Quick Response and Accurate Response Systems Evren Körpeoğlu May 14, 2003

Content Introduction Quick Response Systems Literature on QR Systems Accurate Response Proposed Model Conclusion

Introduction Some basic problems of the Fashion-Apparel Industry: Continuous need of new products Long lead times due to lack of technology Poor forecasting High inventory levels Stockouts (Dissatisfied Customers)

Quick Response Systems Quick Response is the establishment of new business strategies, new relationships and new procedures to speed the flow of information and merchandise between retailers and manufacturers.

QR Methodology Textile and Apparel Manufacturing Level: Using CAD systems for high-quality design Narrowing assortment before season by consumer style testing and limited intoductions Helping retailer customers plan store assortments down to full SKU Implementing quality control programs to avoid duplication of effort Replacing adversarial customer and supplier relationships with cooperative partnerships

QR Methodology At the retail level: Planning assortments by individual store characteristics, rather than aggregate chain req. Managing inventory and re-ordering by store Sharing sales data and other key information with suppliers

QR Technologies Electronic Data Interchange (EDI) Shipping Container Marking (SCM): barcoding the containers to have an information about sent units Barcoding: Uses barcode scanning devices to gather Point of Sales (POS) data Computer-Aided Design (CAD) Unit Production Systems (UPS): automates the movement of products in bundles Flexible Manufacturing: Includes electronic patternmaking and laser-based cutting

QR Technologies Electronic Reorder Line Planning System (LPS): Develops the manufacturing and style line of the fashion goods Development Calender System Computer-Aided Pattern Making Computerized Inventory Systems Short Cycle Sewing Shade Sorting

Quick Response Systems Some Possible Benefits: Decreased lead times Increased market share Reduced inventory levels Enhanced customer loyalty Increased Profits Improved flexibility to meet changing market demand Improved productivity Lower costs

Quick Response Systems Brown et Al. in their paper “Impact of business type upon the adoption of quick response technologies” (2000) states: “Firms attempting to pioneer in product/market development, benefit most from quick response. These firms tend to offer a frequently changing product line and compete primarily by stimulating and meeting new market opportunities. They are also creators of change and uncertainty to which competitors must respond.”

Literature on QR Hammond (1991) explains the concept of Quick Response in a detailed manner. He includes technologies, duties, the development steps and possible echelon companies of a Quick Response system. Brown et Al. (2000) examine the effect of business type while implementing a quick response system and conclude that prospectors have the most benefit.

Literature on QR Perry et Al. (1999) describe the processes occurred during the implementation of quick response program to the textile, clothing and footwear (TCF) industry. They give 12 steps to implement a quick response supply partnership development. Perry and Sohal (2001) investigates the benefits gained by the implementation of QR program in Australia and include the comments of managers of different TCF companies.

Literature on QR Iyer and Bergen (1997) investigates the effects of reduced lead times, which is achieved by QR, on a single period problem. They suggest ways to increase the profit of the manufacturer to make the channel pareto improving. The manufacturer cannot change the wholesale price (w) The manufacturer does not accept returns from the retailer The retail price is fixed (p)

Literature on QR Single Period Problems: Lau HS and Lau AHL (1999) considers the case where manufacturer determines both wholesale price to retailer (w) and return credit amount (r). As the demand uncertainty decreases, the profit of manufacturer increases, but profit of retailer decreases. At a certain (w), retailer may have nearly zero profit, so he may not be willing to sell the product. To prevent this, some constraints for retailer profit are considered. These results also depend on their willingness to take risk coming from demand uncertainty.

Literature on QR Lau HS and Lau AHL (2000) adds the issue of valuation uncertainty to their previous research and examine customer demand uncertainty. Khouja (2001) examines the effect of large order quantities on the single-period problem. Due to the autocorrelation between the current season and following season, if the retailer order too much in this season and have discounted sales near the end of season, this will create a reduction on the demand of next period.

Literature on QR Lau HS and Lau AHL (2002) tries to expand the paper of Iyer and Bergen (1997) by including (w), (r) and (p) as decision variables. If w is high, then Q will be smaller. However, if demand uncertainty is low, then Q will not be sensitive to w. This paper assumes no coordination between parties, however, a channel coordination may give better results.

Fixed-value Variables < 0 (high α) > 0 (low α) Literature on QR Decision Variables Fixed-value Variables ∂M/∂σ ∂R/∂σ ∂T/∂σ Q w, r=0, p < 0 if SL < 0.5 < 0 always < 0 if SL > 0.5 > 0 if SL > 0.5 > 0 if SL < 0.5 Q,w r=0,p depends on p , π , s Q,p,w r=0 < 0 in most cases < 0 (high α) > 0 (low α) w,r,p,Q - < 0 (high α) > 0 (low α)

Accurate Response A new approach to decrease the forecast accuracy by dividing the period into two or more small periods and making the orders after first subperiod with the information gathered and demand estimated in that period.

Accurate Response Stochastic D1 Stochastic D2 t1 t2 te Initial Order Q1 arrives Replenishment Q2 arrives

Accurate Response Although eventually two decision variables Q1 and Q2 need to be determined, at the beginning of period there is only one decision variable Q1 to determine. Q2 should not be determined at the start of the season, but during the mid-season, with the demand information gathered during this time. However, it is essential to coordinate the determination of Q1 and Q2. Q1 can be determined correctly only if all the Q2’s possibilities are fully considered.

Accurate Response If S is the surplus left from subperiod 1,then: Q2 = 0 if S >= E(D2) Q2 = E(D2) – S if 0 < S < E(D2) Q2 = E(D2) if S = 0 E(D2): Expected demand of the subperiod 2 computed with the demand information in subperiod 1.

Accurate Response Some Benefits: Accurate Forecasts Reduced mismatches between supply and demand Lower costs Increased Profits Lower Inventory levels

Literature on Acc. Res. Fisher and Raman (1996) analyze accurate response and provides a methodology for performing this more complex response-based production planning. Correlation between first and second subperiods are found. A method to estimate demand of first subperiod is proposed. Since there is limited capacity for the production for second subperiod, a capacity constraint is used. Minimum product lot size is used to make setups and fixed cost feasible.

Literature on Acc. Res. Lau HS and Lau AHL (1996) consider the reordering strategies for a single-period problem. Normal and Beta demand distributions are considered. Q1 is always less than Q and Zt is always greater than Z. For same c values, as demand uncertainty increases, the ability to delay ordering decisions become more valuable. Percentage increase in expected profit increases with the (c/p) ratio and it decreases as the profit margin decreases. Percentage increase in Total Volume is negative for small c-values but positive for larger c.

Literature on Acc. Res. Lau HS and Lau AHL (1997) investigate the same condition with uniform demand distribution and get similar results. Donohue (2000) considers the accurate response including two-production mode system in which the retailer may choose the production-mode.

Proposed Model Integration of quick response and accurate response systems. Single period divided into two subperiods. Two alternatives: long leadtime with c1 short leadtime with c2 (c2>c1) Order arrival time for subperiod 2 (t2) is variable. Return credits (r) are included.

Proposed Model Stochastic D1 Stochastic D2 t1 Variable t2 te Initial Order Q1 arrives with c1 Replenishment Q2 arrives with c2

Proposed Model Q1 should be predetermined by some forecasting methods or information taken from other companies. Q1 will be ordered in a long-lead time with a cost of c1, but this ordering should be long before the season begins. Q2 will be ordered and delivered with QR with a cost of c2 to reduce lead time. After the season started, actual demand data will be collected and demand distribution will be found.

Proposed Model Using this demand distribution and using the estimated lead time of QR, demand during lead time can be estimated. Since the demand during lead time is known, time to order for second period is known. Q2 can be estimated more accurately by using demand distribution of subperiod 1. At time (t2-L), which is the beginning of subperiod 2 minus lead time of QR, second order will be placed.

Possible Results In the optimum solution, Q1 will be as large as possible because of less ordering cost. There won’t be any surplus left from subperiod 1, unless Q1 is greater than the total demand of season. Overall profit of the system will be increased. Since Q2 is accurate, inventory leftovers and stockouts will be reduced.

Future Research c1 and c2 can be variable. (manufacturer can determine these values) Effects of ordering with QR before subperiod 1 can be examined. Capacity of the manufacturer can be considered before ordering Q2. Lot size issues to make both orders can be considered. Multiple subperiods can be used.

Conclusion Both Quick Response and Accurate Response are new systems providing better forecasts. In a competitive environment, these systems can be very useful. Past studies show that these systems are profitable. Proposed model may be useful and have benefits relating to apparel and other single-period industries.

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