Optimal ordering and pricing in a quick response system

Serel, Doğan A.

doi:10.1016/j.ijpe.2009.04.020

Cited by 37 publications

(20 citation statements)

References 37 publications

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“…A common practice utilizing information update in a QR system is to split the traditional single purchase order into two lots (see, e.g., Serel 2009). In general, uncertainty in customer demand forecast is higher when the forecasting is conducted a long time before the selling season starts.…”

Section: The Literaturementioning

confidence: 99%

Innovative Quick Response Programs in Logistics and Supply Chain Management

Cheng

Choi

2010

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Section: The Literaturementioning

confidence: 99%

Innovative Quick Response Programs in Logistics and Supply Chain Management

Cheng

Choi

2010

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“…Choi et al (2003) study a similar problem in which the demand is assumed to follow a normal distribution with an unknown mean, and a Bayesian approach is used to update the estimate of the distribution mean at the second stage. Along the same line, Serel (2009) explores the impact of price-sensitive demand on the retailer's ordering policy. Choi et al (2006) extend their previous work to the case where both the mean and variance of the demand distribution are unknown, and estimated by the retailer using a two-parameter Bayesian updating method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We note that s x corresponds to a 0:5 ii for product i that has an independent demand distribution in the multi-product problem (see e.g., Serel, 2009). We can write the order amount at time 2, Q 2 , as…”

Section: Single Product Problem With a Budget Constraintmentioning

confidence: 99%

Multi-item quick response system with budget constraint

Serel

2012

International Journal of Production Economics

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a b s t r a c tQuick response mechanisms based on effective use of up-to-date demand information help retailers to reduce their inventory management costs. We formulate a single-period inventory model for multiple products with dependent (multivariate normal) demand distributions and a given overall procurement budget. After placing orders based on an initial demand forecast, new market information is gathered and demand forecast is updated. Using this more accurate second forecast, the retailer decides the total stocking level for the selling season. The second order is based on an improved demand forecast, but it also involves a higher unit supply cost. To determine the optimal ordering policy, we use a computational procedure that entails solving capacitated multi-item newsboy problems embedded within a dynamic programming model. Various numerical examples illustrate the effects of demand variability and financial constraint on the optimal policy. It is found that existence of a budget constraint may lead to an increase in the initial order size. It is also observed that as the budget available decreases, the products with more predictable demand make up a larger share of the procurement expenditure.

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“…Style goods are essentially seasonal products such as toys and fashion apparel. According to Serel (), by updating the demand forecast as actual market data become available, quick‐response systems allow for adjusting the stock of a retail item as closely as possible to the optimal level. Hence, the number of ordering opportunities is limited, generally to one or two.…”

Section: Introductionmentioning

confidence: 99%

Single‐Versus Two‐Opportunity Price Postponement and Ordering Strategies of a Seasonal Product

Herbon

2017

Decision Sciences

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We compare two strategies of ordering and pricing postponement for a seasonal product. In the single‐opportunity strategy, the retailer orders all base‐stock prior to the beginning of the season and sets the price when the season begins and demand information becomes available. In the two‐opportunity strategy, the retailer orders only some of her stock before the season, and places an additional order after the season starts; the second‐order quantity and the prices for each quantity of base‐stock are determined according to currently available demand information. The latter strategy can accommodate unexpected demand changes that occur late in the selling season. We provide sufficient conditions in which the two‐opportunity strategy is preferable to the single‐opportunity strategy. Each problem is analyzed using a multistage programming approach, and optimal prices as well as optimality conditions for the different base‐stock levels are obtained. In contrast to previous studies, our model addresses the effect of the timing of the arrival of the second order and accounts for holding costs over time as well as a reputation penalty associated with lost sales. Moreover, it does not ignore fixed costs associated with order placement and processing of demand information. A numerical example and sensitivity analysis of the key parameters show that the ratio between the optimal expected profits obtained under the single‐opportunity strategy and under the two‐opportunity strategy is lower for higher values of the holding costs or reputation penalty. Moreover, the ratio is higher for later splitting points as well as for higher fixed costs.

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Optimal ordering and pricing in a quick response system

Cited by 37 publications

References 37 publications

Innovative Quick Response Programs in Logistics and Supply Chain Management

Innovative Quick Response Programs in Logistics and Supply Chain Management

Multi-item quick response system with budget constraint

Single‐Versus Two‐Opportunity Price Postponement and Ordering Strategies of a Seasonal Product

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