Collaborative Agents Supporting Tactical Planning Activities – An Industrial Application

  • Ana Paula M. Tanajura
  • Pinar Öztürk
  • Herman Lepikson
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 349)


This paper presents a collaborative agents model for Sales and Operation Planning (S&OP) in industry. We show how an S&OP system can be used in a multi-agent approach considering existing legacy software and how people and software agents interact in a planning environment in a process industry, such as the petrochemical industry. The model helps to describe the process, determine the pre-existing software integrations, define responsibilities, and promote communication and learning. The solution adopted is crucial for a high performance S&OP. It is also important to consider the interaction among human agents and software agents, which are required for its success. The multi-agent system (MAS) paradigm is useful for helping industries handle distributed information sources and interactions between a number of actors and teams. We examine activities of an S&OP in a petrochemical company and how these activities can be more efficiently performed through MAS.


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  1. 1.
    Vrba, P.: Review of Industrial Applications of Multi-agent Technologies. In: Borangiu, T., Thomas, A., Trentesaux, D. (eds.) Service Orientation in Holonic and Multi agent, SCI, vol. 472, pp. 327–338. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Shen, W., Hao, Q., Yoon, H.J., Norrie, D.H.: Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics 20, 415–431 (2006), doi:10.1016/j.aei.2006.05.004.CrossRefGoogle Scholar
  3. 3.
    He, N., Zhang, D.Z., Li, Q.: Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system. International Journal of Production Economics (2013),
  4. 4.
    Hernández, J.E., Lyons, A.C., Mula, J., Poler, R., Ismai, H.: Production Planning & Control: Supporting the collaborative decision-making process in an automotive supply chain with a multi-agent system. Production Planning & Control: The Management of Operations (2013), doi:10.1080/09537287.2013.798086Google Scholar
  5. 5.
    Tichý, P., Slechta, P., Maturana, F., Balasubramanian, S.: Industrial MAS for Planning and Control. In: Marik, V., et al. (eds.) Proceedings of the 9th ECCAI-ACAI/EASSS 2001, AEMAS 2001, HoloMAS 2001 on Multi-Agent-Systems and Applications II-Selected Revised Papers, pp. 280–295 (2002)Google Scholar
  6. 6.
    Jindal, K., Srinivasan, S., Sharma, M.: Review of Decision Support System Based on Multi Agent in Production Scheduling. International Journal of Engineering and Social Science 3(10), 33–36 (2013)Google Scholar
  7. 7.
    Leitão, P.: Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence 22(7), 979–991 (2009)CrossRefGoogle Scholar
  8. 8.
    Caridi, M., Cavalieri, S.: Multi-agent systems in production planning and control: an overview. Production Planning & Control: The Management of Operations 15(2), 106–118 (2004), doi:10.1080/09537280410001662556CrossRefGoogle Scholar
  9. 9.
    Vollmann, T.E., Berry, W.L., Whybark, D.C., Jacobs, F.R.: Manufacturing Planning and Control Systems for Supply Chain. Irwin, NY (2005)Google Scholar
  10. 10.
    Grimson, J.A., Pyke, D.F.: Sales and operations planning: an exploratory study and framework. International Journal of Logistics Management 18(3), 322–346 (2007)CrossRefGoogle Scholar
  11. 11.
    Schlegel, G.L., Murray, P.: Next Generation of S&OP: Scenario Planning with Predictive Analytics & Digital Modeling. Journal of Business Forecasting (Fall 2010)Google Scholar
  12. 12.
    Olhager, J.: Evolution of operations planning and control: from production to supply chains. International Journal of Production Research 51, 6836–6843 (2013), doi:10.1080/00207543.2012.761363.CrossRefGoogle Scholar
  13. 13.
    Wallace, T.F.: Sales & Operations Planning: The How-To Handbook, USAGoogle Scholar
  14. 14.
    Lapide, L.: Sales and operations planning Part I: the process. The Journal of Business Forecasting 23(3), 17–19 (2004a)Google Scholar
  15. 15.
    Wing, L.G.P.: Toward twenty-first-century pharmaceutical sales and operations planning. Pharmaceutical Technology North America, 20–26 (2001)Google Scholar
  16. 16.
    Lapide, L.: Sales & operations planning part III: a diagnostic model. Journal of Business Forecasting 24(1), 13–16 (2005)Google Scholar
  17. 17.
    Ventana Research. Sales and operations Planning: Measuring Maturity and Opportunity for Operational Performance Management. Ventana Research, San Mateo, CA, USA (2006)Google Scholar
  18. 18.
    Feng, Y., Sophie D’Amours, S., Beauregard, R.: The value of sales and operations planning in oriented strand board industry with make-to-order manufacturing system: cross functional integration under deterministic demand and spot market recourse. International Journal of Production Economics 115(1), 189–209 (2008), doi:10.1016/j.ijpe.2008.06.002.CrossRefGoogle Scholar
  19. 19.
    Viswanathan, N.: Sales and operations Planning: Integrate with Finance and Improve Revenue. Aberdeen Group, Boston (2009)Google Scholar
  20. 20.
    Cacere, L., Barret, J., Mooraj, H.: Sales and Operations Planning: Transformation from Tradition. Industry Value Chain Strategies. AMR Research, Boston (2009)Google Scholar
  21. 21.
    Wells, A.M., Schorr, J.: Sales and Operations Planning: The key to continuous demand satisfaction. SAP INSIGHT Business Process Innovations (2007),
  22. 22.
    Lapide, L.: Sales and operations planning Part II: enabling technology. The Journal of Business Forecasting 23(3), 18–20 (2004)Google Scholar
  23. 23.
    Jurečka, P.: Strategy and Portfolio Management Aspects of Integrated Business Planning. Central European Business Review 2(1), 28–36 (2013)Google Scholar
  24. 24.
    Gundersen, O.E., Kofod-Petersen, A.: Multiagent Based Problem-solving in a Mobile Enviroment. In: Norsk Informatik Konferanse, Bergen (2005)Google Scholar
  25. 25.
    Öztürk, P., Rossland, K., Gundersen, O.E.: A multiagent framework for coordinated parallel problem solving. Appl. Intell. 33(2), 132–143 (2010), doi:10.1007/s10489-008-0154-7CrossRefGoogle Scholar
  26. 26.
    Adamczak, M., Domański, R., Cyplik, P.: Use of sales and operations planning in small and medium-sized enterprises. LogForum 9(1), 11–19 (2013)Google Scholar
  27. 27.
    APICS. Sales & Operations Planning. Presented at the Region IV Meeting for the Association for Operation Management, New Orleans (April 14, 2007)Google Scholar
  28. 28.
    Thomé, A.M.T., Scavarda, L.P., Fernandez, N.S., Scavarda, A.J.: Sales and Operations Planning: A Research Synthesis. International Journal of Production Economics 138(1), 1–13 (2012)CrossRefGoogle Scholar
  29. 29.
    Corrêa, H.L., Gianesi, I.G.N., Caon, M.: Planejamento, Programação e Controle da Produção. Editora Atlas, São Paulo (2001)Google Scholar
  30. 30.
    Chandrasekaran, B., Johnson, T.R.: Generic tasks and Task structures: History, critique and new directions. In: David, J.M., Krivine, J.P., Simmons, R. (eds.) Second Generation Expert Systems, pp. 232–272. Springer, Berlin (1993)CrossRefGoogle Scholar
  31. 31.
    Fensel, D., Motta, E., Benjamins, V.R., Crubezy, M., Decker, S., Gaspari, M., Groenboon, R., Grosso, W., Van Harmelen, F., Musen, M., Plaza, E., Schreiber, G., Studer, R., Wielinga, B.: The unified problem-solving method development language UPML. Knowledge and Information Systems (1999)Google Scholar
  32. 32.
    Wooldridge, M.: Intelligent Agents. In: Weiss, G. (ed.) Multiagent Systems – A Modern Approach to Distributed Artificial Intelligence. MIT Press (1999)Google Scholar
  33. 33.
    Tanajura, A.P.M., Cabral, S.: Sales and Operations Planning (S&OP) in a Petrochemical Company. TAC, Curitiba 1(2), 55–67 (2011)Google Scholar
  34. 34.
    Rossi, M.C., Bandoni, J.A.: Planning of an integrated Petrochemical Complex Using SCMart®. Paper presented at the 2nd Mercosur Congress on Chemical Engineering 4th Mercosur Congress on Process Systems Engineering, Costa Verde, August 14-18 (2005)Google Scholar
  35. 35.
    Cox, J., Goldratt, E.M.: The goal: a process of ongoing improvement. North River Press, Great Barrington (1986)Google Scholar
  36. 36.
    Finin, T., Fritzson, R., McKay, D., McEntire, R.: KQML as an agent communication language. In: Proceedings of the Third International Conference on Information and Knowledge Management, CIKM 1994, New York, USA, pp. 456–463 (1994)Google Scholar
  37. 37.
    Searle, J.: Indirect speech acts. In: Syntax and Semantics, vol. 3: Speech Acts, pp. 59–82. Academic Press, New York (1975)Google Scholar
  38. 38.
    Smith, R.G.: The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. IEEE Transactions on Computers C-29(12), 1104–1113 (1980)CrossRefGoogle Scholar
  39. 39.
    Foner, L.N., Crabtree, I.B.: Multi-Agent Matchmaking. In: Nwana, H.S., Azarmi, N. (eds.) Software Agents and Soft Computing: Towards Enhancing Machine Intelligence. LNCS, vol. 1198, pp. 100–115. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  40. 40.
    Öztürk, P., Aamodt, A.: A context model for knowledge-intensive case-based reasoning. International Journal of Human Computer Studies 48, 331–355 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Ana Paula M. Tanajura
    • 1
    • 2
  • Pinar Öztürk
    • 3
  • Herman Lepikson
    • 1
    • 2
  1. 1.SENAI CIMATECSalvadorBrazil
  2. 2.Program of Industrial EngineeringFederal University of BahiaSalvadorBrazil
  3. 3.NTNU, Norwegian University of Science and TechnologyTrondheimNorway

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