Abstract
Industry 4.0 is providing unprecedented opportunities for the capture and use of data into production planning and control (PPC). The accuracy of such data for PPC has been found to have a direct positive effect on operational performance. This study builds on a dynamic approach where production feedback data is used to improve the accuracy of master data used in tactical planning. The study applies a model-based approach using data from a real case. Two illustrative sensitivity analyses indicate that even small deviations in the accuracy of master data have an impact on the production schedule in terms of job sequence and makespan. The paper's main theoretical contribution is the development of six propositions on this relationship, where in short, the sequence appears to be sensitive to the accuracy of both changeover time and processing time. The paper illustrates how sensitivity analysis can be used in investment decisions about which production feedback data to capture and use for PPC purposes. Further research should test the propositions in more real cases and other production environments and carry out sensitivity analyses with more types of master data, variables, and combinations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bueno, A.F., Godinho Filho, M., Frank, A.G.: Smart production planning and control in the Industry 4.0 context: a systematic literature review. Comput. Indust. Eng. 106774 (2020)
Oluyisola, O.E., Sgarbossa, F., Strandhagen, J.O.: Smart production planning and control: concept, use-cases and sustainability implications. Sustainability 12(9), 3791 (2020)
Oluyisola, O.E., et al.: Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study. J. Intell. Manufac. 33(1), 311–332 (2022)
Schäfers, P., Mütze, A., Nyhuis, P.: Integrated concept for acquisition and utilization of production feedback data to support production planning and control in the age of digitalization. Procedia Manufac. 31, 225–231 (2019)
Schuh, G., et al.: Achieving higher scheduling accuracy in production control by implementing integrity rules for production feedback data. Procedia CIRP 19, 142–147 (2014)
Reuter, C., Brambring, F.: Improving data consistency in production control. Procedia CIRP 41, 51–56 (2016)
Chae, B.K., et al.: The impact of advanced analytics and data accuracy on operational performance: a contingent resource based theory (RBT) perspective. Decis. Support Syst. 59, 119–126 (2014)
Oluyisola, O.E.: Towards Smart Production Planning and Control: Frameworks and case studies investigating the enhancement of production planning and control using internet-of-things, data analytics and machine learning. NTNU (2021)
Schuh, G., et al.: Increasing data integrity for improving decision making in production planning and control. CIRP Ann. 66(1), 425–428 (2017)
Rahmani, M., et al.: Smart production planning and control; concept for improving planning quality with production feedback data. In: Alfnes, E., et al.(eds.) IFIP Advances in Production Management Systems: Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures, forthcoming, Springer
Slack, N., Brandon-Jones, A., Johnston, R.: Operations Management, 7th edn. Pearson (2013)
Jacobs, F.R., et al.: Manufacturing planning and control for supply chain management: APICS/CPIM Certification Edition. McGraw-Hill Education (2011)
Abd Elmonem, M.A., Nasr, E.S., Geith, M.H.: Benefits and challenges of cloud ERP systems–a systematic literature review. Future Comput. Inform. J. 1(1-2), 1–9 (2016)
Arica, E., Powell, D.J.: A framework for ICT-enabled real-time production planning and control. Adv. Manufac. 2(2), 158–164 (2014)
Klaus, H., Rosemann, M., Gable, G.G.: What is ERP? Inf. Syst. Front. 2(2), 141–162 (2000)
Sagegg, O.J., Alfnes, E.: ERP Systems for Manufacturing Supply Chains: Applications, Configuration, and Performance. CRC Press (2020)
Jakubiak, M.: The Concept of Minimizing Master Data in The Production Planning Process on The Example of ERP Software (2021)
Knolmayer, G.F., Röthlin, M.: Quality of material master data and its effect on the usefulness of distributed ERP systems. In: Roddick, J.F., et al. (eds.) ER 2006. LNCS, vol. 4231, pp. 362–371. Springer, Heidelberg (2006). https://doi.org/10.1007/11908883_43
Geiger, F., Reinhart, G.: Knowledge-based machine scheduling under consideration of uncertainties in master data. Prod. Eng. 10, 197–207 (2016)
Rahmani, M., et al.: 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 (2022)
De Man, J.C., Strandhagen, J.O.: Spreadsheet application still dominates enterprise resource planning and advanced planning systems. Ifac-Papersonline 51(11), 1224–1229 (2018)
Pinedo, M.L.: Scheduling, vol. 29. Springer (2012)
Kempf, K., et al.: Evaluation and comparison of production schedules. Comput. Ind. 42(2–3), 203–220 (2000)
Demir, Y., İşleyen, S.K.: Evaluation of mathematical models for flexible job-shop scheduling problems. Appl. Math. Model. 37(3), 977–988 (2013)
Chaudhry, I.A., Khan, A.A.: A research survey: review of flexible job shop scheduling techniques. Int. Trans. Oper. Res. 23(3), 551–591 (2016)
Choi, I.-C., Choi, D.-S.: A local search algorithm for jobshop scheduling problems with alternative operations and sequence-dependent setups. Comput. Ind. Eng. 42(1), 43–58 (2002)
Shen, X.-N., Yao, X.: Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems. Inf. Sci. 298, 198–224 (2015)
Brucker, P., Schlie, R.: Job-shop scheduling with multipurpose machines. Computing (1990)
Xie, J., et al.: Review on flexible job shop scheduling. IET Collabor. Intell. Manufac. 1(3), 67–77 (2019)
Framinan, J.M., Fernandez-Viagas, V., Perez-Gonzalez, P.: Using real-time information to reschedule jobs in a flowshop with variable processing times. Comput. Ind. Eng. 129, 113–125 (2019)
Fernandez-Viagas, V., Framinan, J.M.: Exploring the benefits of scheduling with advanced and real-time information integration in Industry 4.0: a computational study. J. Indust. Inform. Integrat. 27, 100281 (2022)
Koh, L., Orzes, G., Jia, F.J.: The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management. Int. J. Oper. Product. Manage. 39(6/7/8), 817–828 (2019)
Shen, L., Dauzère-Pérès, S., Neufeld, J.S.: Solving the flexible job shop scheduling problem with sequence-dependent setup times. Eur. J. Oper. Res. 265(2), 503–516 (2018)
Acknowledgements
The research presented in this paper was conducted as part of the DigiMat project, with financial support from NTNU, the participating companies, and the Research Council of Norway.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 IFIP International Federation for Information Processing
About this paper
Cite this paper
Rahmani, M., Romsdal, A., Syversen, Ø.A.M., Sgarbossa, F., Strandhagen, J.O. (2023). Production Scheduling Using Production Feedback Data; An Illustrative Case Study. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-031-43670-3_59
Download citation
DOI: https://doi.org/10.1007/978-3-031-43670-3_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43669-7
Online ISBN: 978-3-031-43670-3
eBook Packages: Computer ScienceComputer Science (R0)
