We incorporate information flow between a supplier and a retailer in a two-echelon model that captures the capacitated setting of a typical supply chain. We consider three situations: (1) a traditional model where there is no information to the supplier prior to a demand to him except for past data; (2) the supplier knows the (s, S) policy used by the retailer as well as the end-item demand distribution; and (3) the supplier has full information about the state of the retailer. Order up-to policies continue to be optimal for models with information flow for the finite horizon, the infinite horizon discounted and the infinite horizon average cost cases. Study of these three models enables us to understand the relationships between capacity, inventory, and information at the supplier level, as well as how they are affected by the retailer's (S - s) values and end-item demand distribution. We estimate the savings at the supplier due to information flow and study when information is most beneficial.information sharing, (s, S) policy, optimal policies, capacitated production-inventory model, infinitesimal perturbation analysis

We investigate the impact of advance notice of product returns on the performance of a decentralised closed loop supply chain. The market demands and the product returns are stochastic and are correlated with each other. The returned products are converted into "as-good-as-new" products and used, together with new products, to satisfy the market demand. The remanufacturing process takes time and is subject to a random yield. We investigate the benefit of the manufacturer obtaining advance notice of product returns from the remanufacturer. We demonstrate that lead times, random yields and the parameters describing the returns play a significant role in the benefit of the advance notice scheme. Our mathematical results offer insights into the benefits of lead time reduction and the adoption of information sharing schemes.

Many organizations have only recently recognized that sharing information with other members in their supply chain can lead to signficant reduction in the total costs.Usually these information flows are incorporated into existing operating policies at the various parties.In this paper we argue that, in some cases, it may be necessary to change the way the supply chain is managed to make complete use of the information flows. We support this argument by analyzing a supply chain containing a capacitated supplier and a retailer facing i.i.d. demands. In addition there are fixed ordering costs between the retailer and the supplier.In this setting, we consider two models: (1) the retailer is using the optimal (s,S) policy and providing the supplier information about her inventory levels; and (2) the retailer, still sharing information on her inventory levels, orders in a period only if by the previous period the cumulative end-customer demand since she last ordered was greater than \delta . Thus, in Model 1, information sharing is used to supplement existing policies; while, in Model 2, we have redefined operating policies to make better use of the information flows. We will show, via a detailed computational study, that the total supply chain costs of Model 2 are 10.4% lower, on the average, than that of Model 1. We noticed that this reduction in costs is higher at higher capacities, higher supplier penalty costs, lower retailer penalty costs, moderate values of set-up cost, and at lower end-customer demand variances.supply chain management, information sharing, inventory control

PurposeThe purpose of this research is to develop a method for measuring police efficiency.Design/methodology/approachThe design of this paper is to apply the technique of Data Envelopment Analysis (DEA), a comparative or relative efficiency measuring mechanism to police‐work‐related data from India. This application provides a rationale for identifying good performance practices. It helps in generating targets of performance, the optimum levels of operations, role models that inefficient departments can emulate and the extent to which improvements can be made over a period of time.FindingsThe paper measures the performances of State police units in India and the results suggest ways in which some State police departments can improve their overall efficiency.Practical implicationsThe paper suggests ways in which the efficiency of any unit of criminal justice systems may be formulated and compared across different units of the system.Originality/valueThe value is that it introduces a new technique to police practitioners and researchers and demonstrates its efficacy by case analysis from India.

Customer reviews submitted at Internet travel portals are an important yet underexplored new resource for obtaining feedback on customer experience for the hospitality industry. These data are often voluminous and unstructured, presenting analytical challenges for traditional tools that were designed for well-structured, quantitative data. We adapt methods from natural language processing and machine learning to illustrate how the hotel industry can leverage this new data source by performing automated evaluation of the quality of writing, sentiment estimation, and topic extraction. By analyzing 5,830 reviews from 57 hotels in Moscow, Russia, we find that (i) negative reviews tend to focus on a small number of topics, whereas positive reviews tend to touch on a greater number of topics; (ii) negative sentiment inherent in a review has a larger downward impact than corresponding positive sentiment; and (iii) negative reviews contain a larger variation in sentiment on average than positive reviews. These insights can be instrumental in helping hotels achieve their strategic, financial, and operational objectives.