Expert System Application for Production Planning and Control Optimization in Jobshop Manufacturing: A Review for Developing Economies

Authors

  • Abiodun Gbemileke ABIOYE Osun State University Osogbo,
  • Musibaudeen Olatunde IDRIS
  • Olakunle OLUKAYODE
  • Oriyomi O. ADETAYO
  • Abdulhafiz Ademola ADEFAJO
  • Babajide Joshua OJERINDE

DOI:

https://doi.org/10.21070/r.e.m.v11i2.1869

Keywords:

Expert System (ES); Optimization; Production Planning and Control (PPC); jobshop; Developing economy

Abstract

Production planning and control (PPC) in job-shop manufacturing is complicated by factors such as high product variety, small batch sizes, changing routings, and frequent disruptions. These difficulties are more severe in developing economies, where limited infrastructure, shortage specialist expertise, unreliable energy supply, and financial constraints restrict the adoption of advanced planning systems. This review critically examines the application of expert systems (ESs) to PPC in job shops. It evaluates their capacity to integrate forecasting, manpower planning, energy utilization, machine scheduling, inventory control, cost estimation, and due-date determination. The review follows a sequential process of literature identification, screening, thematic classification, quality appraisal, and synthesis. Its novelty lies in treating these PPC functions as interdependent rather than isolated decisions and in translating  evidence into an explainable, feedback-based ES–PPC architecture designed for the operational realities of small and medium-sized enterprises in developing economies. The synthesis shows that existing studies generally optimize individual functions, while only a limited number integrate rule-based reasoning, optimization models, shop-floor data, and performance feedback within a unified framework. The review therefore proposes an integrated architecture and identifies priorities for real-time adaptation, low-cost deployment, explanation of recommendations, and validation with shop-floor data. These contributions provide a structured foundation for the development of practical ES-enabled PPC systems for job-shop manufacturing.

References

[1] Liaqait, R. A., Hamid, S., Warsi, S. S., & Khalid, A. (2021). A critical analysis of job shop scheduling in context of Industry 4.0. Sustainability, 13(14), 7684. https://doi.org/10.3390/su13147684

[2] Heizer, J., Render, B., & Munson, C. (2023). Operations management: Sustainability and supply chain management (14th ed.). Pearson Education. https://www.pearson.com/nl/en_NL/higher-education/subject-catalogue/business-and-management/heizer-operations-management-sustainability-and-supply-chain.html

[3] Stevenson, W. J. (2021). Operations management (14th ed.). McGraw-Hill Education. https://www.mheducation.com/unitas/highered/changes/stevenson-opertations-management-14e.pdf

[4] Theradapuzha Mathew, N., & Johansson, B. (2023). Production planning and scheduling challenges in the engineer-to-order manufacturing segment: A literature study. International Journal of Innovation, Management and Technology, 14(3), 80-87. https://www.ijimt.org/show-133-1326-1.html

[5] Dauzère-Pérès, S., Ding, J., Shen, L., & Tamssaouet, K. (2024). The flexible job shop scheduling problem: A review. European Journal of Operational Research, 314(2), 409-432. https://doi.org/10.1016/j.ejor.2023.05.017

[6] Bouguessa, S., & Ourari, S. (2026). Job shop scheduling with unit processing times under uncertainties: Sequential flexibility and new lower-bound. Advances in Mechanical Engineering, 18(2), 1-16. https://doi.org/10.1177/16878132251405002

[7] Jamwal, A., Agrawal, R., Sharma, M., & Giallanza, A. (2021). Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions. Applied Sciences, 11(12), 5725. https://doi.org/10.3390/app11125725

[8] Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2022). Enhancing the decision-making process through Industry 4.0 technologies. Sustainability, 14(1), 461. https://doi.org/10.3390/su14010461

[9] Sajjad, A., Eweje, G., & Tappin, D. (2022). Decision-making process development for Industry 4.0 transformation. Advances in Science and Technology Research Journal, 16(1), 182-196. https://doi.org/10.12913/22998624/147237

[10] Brozzi, R., Forti, D., Rauch, E., & Matt, D. T. (2020). The advantages of Industry 4.0 applications for sustainability: Results from a sample of manufacturing companies. Sustainability, 12(9), 3647. https://doi.org/10.3390/su12093647

[11] Spanos, A., De Simone, M., & Entringer, T. C. (2022). Expert systems for small and medium-sized enterprises: A structured review of knowledge-based decision support. Journal of Intelligent Manufacturing. https://link.springer.com/journal/10845

[12] Momenikorbekandi, A., & Kalganova, T. (2025). Intelligent scheduling methods for optimisation of job shop scheduling problems in the manufacturing sector: A systematic review. Electronics, 14(8), 1663. https://doi.org/10.3390/electronics14081663

[13] Zeiträg, Y., & Figueira, J. R. (2023). Automatically evolving preference-based dispatching rules for multi-objective job shop scheduling. Journal of Scheduling, 26(3), 289-314. https://doi.org/10.1007/s10951-023-00783-9

[14] Herrmann, F. (2022). Cyber-physical production planning and control: Feedback integration in Industry 4.0. Procedia CIRP, 107, 45-52. https://doi.org/10.1016/j.procir.2022.05.008

[15] Artiba, A., & Elmaghraby, S. E. (1997). The planning and scheduling of production systems: Methodologies and applications. Springer. https://link.springer.com/book/10.1007/978-1-4615-5709-8

[16] Collier, D. A., & Evans, J. R. (2023). Operations management: Goods, services and value chains (4th ed.). Cengage Learning. https://www.cengage.com/c/operations-management-goods-services-and-value-chains-4e-collier/9780357131695/

[17] Biswas, T., & Baral, R. N. (2021). A review on production planning and control. International Journal of Multidisciplinary Innovative Research, 1(1), 70-78. https://ijmir.org/doc/Vol-1-No-1-2021/Paper_9%20IJMIR%20Vol%201%20Issue%201%20pp.%2070-78.pdf

[18] Andrade, J. H. de, & Fernandes, F. C. F. (2018). Barriers and challenges to improve interfunctional integration between product development and production planning and control in engineering-to-order environments. Gestão & Produção, 25(3), 610-625. https://doi.org/10.1590/0104-530X1759-14

[19] Hazem, H., & Farouk, A. (2016). The application of theory of constraints in a production planning process. Arab Academy for Science, Technology & Maritime Transport. https://aast.edu/

[20] Jeon, S. M., & Kim, G. (2016). A survey of simulation modeling techniques in production planning and control (PPC). Production Planning & Control, 27(5), 360-377. https://doi.org/10.1080/09537287.2015.1128010

[21] Yadav, V., Jain, R., Mittal, M. L., Panwar, A., & Lyons, A. C. (2019). The propagation of lean thinking in SMEs. Production Planning & Control, 30(10-12), 854-865. https://doi.org/10.1080/09537287.2019.1582099

[22] Entringer, T. C., Ferreira, A. da S., Souza, M. V., & Nascimento, D. C. de O. (2018). Reference model for production planning and control systems: A bibliometric analysis and future perspectives. International Journal of Advanced Engineering Research and Science, 5(8), 255-264. https://doi.org/10.22161/ijaers.5.8.29

[23] Buchmeister, B., Palčič, I., & Ojsteršek, R. (2021). Job shop scheduling methods review. In B. Katalinic (Ed.), DAAAM International Scientific Book 2021 (pp. 001-016). DAAAM International. https://doi.org/10.2507/daaam.scibook.2021.01

[24] Velaga, P. (2018). Various approaches to production scheduling in job shops. https://www.researchgate.net/search/publication?q=Various%20approaches%20to%20production%20scheduling%20in%20jobshops

[25] Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Springer. https://doi.org/10.1007/978-3-319-26580-3

[26] Mathew, N. T., & Johansson, B. (2023). Production planning and scheduling challenges in the engineer-to-order manufacturing segment: A literature study. International Journal of Innovation, Management and Technology, 14(3), 80-87. https://doi.org/10.18178/ijimt.2023.14.3.942

[27] Bagshaw, K. B. (2021). PERT and CPM in project management with practical examples. American Journal of Operations Research, 11(4), 215-226. https://doi.org/10.4236/ajor.2021.114013

[28] Chakrabortty, R. K., Sarker, R. A., & Essam, D. L. (2020). Single mode resource constrained project scheduling with unreliable resources. Operational Research, 20(3), 1369-1403. https://doi.org/10.1007/s12351-018-0380-7

[29] Kong, X., & Dou, Y. (2021). Job shop scheduling based on digital twin technology: A survey and an intelligent platform. Complexity, 2021, Article ID 8823273. https://doi.org/10.1155/2021/8823273

[30] Zhang, C., & Zheng, R. (2023). Event-driven dynamic jobshop scheduling method with strong process constraints. Journal of Computing and Electronic Information Management, 10(3), 72-79. https://drpress.org/ojs/index.php/jceim/search/search?query=Event-driven%20dynamic%20jobshop%20scheduling%20method%20with%20strong%20process%20constraints

[31] Leo Kumar, S. P. (2019). Knowledge-based expert system in manufacturing planning: State-of-the-art review. International Journal of Production Research, 57(15-16), 4766-4790. https://doi.org/10.1080/00207543.2018.1552032

[32] Xie, J., Gao, L., Peng, K., Li, X., & Li, H. (2019). Review on flexible job shop scheduling. IET Collaborative Intelligent Manufacturing, 1(3), 67-77. https://doi.org/10.1049/iet-cim.2018.0009

[33] Köksal, G., Batmaz, İ., & Testik, M. C. (2011). A review of data mining applications for quality improvement in manufacturing industry. Expert Systems with Applications, 38(10), 13448-13467. https://doi.org/10.1016/j.eswa.2011.04.063

[34] Denkena, B., Dittrich, M. A., Stamm, S., Wichmann, M., & Wilmsmeier, S. (2021). Reprint of: Gentelligent processes in biologically inspired manufacturing. CIRP Journal of Manufacturing Science and Technology, 34, 105-118. https://doi.org/10.1016/j.cirpj.2021.03.004

[35] Razeih, A. C., & Zahra, A. C. (2013). A review of expert systems and their usage in management. https://scholar.google.com/scholar?q=%22A+Review+of+Expert+Systems+and+Their+Usage+in+Management%22

[36] Sotnik, S., Deineko, Z., & Lyashenko, V. (2022). Key directions for development of modern expert systems. International Journal of Academic Information Systems Research, 6(4), 4-7. https://philarchive.org/archive/SOTKDF

[37] Sayed, B. T. (2021). Application of expert systems or decision-making systems in the field of education. Information Technology in Industry, 9(1), 1396-1405. https://doi.org/10.17762/itii.v9i1.283

[38] Zhang, J., Ding, G., Zou, Y., Qin, S., & Fu, J. (2019). Review of job shop scheduling research and its new perspectives under Industry 4.0. Journal of Intelligent Manufacturing, 30, 1809-1830. https://doi.org/10.1007/s10845-017-1350-2

[39] Centeno, M. A., & Standridge, C. R. (1991). Modeling manufacturing systems: An information-based approach. ACM SIGSIM Simulation Digest, 21(3), 230-239. https://doi.org/10.1145/106073.306864

[40] Maylawati, D. S., Darmalaksana, W., & Ramdhani, M. A. (2018). Systematic design of expert system using unified modelling language. IOP Conference Series: Materials Science and Engineering, 288(1), 012047. https://doi.org/10.1088/1757-899X/288/1/012047

[41] Lödding, H., Reichelt, A., & Nyhuis, P. (2021). Approaching automation of production planning and control: A theoretical framework. Journal of Production Systems and Logistics, 1, Article 16. https://doi.org/10.15488/11127

[42] Metaxiotis, K. S., Psarras, J. E., & Askounis, D. T. (2002). GENESYS: An expert system for production scheduling. Industrial Management & Data Systems, 102(6), 309-317. https://doi.org/10.1108/02635570210432000

[43] Xiong, H., Shi, S., Ren, D., & Hu, J. (2022). A survey of job shop scheduling problem: The types and models. Computers & Operations Research, 142, 105731. https://doi.org/10.1016/j.cor.2022.105731

[44] Tatsiopoulos, I. P., & Mekras, N. D. (1999). An expert system for the selection of production planning and control software packages. Production Planning & Control, 10(5), 414-425. https://doi.org/10.1080/095372899233213

[45] Prak, D., & Teunter, R. (2019). A general method for addressing forecasting uncertainty in inventory models. International Journal of Forecasting, 35(1), 224-238. https://doi.org/10.1016/j.ijforecast.2017.11.004

[46] Idris, M. O. (2018). Evaluation of effects of external factors on due-date prediction in manufacturing jobshops. FUTA Journal of Engineering and Engineering Technology, 12(2), 250-260. https://www.futajeet.com/index.php/jeet/article/view/95

[47] Schaer, O., Kourentzes, N., & Fildes, R. (2019). Demand forecasting with user-generated online information. International Journal of Forecasting, 35(1), 197-212. https://doi.org/10.1016/j.ijforecast.2018.09.003

[48] Williams, T. M. (2015). Forecasting and demand management. In J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences (2nd ed., pp. 19-24). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.73039-8

[49] Wuest, T., Irgens, C., & Thoben, K.-D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25, 1167-1180. https://doi.org/10.1007/s10845-013-0761-y

[50] Karami, S., Karami, E., Buys, L., & Drogemuller, R. (2017). System dynamic simulation: A new method in social impact assessment (SIA). Environmental Impact Assessment Review, 62, 25-34. https://doi.org/10.1016/j.eiar.2016.07.009

[51] Peck, W. J., & Finke, D. A. (2019). Systems dynamic modeling: Planning beyond the worker. In 2019 Winter Simulation Conference (WSC) (pp. 2606-2616). IEEE. https://doi.org/10.1109/WSC40007.2019.9004685

[52] Akinyele, S. T. (2007). Determination of the optimal manpower size using linear programming model. Research Journal of Business Management, 1(1), 30-36. https://doi.org/10.3923/rjbm.2007.30.36

[53] Swinerd, C., & McNaught, K. R. (2012). Design classes for hybrid simulations involving agent-based and system dynamics models. Simulation Modelling Practice and Theory, 25, 118-133. https://doi.org/10.1016/j.simpat.2011.09.002

[54] Vujović, A., Jovanović, J., Krivokapić, Z., Peković, S., Soković, M., & Kramar, D. (2017). The relationship between innovations and quality management system. Tehnički vjesnik/Technical Gazette, 24(2), 551-556. https://doi.org/10.17559/TV-20150528100824

[55] Kotir, J. H., Smith, C., Brown, G., Marshall, N., & Johnstone, R. (2016). A system dynamics simulation model for sustainable water resources management and agricultural development in the Volta River Basin, Ghana. Science of the Total Environment, 573, 444-457. https://doi.org/10.1016/j.scitotenv.2016.08.081

[56] Fernandes, J. M. R. C., Homayouni, S. M., & Fontes, D. B. M. M. (2022). Energy-efficient scheduling in job shop manufacturing systems: A literature review. Sustainability, 14(10), 6264. https://doi.org/10.3390/su14106264

[57] Abioye, A. G., Idris, M. O., Adeniran, A. D., & Adetayo, O. O. (2026). Applications of an expert system for efficient energy optimization in jobshops. Journal of Engineering for Development, 18(1), 315-326. No public DOI/source link confirmed.

[58] Gao, K., Huang, Y., Sadollah, A., & Wang, L. (2020). A review of energy-efficient scheduling in intelligent production systems. Complex & Intelligent Systems, 6, 237-249. https://doi.org/10.1007/s40747-019-00122-6

[59] Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). Operational methods for minimization of energy consumption of manufacturing equipment. International Journal of Production Research, 45(18-19), 4247-4271. https://doi.org/10.1080/00207540701450013

[60] Jewo, A. O., Kareem, B., & Ebojoh, E. (2021). Application of an expert system for critical equipment identification in production plant. International Journal of Engineering Research and Applications, 11(11), 26-36. https://doi.org/10.9790/9622-1111012636

[61] Idris, M. O., Adeboye, B. S., Yusuf, F. A., & Adekanye, S. A. (2022). An integrated investment scheduling model for new job shops: A phase-in approach. Jurnal Analisis Bisnis Ekonomi, 20(1), 1-17. https://journal.unimma.ac.id/index.php/bisnisekonomi/article/view/6851

[62] Mohamed, A. E. (2024). Inventory management. In T. Bányai (Ed.), Operations management: Recent advances and new perspectives. IntechOpen. https://doi.org/10.5772/intechopen.113282

[63] Manufapp. (2025). Understanding inventory in manufacturing: From valuation to KPIs. Manufapp. https://www.manufapp.com

[64] Fazlollahtabar, H., & Mahdavi-Amiri, N. (2010). Design of an expert system to estimate cost in an automated jobshop manufacturing system. In 40th International Conference on Computers and Industrial Engineering (pp. 1-6). IEEE. https://doi.org/10.1109/ICCIE.2010.5668385

[65] Grahovac, D., & Devedžić, V. (2010). COMEX: A cost management expert system. Expert Systems with Applications, 37(12), 7684-7695. https://doi.org/10.1016/j.eswa.2010.04.073

[66] ElMaraghy, W. H., & Alami, D. (2020). Activity-based aggregate job costing model for reconfigurable manufacturing systems. International Journal of Industry and Sustainable Development, 1(2), 1-19. https://doi.org/10.21608/ijisd.2020.101602

[67] Duran, O., & Afonso, P. S. L. P. (2020). An activity-based costing decision model for life-cycle economic assessment in spare parts logistic management. International Journal of Production Economics, 222, 107499. https://doi.org/10.1016/j.ijpe.2019.09.020

[68] Santana, A., Afonso, P. S. L. P., Zanin, A., & Wernke, R. (2017). Costing models for capacity optimisation in Industry 4.0: Trade-off between used capacity and operational efficiency. Procedia Manufacturing, 13, 1183-1190. https://doi.org/10.1016/j.promfg.2017.09.194

[69] Idris, M. O., Enwerem, G. C., & Kareem, B. (2020). Development of dynamic exponential smoothing approach for job shops in the developing nations. Journal of Science, Technology, Mathematics and Education, 16(3), 12-24. https://journal.engineering.fuoye.edu.ng/index.php/engineer/article/view/1480/837

[70] Obembe, J. J. (2016). Investigation into causes of project failure in Akure Metropolis, Ondo State, Nigeria. FUTA Journal of Engineering and Engineering Technology, 10(2), 53-60. https://doi.org/10.51459/futajeet.2016.10.2.105

[71] Bhosale, K. C., & Pawar, P. J. (2019). Material flow optimisation of production planning and scheduling problem in flexible manufacturing system by real coded genetic algorithm (RCGA). Flexible Services and Manufacturing Journal, 31, 381-423. https://doi.org/10.1007/s10696-018-9310-5

[72] Shin, K., Park, G., Choi, J. Y., & Choy, M. (2017). Factors affecting the survival of SMEs: A study of biotechnology firms in South Korea. Sustainability, 9(1), 108. https://doi.org/10.3390/su9010108

[73] Sun, D., Huang, R., Chen, Y., Wang, Y., Zeng, J., Yuan, M., Pong, T.-C., & Qu, H. (2020). PlanningVis: A visual analytics approach to production planning in smart factories. IEEE Transactions on Visualization and Computer Graphics, 26(1), 579-589. https://doi.org/10.1109/TVCG.2019.2934275

[74] Oluyisola, O. E., Sgarbossa, F., & Strandhagen, J. O. (2020). Smart production planning and control: Concept, use-cases and sustainability implications. Sustainability, 12(9), 3791. https://doi.org/10.3390/su12093791

[75] Solaja, O. A., Abiodun, J. A., Abioro, M. A., Ekpudu, J. E., & Olasubulumi, O. M. (2019). Application of linear programming in production planning. International Journal of Applied Operational Research, 9(3), 11-19. https://mpra.ub.uni-muenchen.de/98226/

[76] Adamu, P. I., Okagbue, H. I., & Oguntunde, P. E. (2019). A new priority rule for solving project scheduling problems. Wireless Personal Communications, 106, 681-699. https://doi.org/10.1007/s11277-019-06185-5

[77] Oluyisola, O. E., Bhalla, S., Sgarbossa, F., et al. (2022). Designing and developing smart production planning and control systems in the Industry 4.0 era: A methodology and case study. Journal of Intelligent Manufacturing, 33, 311-332. https://doi.org/10.1007/s10845-021-01808-w

[78] Sánchez-Herrera, S., Montoya-Torres, J. R., & Solano-Charris, E. L. (2019). Flow shop scheduling problem with position-dependent processing times. Computers & Operations Research, 111, 325-345. https://doi.org/10.1016/j.cor.2019.05.001

[79] Strandhagen, J. O., Vallandingham, L. R., Fragapane, G., Strandhagen, J. W., Stangeland, A. B. H., & Sharma, N. (2017). Logistics 4.0 and emerging sustainable business models. Advances in Manufacturing, 5(4), 359-369. https://doi.org/10.1007/s40436-017-0198-1

Published

2026-09-03