The Future of Retail Operations

Retailing consists of all the activities associated with the selling of goods to the final consumer. In this article, we review the research on retail operations published in Manufacturing & Service Operations Research (M&SOM) since 1999. We then discuss the current retail landscape and the new research directions it offers, in which M&SOM can play a prominent role

CROSS-CULTURAL DIFFERENCES IN ONLINE PRICE ELASTICITY

This study empirically derives price elasticity estimates in fashion e-commerce in six European countries and explores their relationship within the given cultural context using Hofstede’s cultural dimensions theory. The authors use a novel data set consisting of more than two million actual sales transactions provided by a leading European fashion e-commerce company for regression analysis and find considerable cross-country differences in price elasticity. Furthermore, cultural dimensions power distance, individualism, and masculinity relate to a less distinct price elasticity whereas long-term orientation pertains to the opposite. Lastly, the study analyzes profit implications for multinational corporations employing cross-country price discrimination

The Top-Dog Index: A New Measurement for the Demand Consistency of the Size Distribution in Pre-Pack Orders for a Fashion Discounter with Many Small Branches

We propose the new Top-Dog-Index, a measure for the branch-dependent historic deviation of the supply data of apparel sizes from the sales data of a fashion discounter. A common approach is to estimate demand for sizes directly from the sales data. This approach may yield information for the demand for sizes if aggregated over all branches and products. However, as we will show in a real-world business case, this direct approach is in general not capable to provide information about each branch's individual demand for sizes: the supply per branch is so small that either the number of sales is statistically too small for a good estimate (early measurement) or there will be too much unsatisfied demand neglected in the sales data (late measurement). Moreover, in our real-world data we could not verify any of the demand distribution assumptions suggested in the literature. Our approach cannot estimate the demand for sizes directly. It can, however, individually measure for each branch the scarcest and the amplest sizes, aggregated over all products. This measurement can iteratively be used to adapt the size distributions in the pre-pack orders for the future. A real-world blind study shows the potential of this distribution free heuristic optimization approach: The gross yield measured in percent of gross value was almost one percentage point higher in the test-group branches than in the control-group branches.Comment: 22 pages, 15 figure

Leveraging downstream data in the footwear/apparel industry

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections."June 2007."Includes bibliographical references (leaf 65).Retailers collect information regarding consumer purchases on a transactional basis. This data is not completely being leveraged by manufacturers in the footwear and apparel industry to increase on-shelf availability. However, certain apparel and consumer products companies have developed best-in-class methods for collecting and utilizing data to enhance supply chain visibility and to drive increased sales. A description of these best-in-class practices is provided, strategies to use the data are presented, and the importance of collaboration among supply chain partners is discussed. Further, point of sale data from a footwear and apparel manufacturer is analyzed to illustrate how the data can be leveraged to predict subsequent season sales, to improve forecasting accuracy, and to allocate replenishment inventory more effectively.by Jeffrey Edward Axline [and] Brian Joseph Lebl.M.Eng.in Logistic

Quantum prices

Many retailers practice an extreme form of discrete pricing defined as quantum prices: differentiated products are priced using few and sparse price buckets. To show this, online data was collected for 350,000 products from over 65 fashion retailers in the U.S. and the U.K. This pricing strategy is observed within categories and across categories (i.e., similar or even disparately distinct products like jeans and bags have an identical price), as well as in product introductions, where new products come in at previous price buckets. Normalized indices indicate substantial price clustering after controlling for popular prices, convenient prices, assortment size, or digit endings. Quantum prices have implications for price adjustments through product shares, markdown prices, and for the law-of-one-price. A behavioral model of price salience and recall is discussed

Zara and Benetton: Comparison of two business models

The project analizes and compares two very important and diferent business models in fast fashion industry: Zara y Benetton models. Their models are so diferent but have been a great success, due to their capacity to respond quickly to demand of the market, then due to their flexibility. In this regard, the project also demonstrates how information sharing have a big role to the success of a company. It improves the efficiency of a company and helps to achieve the customer satisfaction . To achieve a good sharing information, it' s important a good and strenght relationship between manufacturer and retailer

Hybrid Model for It Investment Analysis: Application to Rfid Adoption in the Retail Sector

One of the major obstacles in Information Technology (IT) adoption is its return on investment analysis. IT benefits in organizations are hard to measure and are usually realized over time. System dynamics approach has been used in IT literature to identify the impact of IT on business processes. Given benefits of any IT system in organizations, however, there is a high degree of uncertainty in achieving such benefits. Managerial flexibility in decision making process of implementing a new IT helps managers to overcome this uncertainty over time. Traditional cost benefit analysis such as NPV that is typically used to value any technology is unable to value managerial flexibilities while real options theory offers a model that can value a new investment as uncertainties about the system decreases over time. In this dissertation, we are proposing a new hybrid model for IT return on investment (ROI) that combines system dynamics and real options as two major techniques in economics of IT. This robust hybrid model takes advantages of both techniques while overcoming their weaknesses. We propose a systems dynamic solution to simulate the way an IT influences and improves an organization to be able to estimate the parameters used in the real options model. The hybrid model is used to find the best time for investing in item-level RFID in the retail sector.The results of return on investment analysis on item-level investment show that the variable cost of investment that is the tag prices dominates the return on investment. Other factors such as product unit price and consequently type of retail stores are important as well. The system dynamics simulation provided some major parameters of the real options model such as the expected payoffs and volatility of the expected payoffs that were hard to find in the literature.Business Administration (MBA

Mitigating demand risk of durable goods in online retailing

Purpose An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost. Design/methodology/approach Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer. Findings Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction. Research limitations/implications Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss. Practical implications The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner. Originality/value The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information