Inventory management maximization based on sales forecast: case study

The major purpose of this paper is to present a concept for reliable planning of the sales of small firms, where the large number of product variants complicates the implementation of this kind of system considerably. First, a methodology is presented to set up sales forecasting so that it can be integrated into the inventory management process. This inventory management software interprets forecasting information and provides users with a decision support system to minimize stocks in stores while at the same time avoiding missed sales. It is best applied in company types requiring high precision inventories, notably those in the textile industry; a large range of patterns are produced with many small variations (colors, size, customizations, etc.) and these products have a limited lifetime. Inventory management is difficult due to the multitude of products to account for and the necessity to sell them quickly. The methodology is intended for inventory management at the end of the supply chain. In store, the number of references, their similarities and the necessity to minimize unsold stock greatly complicates the reordering and restocking process. These types of companies do not easily lend themselves to classic techniques of sales forecasting and require specialized methods to estimate their needs precisely.

[1]  Margaret Bruce,et al.  Adding value: challenges for UK apparel supply chain management – a review , 2011 .

[2]  Graham K. Rand,et al.  Decision Systems for Inventory Management and Production Planning , 1979 .

[3]  J. J. Kanet,et al.  Dynamic planned safety stocks in supply networks , 2010 .

[4]  C. C. Holt,et al.  A Linear Decision Rule for Production and Employment Scheduling , 1955 .

[5]  D. Agrawal,et al.  Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model , 1996 .

[6]  J. Hammond,et al.  Control your inventory in a world of lean retailing. , 2000, Harvard business review.

[7]  Srinivas Talluri,et al.  Integrating demand and supply variability into safety stock evaluations , 2004 .

[8]  E. J. Orav Quantitative Forecasting Methods , 1990 .

[9]  Farzad Mahmoodi,et al.  Safety stock determination based on parametric lead time and demand information , 2010 .

[10]  P. R. Shearer,et al.  Quantitative Forecasting Methods , 1990 .

[11]  Marc Lambrecht,et al.  The dynamics of aggregate planning , 2003 .

[12]  Haralambos Sarimveis,et al.  A combined model predictive control and time series forecasting framework for production-inventory systems , 2008 .

[13]  Peter R. Winters,et al.  Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .

[14]  C. C. Holt,et al.  Planning Production, Inventories, and Work Force. , 1962 .

[15]  G. N. Evans,et al.  Analysis and design of an adaptive minimum reasonable inventory control system , 1997 .

[16]  George L. Hodge,et al.  Adapting lean manufacturing principles to the textile industry , 2011 .

[17]  Barbara Pfeffer,et al.  Smoothing Forecasting And Prediction Of Discrete Time Series , 2016 .

[18]  Tiaojun Xiao,et al.  Coordination of a fashion apparel supply chain under lead-time-dependent demand uncertainty , 2011 .

[19]  Bhaba R. Sarker,et al.  Optimal inventory system with two backlog costs in response to a discount offer , 2011 .

[20]  Janat Shah,et al.  Supply Chain Management:: Text and Cases , 2009 .

[21]  R. Brown Statistical forecasting for inventory control , 1960 .

[22]  S. Dashkovskiy,et al.  Production Planning & Control , 2013 .