استراتژی ارائه خدمات بهینه برای خرید اینترنتی با تقاضای مصرف کننده وابسته به زمان
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|11829||2006||25 صفحه PDF||سفارش دهید||11102 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Transportation Research Part E: Logistics and Transportation Review, Volume 42, Issue 6, November 2006, Pages 473–497
This study attempts to optimize a delivery service strategy for Internet shopping by considering time-dependent consumer demand, demand–supply interaction and consumer socioeconomic characteristics. A nonlinear mathematical programming model is formulated for solving the optimal number and duration of service cycles for discriminating strategy by maximizing profit subject to demand–supply interaction. An example is employed to demonstrate the application of the model. Results suggest that discriminating service strategy is a better strategy in response to time-dependent consumer demand than uniform strategy. Finally, the proposed model is demonstrated to yield more profit than models that do not consider variations in consumer demand or demand–supply interaction.
Electronic data interchange (EDI) and related technologies have made it more efficient to transmit information to suppliers. At the same time, information flow-based Internet shopping has markedly improved consumer service by reducing order processing time and providing delivery information. Since real-time consumer demand is processed via the Internet, operator inventory costs are reduced by ordering goods from wholesalers or manufactures and shipping them directly to consumers. However, high order frequency and small order quantity that characterize consumer Internet shopping behavior make it expensive to deliver goods to individual consumers (Huppertz, 1999). With fixed transportation costs for each shipment, the average logistics cost per item decreases with increasing shipment size. Therefore, a larger quantity of goods will accumulate with longer shipping cycles, which also results in an increased delay in receiving ordered goods, thus reducing consumer intention to shop via the Internet. The above process involves a trade-off between consumer demands and operator logistics costs. The goal of delivery strategies is to reduce logistics costs and satisfy consumer needs. A crucial factor in optimizing a delivery service strategy is consumer demand. The assumption of constant demand is highly controversial, since in reality demand varies with time, space, and consumer socioeconomic characteristics. For example, peak demand for food products is likely to occur at lunchtime. Serving consumers via uniform shipping cycles without considering variations in cumulative quantities ordered during each shipping cycle may result in high logistics costs under time-dependent consumer demand. Conversely, shipping cycle has a dramatic influence on consumer intention to shop via the Internet because it determines delay in receiving ordered goods. When a consumer orders goods from an Internet store, they typically receive delivery information with respect to each service cycle, which is posted on the Internet. Upon the completion of the service cycle, the goods ordered during that cycle are shipped to consumers. Thus, service cycles coincide with shipping cycles for Internet store operators. In addition to time-dependent consumer demand, consumer demand for Internet shopping is also characterized by socioeconomic characteristics, and temporal and spatial variations. Even when served by the same service cycles, consumers with different characteristics perceive Internet shopping differently, which may further influence consumer demand for Internet store goods and, thus, profit. In summary, how to determine an optimal delivery service strategy for Internet shopping by considering demand–supply interaction, time-dependent consumer demand and consumer characteristics has become important. Previous empirical studies have investigated the impacts of delivery-related issues on consumer satisfaction with Internet shopping (e.g., Rabinovich, 2004, Esper et al., 2003 and Rabinovich and Bailey, 2004). Studies of consumer choices between shopping modes focused primarily on investigating the influences of demand and supply attributes on consumer intention to shop via the Internet (e.g., Sim and Koi, 2002 and Bhatnagar and Ghose, 2004). Some studies have quantified consumer demand for Internet store goods and costs under different shipping strategies (Khouja, 2001, Hsu et al., 2003 and Chen, 2001). However, few have integrated issues such as consumer socioeconomic characteristics, time-dependent consumer demand, demand–supply interaction and the 24-h nature of Internet shopping into their models. Discriminating service strategy proposed in this study differs significantly from the traditional and typical uniform service strategy in which all consumers are served according to the same delivery cycle. Periods with considerable consumer demand suggest that frequent and short service cycles are suitable and may stimulate consumer demand for Internet store goods because of reduced delay in receiving ordered goods; this perspective also implies that long service cycles are suitable when demand is very low. Such an approach would reduce logistics costs and boost profit. The Internet store in this study is assumed to operate as a retailer, ordering a batch of goods from wholesalers or manufactures and distributing these goods to consumers. Delay in receiving ordered goods is determined here as the time between consumers ordering and receiving goods, and depends on delivery cycles which include lead time for processing and handling. This study explores how to optimize a delivery service strategy for Internet shopping in terms of service cycle frequency and duration by considering time-dependent consumer demand and demand–supply interaction. The model applies mathematical programming methods and compares profit between using discriminating and uniform service strategies thereby identifying the optimal strategy for Internet store operators. This study uses R-company selling flowers via the Internet in Taiwan, as an example to demonstrate the application of the model. The remainder of this paper is organized as follows. Section 2 reviews the literature on Internet shopping and physical distribution problems. Section 3 formulates the consumer choice probability model for Internet shopping and aggregates consumer demand for Internet store goods in each service cycle. Nonlinear programming problems are formulated in Section 4 for determining the optimal number and duration of service cycles for discriminating service strategy and uniform service strategy by maximizing Internet store profit subject to demand–supply equality. In Section 5, a case study and numerical examples are presented to demonstrate the application of the model and the effects of changing in key parameters on the optimal solution. Finally, Section 6 presents a summary of study findings and conclusions.
نتیجه گیری انگلیسی
Recent studies have investigated Internet shopping carriers and provider issues and their effects on consumer services and operating strategies. Most of these empirical studies dealt these issues by collecting empirical data and testing hypotheses. This study further develops a mathematical programming model that can determine the optimal number and duration of service cycles for Internet shopping by exploring demand–supply interaction and time-dependent consumer demand. This study shows how demand–supply interaction can be carefully considered in advance of solving delivery service problems. This study also shows how variations in consumer socioeconomic, temporal and spatial distributions influence consumer demand for Internet store goods and, thereby, profit. The results show discriminating service strategy yields better objective values than uniform service strategy, from which indicates that the Internet store operator and consumers may benefit from spacing service cycles according to time-dependent consumer demand. This finding also suggests that in practice an Internet store operator should employ frequent and short service cycles for periods with increased demand and long service cycles when demand is very low. The results further show that when transportation cost increases, the optimal frequent service cycles remains the same or increases. This finding indicates that the impact of reduced consumer demand for Internet store goods on profit is more significant than the increased logistics cost and, therefore, Internet store operators should employ more frequent service cycles to attract consumers and offset the influence of increasing costs. The results show that variations in consumer socioeconomic, temporal and spatial characteristics play important roles in determining the optimal number and duration of service cycles and that not considering these variables yields reduced profit. This finding implies that the Internet store operator should carefully investigate the temporal and spatial distribution of consumer demand, income and needs and provide a delivery service strategy tailored to these criteria. For example, service cycles could be intensely spaced for a consumer area or region with numerous retail stores or during periods of large consumer demand. The finding also implies that consumers with high income are more sensitive to delivery delay than to the price of goods and, thus, serving these consumers with frequent service cycles for high price of goods could yield increased profit. Conversely, this study shows that without considering demand–supply interaction, the Internet store operator typically minimizes average logistics cost per item by assuming inelastic demand and then applying least-frequent service cycles. However, this strategy yields lower profit than strategies that consider demand–supply interaction. In this study, demand–supply interaction is examined in a way that reduces logistics cost due to a large accumulation of goods based on long and less frequent service cycles; however, this strategy also results in an increased delay in receiving ordered goods, thus reducing consumer intention to shop via the Internet. Consequently, this finding in this study implies that the delivery service strategy may not only affect consumer demand for Internet store goods, but also operator logistics costs. In practice, Internet store operators may investigate the effects of service cycles on consumer demand for Internet store goods and its relationship with logistics costs. This study can be extended in several ways. On the demand side, this study focused only on choice probabilities for two shopping modes rather than that among shopping stores within each mode. Future studies may use the joint or nested logit models to determine consumer choice probabilities for a specific Internet store. Second, the case study is based on an Internet store selling flowers in Taiwan with a study period of one operating day. Future studies may apply the model to different goods, such as computers and extend the study period beyond one day. Such studies would need to examine the impact of different characteristics of goods on consumer intention toward Internet shopping and calibrate a consumer demand function. Finally, as Chen (2001) suggested, profit may be improved by segmenting the market and then serving different market segments with different combinations of prices and service cycle frequencies. Future studies may expand this study’s model and address this issue by determining an optimal segmenting strategy and investigating the relative influences of the price of goods and delay in receiving ordered goods on consumer intention to shop via the Internet in the contexts of these different segments.