Simulation Modelling of Quick Response Replenishment of Seasonal Clothing

Quick Response (QR) is a phrase that has been used within the clothing industry to convey a marketing strategy that is strongly reactive to customer demand. QR allows frequent in-season re-estimation of true consumer demand and reorders of merchandise by the retailer, and places heavy emphasis on local suppliers to replenish goods that have sold well.

This contrasts with the traditional pattern of pre-ordering most of the merchandise, resulting in stockouts during the season and leaving, at the end of the season, significant volumes of stock which were at odds with the customers preference, and which is only disposed of by marking down the selling price.

The strategies needed to implement Quick Response have been much discussed. One key idea is to shorten the textile/ clothing supply pipeline by eliminating unnecessary inventory and processing times. This can be achieved by improved operational practices, effective information transfer and collaborative relationships between organisations.

At Hollings Faculty, a prototype stochastic computer-simulation model has been constructed to simulate the clothing supply chain (with the feed forward flow of materials and the feed backward flow of information), thus creating an analytical tool. The model allows exploration of the underlying differences between traditional and Quick Response procedures. The model is also designed to allow the investigation of the effects of improved retailing and supply procedures on financial and other performance measures.

The model, as it currently exists, permits an evaluation of Quick Response supply methodologies (with frequent re-estimations of consumer demand and reorders of merchandise) in relation to different quantities and lead-time scenarios. The model offers the opportunity to evaluate a variety of "What if…?" scenarios in any initiative to enhance the responsiveness of supply chains.

The simulation model monitors the inventory of garments at the retail store. This inventory is affected by the initial supply, any reorders that arrive during the season and consumer purchases. The underlying consumer demand is currently expressed in terms of customer volume and seasonality of arrivals (i.e. percentage of customer volume in each week). These are unknown and must be estimated by the retailer. The retailer’s plan (preseason) also determines the initial supply.

The Model developed to date has two supply strategies: fixed quantity re-ordering and fixed interval re-ordering. These two alternatives offer a wide range of options in experimentation.

A major requirement of the simulation model is that it should capture the dynamic and stochastic nature of consumer behaviour at the retail store, which allows investigation of buyer strategies for seasonal stock. If the stock level falls below a specified level (Fixed quantity ordering model), the retail store will send a fixed quantity order to the supplier which will arrive after a specified lead-time.

The initial supply and reorder schemes are specified as input to the model as is the reorder lead-time. The reorder lead-time has an effect on the possible number of reorders during the 12-week season.

Early experimental work with the (fixed quantity-ordering model) has shown that if replenishment times exceed 2 weeks, the potential for lost sales greatly increases. This provides a benchmark figure for assessing the responsiveness of clothing industry supply chain.

If you are interesting in discussing this project further, please contact:

David Tyler
Department Of Clothing Design & Technology, Hollings Faculty
The Manchester Metropolitan University
Old Hall Lane, Manchester, M14 6HR.

Telephone Number:  0161 247 2636
Email:  d.tyler@mmu.ac.uk