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This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice.

Sumatriptan, a drug widely used to alleviate migraine headaches, has several somatosensory adverse effects, including tactile allodynia. To understand whether sumatriptan affects sensory and affective circuitries simultaneously, we investigated the responses of 12 healthy volunteers to electrical stimuli after infusion with either sumatriptan or saline. Using a double-blind crossover study design, we used functional magnetic resonance imaging (fMRI) to measure brain activation in different areas during electrical stimulation. The visual analog scale (VAS) and short-form McGill pain questionnaire (SF-MPQ) were used to rate stimulation-evoked sensations and affections after drug administration. VAS rating, SF-MPQ, and block fMRI were all performed in each subject during sumatriptan and saline injection. Echo-planar imaging sequences were used to determine the whole-brain blood oxygenation level-dependent signal of the entire brain. Our results showed that sumatriptan predominantly activated regions in the medial pain system and smaller regions in the lateral pain system. These regions included the secondary somatosensory cortex (SII), anterior insular cortex, orbitofrontal cortex, medial thalamus, cerebellar supravermis, dentate nucleus, and the majority of the anterior cingulate cortex (ACC). In contrast, activation following saline administration was observed primarily in the lateral pain system, including the primary sensory cortex, lateral SII, posterior insular cortex, anterior ACC, and lateral thalamus. Importantly, we found that VAS ratings and MPQ scores were increased after sumatriptan infusion, but not after saline administration. Our fMRI, VAS, and SF-MPQ findings suggest that sumatriptan plays a significant role in the affective dimension of pain and a minor role related to sensory discrimination.

In order to reduce business latency and packet loss rate, to improve the throughput of integration of heterogeneous wireless network, to achieve load diversification and to improve end quality of service, while there are many problems on the dropout rate of the traditional load balancing algorithm in processing throughput, delay and business, therefore, gateway load balancing algorithm is proposed in this paper. All terminals in the access network can reflect the effectiveness of the average network load level of the network, this algorithm will gain weight of the load in the network of small business terminal to switch to the network load which is light. First, it defines the heterogeneous networks and network terminal payoff function utility function, which are used to characterize the experience and network QOS terminal load situation, and then presents the specific processes of the gateway load balancing algorithm. Finally, switching decisional load balancing algorithm, proposed by Yan X, etc. is compared with simulation experiments and it shows that: the proposed gateway load balancing algorithm has strong robustness to achieve network load balancing and to achieve a balanced use of network resource.

In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts, but no research has put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra- and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 14.3 percent over the model using only the SKU’s own predictors. But of the improvements achieved, 88.1 percent of it comes from the intra-category information, and only 11.9 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.