Efficient shift scheduling in the retail sector through two-stage optimization

Efficient workforce scheduling has an important impact on store profit and customer service. Standard scheduling problems do not recognize the effect of staff availability on customer sales, however, even though the latter is an important factor in the retail sector. In this paper a two-stage model is proposed for this purpose. In the first stage a sales response model is used to specify hourly staff requirements. The output of the sales response model is then used as the input of a mixed integer optimization model, which finds an optimum assignment of the staff to daily shifts. Simulations are used to validate the sales response function, and to revise the model for more accurate results. In the simulations, customer arrivals and sales response error values are generated using appropriate distribution functions. As a case study the proposed model is applied to a Turkish retailer in the apparel sector.     

The value of competitive information in forecasting FMCG retail product sales and the variable selection problem

Sales forecasting at the UPC level is important for retailers to manage inventory. In this paper, we propose more effective methods to forecast retail UPC sales by incorporating competitive information including prices and promotions. The impact of these competitive marketing activities on the sales of the focal product has been extensively documented. However, competitive information has been surprisingly overlooked by previous studies in forecasting UPC sales, probably because of the problem of too many competitive explanatory variables. That is, each FMCG product category typically contains a large number of UPCs and is consequently associated with a large number of competitive explanatory variables. Under such a circumstance, time series models can easily become over-fitted and thus generate poor forecasting results.     

Demand seasonality in retail inventory management

We investigate the value of accounting for demand seasonality in inventory control. Our problem is motivated by discussions with retailers who admitted to not taking perceived seasonality patterns into account in their replenishment systems. We consider a single-location, single-item periodic review lost sales inventory problem with seasonal demand in a retail environment. Customer demand has seasonality with a known season length, the lead time is shorter than the review period and orders are placed as multiples of a fixed batch size. The cost structure comprises of a fixed cost per order, a cost per batch, and a unit variable cost to model retail handling costs. We consider four different settings which differ in the degree of demand seasonality that is incorporated in the model: with or without within-review period variations and with or without across-review periods variations. In each case, we calculate the policy which minimizes the long-run average cost and compute the optimality gaps of the policies which ignore part or all demand seasonality. We find that not accounting for demand seasonality can lead to substantial optimality gaps, yet incorporating only some form of demand seasonality does not always lead to cost savings. We apply the problem to a real life setting, using Point-of-Sales data from a European retailer. We show that a simple distinction between weekday and weekend sales can lead to major cost reductions without greatly increasing the complexity of the retailer’s automatic store ordering system. Our analysis provides valuable insights on the tradeoff between the complexity of the automatic store ordering system and the benefits of incorporating demand seasonality.     

Vendor managed inventory contracts – coordinating the supply chain while looking from the vendor’s perspective

The paper studies coordination of a supply chain when the inventory is managed by the vendor (VMI). We also provide a general mathematical framework that can be used to analyze contracts under both retailer managed inventory (RMI) and VMI. Using a simple newsvendor scenario with a single vendor and single retailer, we study five popular coordinating supply chain contracts: buyback, quantity flexibility, quantity discount, sales rebate, and revenue sharing contracts. We analyze the ability of these contracts to coordinate the supply chain under VMI when the vendor freely decides the quantity. We find that even though all of them coordinate under RMI, quantity flexibility and sales rebate contracts do not generally coordinate under VMI. Furthermore, buyback and revenue sharing contracts are equivalent. Hence, we propose two new contracts which coordinate under VMI (one of which coordinates under RMI too, provided a well-known assumption holds). Finally, we extend our analysis to consider multiple independent retailers with the vendor incurring linear or convex production cost, and show that our results are qualitatively unchanged.     

In–Out Algorithm for assortment planning under a ranking-based consumer choice model

We consider a retailer’s assortment planning problem under a ranking-based consumer choice model. The retailer incurs a fixed carrying cost per product offered, a substitution penalty cost for each customer who does not purchase his first choice, and a penalty cost on lost sales. We develop an effective In–Out Algorithm to identify the optimal solution. The extensive numerical study shows that the algorithm performs well, and is more than 10,000 times faster than enumeration on problems with 20 products.     

Should retail stores also RFID-tag ‘cheap’ items?

Despite their implementations in a wide variety of applications, there are very few instances where every item sold at a retail store is RFID-tagged. While the business case for expensive items to be RFID tagged may be somewhat clear, we claim that even ‘cheap’ items (i.e., those that cost less than an RFID tag) should be RFID tagged for retailers to benefit from efficiencies associated with item-level visibility. We study the relative price premiums a retailer with RFID tagged items can command as well as the retailer’s profit to illustrate the significance of item-level RFID-tagging both cheap and expensive items at a retail store. Our results indicate that, under certain conditions, item-level RFID tagging of items that cost less than an RFID tag has the potential to generate significant benefits to the retailer. The retailer is also better off tagging all items regardless of their relative price with respect to that of an RFID tag compared to the case where only the expensive item is RFID-tagged.     

Environmental implications for online retailing

Recent press has highlighted the environmental benefits associated with online shopping, such as emissions savings from individual drivers, economies of scale in package delivery, and decreased inventories. We formulate a dual channel model for a retailer who has access to both online and traditional market outlets to analyze the impact of customer environmental sensitivity on its supply. In particular, we analyze stocking decisions for each channel incorporating price dependent demand, customer preference/utility for online channels, and channel related costs. We compare and contrast results from both deterministic and stochastic models, and utilize numerical examples to illustrate the implications of industry specific factors on these decisions. Finally, we compare and contrast the findings for disparate industries, such as electronics, books and groceries.     

Measuring retail company performance using credit scoring techniques

This paper discusses models for evaluating credit risk in relation to the retailing industry. Hunt’s [Hunt, S.D., 2000. A General Theory of Competition. Sage Publications Inc., California] Resource–Advantage Theory of Competition is used as a basis for variable selection, given the theory’s relevancy to retail competition. The study focuses on the US retail market. Four standard credit scoring methodologies: Naïve Bayes, Logistic Regression, Recursive Partitioning and Artificial Neural Network, are compared with Sequential Minimal Optimization (SMO), using a sample of 195 healthy companies and 51 distressed firms over five time periods from 1994 to 2002.     

Allocation of flexible and indivisible resources with decision postponement and demand learning

We consider a firm that uses two perishable resources to satisfy two demand types. Resources are flexible such that each resource can be used to satisfy either demand type. Resources are also indivisible such that the entire resource must be allocated to the same demand type. This type of resource flexibility can be found in different applications such as movie theater complexes, cruise lines, and airlines. In our model, customers arrive according to independent Poisson processes, but the arrival rates are uncertain. Thus, the manager can learn about customer arrival rates from earlier demand figures and potentially increase the sales by postponing the resource allocation decision. We consider two settings, and derive the optimal resource allocation policy for one setting and develop a heuristic policy for the other. Our analysis provides managerial insights into the effectiveness of different resource allocation mechanisms for flexible and indivisible resources.     

Goal programming model for grocery shelf space allocation

The highly competitive grocery retail industry has annual sales of roughly half a trillion dollars in the US. While gross margins average about 28% of sales, net profits after taxes are only 1% industry-wide, causing retailers to continually search for operational improvements that increase profitability and improve customer service. One important decision that affects both of these goals is how to allocate shelf space to different products.