (2014) Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system. Int J Prod Econ 149:117–130

    Article  Google Scholar 

  • Kalantari M, Rabbani M, Ebadian M (2011) A decision support system for order acceptance/rejection in hybrid mts/mto production systems. Appl Math Model 35(3):1363–1377

    Article  Google Scholar 

  • Katoozian H, Zanjani MK (2022) Supply network design for mass personalization in industry 4.0 era. Int J Prod Econ 244:108349

  • Koyuncu E (2017) A fuzzy mathematical model for order acceptance and scheduling problem. Int J Math Comput Phys Electric Comput Eng 11(4)

  • Kunath M, Winkler H (2019) Usability of information systems to support decision making in the order management process. Procedia CIRP 81:322–327

    Article  Google Scholar 

  • Lalmazloumian M, Wong KY, Govindan K, Kannan D (2016) A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Ann Oper Res 240(2):435–470

    Article  Google Scholar 

  • Lei DM, Cao SQ (2017) Order acceptance and job shop scheduling: a new neighborhood search. In: 2017 Chinese Automation Congress (CAC). IEEE, pp 84–89

  • Leng M, Parlar M (2010) Game-theoretic analyses of decentralized assembly supply chains: Non-cooperative equilibria vs. coordination with cost-sharing contracts. Eur J Oper Res 204(1):96–104

    Article  Google Scholar 

  • Li Z, Wang Y, Wang K-S (2017) Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Adv Manuf 5(4):377–387

    Article  Google Scholar 

  • Li H, Womer K (2012) Optimizing the supply chain configuration for make-to-order manufacturing. Eur J Oper Res 221(1):118–128

    Article  Google Scholar 

  • Li X, Ventura JA (2020) Exact algorithms for a joint order acceptance and scheduling problem. Int J Prod Econ 223(107):516

    Google Scholar 

  • Liu N, Chow PS, Zhao H (2020) Challenges and critical successful factors for apparel mass customization operations: recent development and case study. Ann Oper Res 291(1):531–563

    Article  Google Scholar 

  • Ma J, Tu Y, Feng D (2020) Order acceptance policy for make-to-order supply chain. In: Data Management and Analysis. Springer, pp 67–89

  • Mestry S, Damodaran P, Chen CS (2011) A branch and price solution approach for order acceptance and capacity planning in make-to-order operations. Eur J Oper Res 211(3):480–495

    Article  Google Scholar 

  • Mohagheghian E, Fakhrzad B (2019) A game theory approach to multi-period planning of pricing, ordering, and inventory decisions for a make-to-order manufacturing supply chain. Int J Ind Eng Prod Res 30(1)

  • Ngai EW, Chau DC, Chan T (2011) Information technology, operational, and management competencies for supply chain agility: Findings from case studies. J Strateg Inf Syst 20(3):232–249

    Article  Google Scholar 

  • Nguyen S (2016) A learning and optimizing system for order acceptance and scheduling. Int J Adv Manuf Technol 86(5–8):2021–2036

    Article  Google Scholar 

  • Oguz C, Salman FS, Yalçın ZB et al (2010) Order acceptance and scheduling decisions in make-to-order systems. Int J Prod Econ 125(1):200–211

    Article  Google Scholar 

  • Pan A, Choi T-M (2016) An agent-based negotiation model on price and delivery date in a fashion supply chain. Ann Oper Res 242(2):529–557

    Article  Google Scholar 

  • Perez ATE, Rossit DA, Tohme F et al (2022) Mass customized/personalized manufacturing in industry 4.0 and blockchain: Research challenges, main problems, and the design of an information architecture. Inf Fusion 79:44–57

    Article  Google Scholar 

  • Perret JK, Schuck K, Hitzegrad C (2022) Production scheduling of personalized fashion goods in a mass customization environment. Sustainability 14(1):538

    Article  Google Scholar 

  • Persson F, Olhager J (2002) Performance simulation of supply chain designs. Int J Prod Econ 77(3):231–245

    Article  Google Scholar 

  • Rafiei H, Rabbani M, Alimardani M (2013) Novel bi-level hierarchical production planning in hybrid mts/mto production contexts. Int J Prod Res 51(5):1331–1346

    Article  Google Scholar 

  • Rahman HF, Janardhanan MN, Nielsen IE (2019) Real-time order acceptance and scheduling problems in a flow shop environment using hybrid ga-pso algorithm. IEEE Access 7:112742–112755

    Article  Google Scholar 

  • Sawik T (2011) Selection of a dynamic supply portfolio in make-to-order environment withrisks. Comput Oper Res 38(4):782–796

    Article  Google Scholar 

  • Slotnick SA (2011) Order acceptance and scheduling: a taxonomy and review. Eur J Oper Res 212(1):1–11

    Article  Google Scholar 

  • Stecke KE, Zhao X (2007) Production and transportation integration for a make-to-order manufacturing company with a commit-to-delivery business mode. Manuf Serv Oper Manag 9(2):206–224

    Article  Google Scholar 

  • Tarhan İ, Oğuz C (2021) Generalized order acceptance and scheduling problem with batch delivery: Models and metaheuristics. Comput Oper Res 134(105):414

    Google Scholar 

  • Vasnani N, Chua FL, Pacio LB et al (2018) A stackelberg game on production planning of make-to-order manufacturing supply chain with fuzzy parameters. Manuf Lett 15:22–27

    Article  Google Scholar 

  • Volling T, Spengler TS (2011) Modeling and simulation of order-driven planning policies in build-to-order automobile production. Int J Prod Econ 131(1):183–193

    Article  Google Scholar 

  • Wang B, Wang H (2018) Multiobjective order acceptance and scheduling on unrelated parallel machines with machine eligibility constraints. Math Probl Eng 2018

  • Wang S, Ye B (2019) Exact methods for order acceptance and scheduling on unrelated parallel machines. Comput Oper Res 104:159–173

    Article  Google Scholar 

  • Wang Z, Qi Y, Cui H et al (2019) A hybrid algorithm for order acceptance and scheduling problem in make-to-stock/make-to-order industries. Comput Ind Eng 127:841–852

    Article  Google Scholar 

  • Wen-Ying D, Hui-**u Z, Ke-Tai H (2017) Integrated order acceptance and production scheduling with lead time flexibility. In: 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, pp 1353–1358

  • **ao T, Choi T-M, Cheng T (2016) Delivery leadtime and channel structure decisions for make-to-order duopoly under different game scenarios. Transport Res E Logist Transport Rev 87:113–129

    Article  Google Scholar 

  • Xu L, Wang Q, Huang S (2015) Dynamic order acceptance and scheduling problem with sequence-dependent setup time. Int J Prod Res 53(19):5797–5808

    Article  Google Scholar 

  • Yue J, **a Y, Tran T et al (2009) Using frontier portfolios to improve make-to-order operations. Prod Oper Manag 18(2):226–239

    Article  Google Scholar 

  • Zhang J, Ding G, Zou Y et al (2019) Review of job shop scheduling research and its new perspectives under industry 4.0. J Intell Manuf 30(4):1809–1830

    Article  Google Scholar 

Download references

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The authors did not receive support from any organization for the submitted work.

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Author notes

  1. Nasim Nezamoddini and Mohammad T. Khasawneh both contributed equally to this work.

Authors and Affiliations

  1. Systems Science and Industrial Engineering, Binghamton University, Vestal Parkway, Binghamton, 13902, NY, United States

    Amirhosein Gholami & Mohammad T. Khasawneh

  2. Industrial and Systems Engineering, Oakland University, Library Drive, Rochester, 48309, MI, United States

    Nasim Nezamoddini

Authors
  1. Amirhosein Gholami
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  2. Nasim Nezamoddini
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  3. Mohammad T. Khasawneh
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Correspondence to Amirhosein Gholami.

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Gholami, A., Nezamoddini, N. & Khasawneh, M.T. Customized orders management in connected make-to-order supply chains. Oper Manag Res 16, 1428–1443 (2023). https://doi.org/10.1007/s12063-023-00364-1

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