Advertisement

A fuzzy bi-objective MILP approach to integrate sales, production, distribution and procurement planning in a FMCG supply chain

  • Yaser Nemati
  • Mohammad Hosein Alavidoost
Methodologies and Application

Abstract

Sales and operations are the heart of today’s businesses, and the decisions made in these areas will intensively affect the financial performance, operational efficiency and service level of the whole organization. This manuscript is going to develop three multiobjective fuzzy mixed integer linear programming models of sales and operations planning process. Then, the performance of the fully integrated fuzzy model is compared to the similar crisp model, in terms of total supply chain’s cost and customer service level. All the models are developed for a multisite manufacturing company, which is coping with different raw material suppliers and third-party logistics, distribution centers and customers with a wide range of product families. Finally, the models are applied to a real case in a FMCG manufacturing company in Iran. The final results approve the superiority of the fuzzy model over the crisp one. Furthermore, a sensitivity analysis is carried out to analyze the effect of some key factors on the benefits of the SC planning integration.

Keywords

Sales and operations planning (S&OP) Fuzzy mixed integer linear programming (f-MILP) Multiobjective modeling FMCG industry Real-world case study 

Notes

Funding

This study was not funded by any profit or nonprofit organization.

Compliance with ethical standards

Conflict of interest

As authors of the manuscript, we, Yaser Nemati and Mohammad Hosein Alavidoost, declare that we have no conflict of interest to each other.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Alavidoost MH (2017) Assembly line balancing problems in uncertain environment: a novel interactive fuzzy approach for solving multi-objective fuzzy assembly line balancing problems. LAP LAMBERT Academic Publishing, Riga, LatviaGoogle Scholar
  2. Alavidoost M, Tarimoradi M, Zarandi M (2015a) Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks. J Intell Manuf 29(4):809–826CrossRefGoogle Scholar
  3. Alavidoost M, Tarimoradi M, Zarandi MF (2015b) Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems. Appl Soft Comput 34:655–677CrossRefGoogle Scholar
  4. Alavidoost M, Babazadeh H, Sayyari S (2016) An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Appl Soft Comput 40:221–235CrossRefGoogle Scholar
  5. Alavidoost M, Zarandi MF, Tarimoradi M, Nemati Y (2017) Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times. J Intell Manuf 28(2):313–336CrossRefGoogle Scholar
  6. Baumann P, Trautmann N (2014) A hybrid method for large-scale short-term scheduling of make-and-pack production processes. Eur J Oper Res 236(2):718–735MathSciNetCrossRefzbMATHGoogle Scholar
  7. Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci 17(4):B-141–B-164MathSciNetCrossRefzbMATHGoogle Scholar
  8. Bilgen B, Çelebi Y (2013) Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling. Ann Oper Res 211(1):55–82CrossRefzbMATHGoogle Scholar
  9. Bilgen B, Dogan K (2015) Multistage production planning in the dairy industry: a mixed-integer programming approach. Ind Eng Chem Res 54(46):11709–11719CrossRefGoogle Scholar
  10. Cecere L, Hillman M, Masson C (2006). The handbook of sales and operations planning technologies. In: AMR research report, AMRR-19187. pp 1–48Google Scholar
  11. Chandra P, Fisher ML (1994) Coordination of production and distribution planning. Eur J Oper Res 72(3):503–517CrossRefzbMATHGoogle Scholar
  12. Clark AJ, Scarf H (1960) Optimal policies for a multi-echelon inventory problem. Manag Sci 6(4):475–490CrossRefGoogle Scholar
  13. Dhaenens-Flipo C (2000) Spatial decomposition for a multi-facility production and distribution problem. Int J Prod Econ 64(1):177–186CrossRefGoogle Scholar
  14. El-Wahed WFA, Lee SM (2006) Interactive fuzzy goal programming for multi-objective transportation problems. Omega 34(2):158–166CrossRefGoogle Scholar
  15. Feng Y, Martel A, D’Amours S, Beauregard R (2013) Coordinated contract decisions in a make-to-order manufacturing supply chain: a stochastic programming approach. Prod Oper Manag 22(3):642–660.  https://doi.org/10.1111/j.1937-5956.2012.01385.x CrossRefGoogle Scholar
  16. Fleischmann B, Meyr H, Wagner M (2015) Advanced planning supply chain management and advanced planning. Springer, Berlin, pp 71–95Google Scholar
  17. Fumero F, Vercellis C (1999) Synchronized development of production, inventory, and distribution schedules. Transp Sci 33(3):330–340CrossRefzbMATHGoogle Scholar
  18. Guan Z, Philpott AB (2011) A multistage stochastic programming model for the New Zealand dairy industry. Int J Prod Econ 134(2):289–299CrossRefGoogle Scholar
  19. Kır S, Yazgan HR (2016) A sequence dependent single machine scheduling problem with fuzzy axiomatic design for the penalty costs. Comput Ind Eng 92:95–104CrossRefGoogle Scholar
  20. Kopanos GM, Puigjaner L, Georgiadis MC (2011) Resource-constrained production planning in semicontinuous food industries. Comput Chem Eng 35(12):2929–2944CrossRefGoogle Scholar
  21. Kopanos GM, Puigjaner L, Georgiadis MC (2012a) Efficient mathematical frameworks for detailed production scheduling in food processing industries. Comput Chem Eng 42:206–216CrossRefGoogle Scholar
  22. Kopanos GM, Puigjaner L, Georgiadis MC (2012b) Simultaneous production and logistics operations planning in semicontinuous food industries. Omega 40(5):634–650CrossRefGoogle Scholar
  23. Lai Y-J, Hwang C-L (1992) A new approach to some possibilistic linear programming problems. Fuzzy Sets Syst 49(2):121–133MathSciNetCrossRefGoogle Scholar
  24. Ling R (2002) The future of sales and operations planning. In: Paper presented at the international conference proceedings of 2002 APICSGoogle Scholar
  25. Nemati Y, Madhoshi M, Ghadikolaei AS (2017) The effect of sales and operations planning (S&OP) on supply chain’s total performance: a case study in an Iranian dairy company. Comput Chem Eng 104(Supplement C):323–338.  https://doi.org/10.1016/j.compchemeng.2017.05.002 CrossRefGoogle Scholar
  26. Nemati Y, Madhoushi M, Safaei Ghadikolaei A (2017) Towards supply chain planning integration: uncertainty analysis using fuzzy mathematical programming approach in a plastic forming company. Iran J Manag Stud 10(2):335–364Google Scholar
  27. Olhager J, Rudberg M, Wikner J (2001) Long-term capacity management: linking the perspectives from manufacturing strategy and sales and operations planning. Int J Prod Econ 69(2):215–225CrossRefGoogle Scholar
  28. Pant R, Prakash G, Farooquie JA (2015) A framework for traceability and transparency in the dairy supply chain networks. Procedia Soc Behav Sci 189:385–394CrossRefGoogle Scholar
  29. Pauls-Worm KG, Hendrix EM, Haijema R, van der Vorst JG (2014) An MILP approximation for ordering perishable products with non-stationary demand and service level constraints. Int J Prod Econ 157:133–146CrossRefGoogle Scholar
  30. Sel Ç, Bilgen B (2015) Quantitative models for supply chain management within dairy industry: a review and discussion. Eur J Ind Eng 9(5):561–594CrossRefGoogle Scholar
  31. Sel C, Bilgen B, Bloemhof-Ruwaard J, van der Vorst J (2015) Multi-bucket optimization for integrated planning and scheduling in the perishable dairy supply chain. Comput Chem Eng 77:59–73CrossRefGoogle Scholar
  32. Shermeh HE, Najafi S, Alavidoost M (2016) A novel fuzzy network SBM model for data envelopment analysis: a case study in Iran regional power companies. Energy 112:686–697CrossRefGoogle Scholar
  33. Tarimoradi M, Alavidoost M, Zarandi MF (2015) Comparative corrigendum note on papers “Fuzzy adaptive GA for multi-objective assembly line balancing” continued “Modified GA for different types of assembly line balancing with fuzzy processing times”: differences and similarities. Appl Soft Comput 35:786–788CrossRefGoogle Scholar
  34. Touil A, Echchatbi A, Charkaoui A (2016) An MILP model for scheduling multistage, multiproducts milk processing. IFAC-PapersOnLine 49(12):869–874CrossRefGoogle Scholar
  35. Wahlers JL, Cox JF III (1994) Competitive factors and performance measurement: applying the theory of constraints to meet customer needs. Int J Prod Econ 37(2):229–240CrossRefGoogle Scholar
  36. Wallace TF (2004) Sales & operations planning: the “how-to” handbook. T.F. Wallace & Company, CincinnatiGoogle Scholar
  37. Wari E, Zhu W (2016) Multi-week MILP scheduling for an ice cream processing facility. Comput Chem Eng 94:141–156CrossRefGoogle Scholar
  38. Williams JF (1983) A hybrid algorithm for simultaneous scheduling of production and distribution in multi-echelon structures. Manage Sci 29(1):77–92CrossRefzbMATHGoogle Scholar
  39. Youssef MA, Mahmoud MM (1996) An iterative procedure for solving the uncapacitated production-distribution problem under concave cost function. Int J Oper Prod Manag 16(3):18–27CrossRefGoogle Scholar
  40. Zarandi MF, Tarimoradi M, Alavidoost M, Shakeri B (2015a) Fuzzy approximate reasoning toward multi-objective optimization policy: deployment for supply chain programming. In: Paper presented at the Fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on soft computing (WConSC), 2015 annual conference of the North AmericanGoogle Scholar
  41. Zarandi MF, Tarimoradi M, Alavidoost M, Shirazi M (2015b) Fuzzy comparison dashboard for multi-objective evolutionary applications: an implementation in supply chain planning. In: Paper presented at the Fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on Soft computing (WConSC), 2015 annual conference of the North AmericanGoogle Scholar
  42. Zimmermann H-J (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1(1):45–55MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Operations and Production Management Industrial Management DepartmentUniversity of MazandaranBabolsarIran
  2. 2.Industrial Engineering Industrial Engineering DepartmentAmirkabir University of TechnologyTehranIran

Personalised recommendations