Demand Driven DRP: Assessment of a New Approach to Distribution

Document Type: Gol'18


Laboratory Industrial Management and Innovation, Faculty of Science and Technology, Settat, Morocco


The distribution of goods from suppliers to customers plays an important role in the supply chain. In this paper, the approach of Demand Driven Distribution Resource Planning (DDDRP) is proposed in order to optimize the distribution flow in the supply chain. The purpose is to manage all sources of variability "operational, management, supply and demand", while improving the traditional methods as Distribution Resource Planning (DRP). A literature review is presented about the impact of variability on distribution flow, and the solutions proposed in this context. Then, a general study of distribution industries is investigated in order to apply the DDDRP method; we show the buffer positioning in the distribution network, and the profile and levels for these buffers. After the dynamic adjustment, we present the Demand Driven Planning and the execution based on the net flow equation. The results discuss the approach and the steps of the implementation in the distribution industry.


Main Subjects

A. J. MARTIN, (1985), DRP (Distribution Resource Planning)- can you afford not to have it?, Material Handling Engineering, Vol. 40, pp. 131–139.

Aharon B-T, Boaz G, Shimrit S (2009), Robust multi-echelon multi-period inventory control. Eur J Oper Res, Vol. 199 (3),  pp. 922–935.

Alony I, Munoz A (2007), The bullwhip effect in complex supply chains. 2007 International Symposium on Communications and Information Technologies Proceedings ,Darling Harbour, Sydney, Australia, 17–19 October, pp. 1355–1360.

André J. Martin, (1995), Distribution Resource Planning : The Gateway to True Quick Response and Continuous Replenishment, Wiley.

Bagnha, M., M. Cohen. (1995), The stabilizing effect of inventory in supply chains". Oper. Res. Forthcoming.

Chiang, W. K. and Monahan, (2005), G. E. Managing inventories in a two-echelon dual-channel supply chain, Eur. J. Opl Res., Vol. 162, pp. 325–341.

Chandra C, Grabis J (2005), Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand. Eur J Oper Res, Vol. 166 (2), pp. 337–350.

C. Ptak and C. Smith. (2011), Orlicky’s Material Requirements Planning, 3/E. McGraw Hill Professional.

Croson R, Donohue K (2009), Impact of POS data sharing on supply chain management: an experimental study. Prod Oper Manag, Vol. 12(1), pp. 1–11.

Dong, L. and Lee, H. L. (2003), Optimal policies and approximations for a serial multiechelon inventory system with time-correlated demand, Ops Res., Vol. 51(6), pp. 969-980.

E. M. Goldratt, (1990), What is this thing called the theory of constraints?, NY: The North River Press.

Forger, G., (1986), How Lotus cut inventory and increased productivity. Modern Materials Handling, Vol. 41, pp. 70–71.

Frasier-Sleyman, K., (1994), Forecasting and the continuous replenishment craze of the 1990s. Journal of Business Forecasting, Vol. 13, pp. 3–8.

Ganeshan, R (1999). Managing supply chain inventories: a multiple retailer, one warehouse, multiple supplier model, Int. J. Prod. Econ., Vol. 59, pp. 341–354.

Geary S, Disney SM, Towill DR (2006) On bullwhip in supply chains—historical review, present practice and expected future impact. Int J Prod Econ 101(1 SPEC. ISS), pp. 2–18.

Heydari Jafar, Kazemzadeh RB, SK Chaharsooghi (2009), A study of lead time variation impact on supply chain performance. Int J Adv Manuf Technol, Vol. 40(11–12), pp. 1206–1215.

Horne, R., (1989), Charting a course for integrated logistics, Transportation and Distribution,Vol. 30, pp. 45–51.

Kalchschmidt, M., Zotteri, G., and Verganti, R. (2003), Inventory management in a multi-echelon spare parts supply chain, Int. J. Prod. Econ., Vol. 81–82, pp. 397–413.

Krepchin, I. P., (1989), PC-based MRP, DRP help Lipton cut inventories, Modern Materials Handling, Vol. 44, pp. 86–88.

Lee HL, Padmanabhan V, Seungjin W (1997), The bullwhip effect in supply chains. Sloan Manage Rev, Vol. 38(3), pp. 93–102

Lee HL, So KC, Tang CS (2000), The value of information sharing in a two-level supply chain. Manage Sci, Vol. 46(5), pp. 626–643.

Miclo, R., Fontanili, F., Lauras, M., Lamothe, J., and Milian, B., (2016). An empirical comparison of MRPII and Demand-Driven MRP. IFAC-Papers On Line, Vol. 49(12), pp. 1725–1730.  

Minner,S. (2003), Multiple-supplier inventory models in supply chain management: a review, Int. J. Prod. Econ., Vol. 81–82, pp. 265–279.

Ptak, C., Smith, C., (2016). Demand Driven Material Requirements Planning (DDMRP), Industrial Press, Inc.

Rank Chen, Zvi Drezner, Jennifer K. Ryan, David Simchi-Levi, (2000), Quantifying the Bullwhip effectin a simple Supply Chain: The impact of forecasting, Lead Times, and Information, Management Science, Vol. 46 (3), pp. 436-443.

Rau, H.,Wu, M.-Y., and Wee, H.-M. (2003), Integrated inventory model for deteriorating items under a multi-echelon supply chain environment, Int. J. Prod. Econ., Vol. 86, pp. 155–168.

Routroy, S. and Kodali, R. (2005), Differential evolution algorithm for supply chain inventory planning, J. Mfg echnol. Mgmt, Vol. 16(1), pp. 7–17.

Tee, Y. S. and Rossetti, M. D., (2002), A robustness study of a multi-echelon inventory model via simulation, Int. J. Prod. Econ., Vol. 80, pp. 265–277.

T. Ohno, (1987), Toyota Production System. Productivity Press.

Van der Vorst, J. G. A. J., Beulens, A. J. M., and van Beek, (2000), P. Modelling and simulating multi-echelon food systems, Eur. J. Opl Res., Vol. 122, pp. 354–366.

Van der Heijden, M. C. (1999), Multi-echelon inventory control in divergent systems with shipping frequencies, Eur. J. pl Res., Vol. 116, pp. 331–351.

Wei Wang, Richard Y.K. Fung, Yueting Chaic, (2004), Approach of just-in-time distribution requirements planning for supply chain management, Int. J. Production Economics, Vol. 91, pp. 101–107.