Retail atmospherics have been well studied to have both functional and emotional impact on the behavioural intent of shoppers. The usage of well-orchestrated retail servicescape with its expanding canvas, both inside and outside the retail premise, has been targeted to induce subliminal perception of comfort and convenience in the mind of shoppers. Modern retail practice has incorporated customer relationship management (CRM), a pivotal business analytical process, to strengthen their interaction with shoppers. This paper attempts to gather empirical evidence on the possible mediating effects of CRM dimensional performance on the emotional orientation of shoppers, apprehended to be antecedent to favourable shopping behaviour. Appropriate statistical applications, following adequate literature survey, were deployed to test the hypotheses and the robustness of the default model. The results were indicative of strong moderating impact of CRM dimensional performance in augmenting emotional behaviour of customers to induce a fovourable behavioural intent. The default model also holds good confirming the causal convergence of constructs.

The purpose of in this paper is to study demand fluctuation of dairy product and the forecasting practices that have been used by Raipur Dugdh Sangh (Devbhog dairy industry) in Chhattisgarh. The objective is to detect how these industries have used forecasting method, what are the main factors that influence their choice and what are the main difficulties in the use of forecasting methods. Based on literature survey, weekly data collected over October 2011 to October 2012 from Raipur Dugdh Sangh (Devbhog dairy industry) in Chhattisgarh. Data were analyzed by statistics technique using the Microsoft excel software. The result shows that the demand of milk product fluctuated over the period of time. The factors that influence the choice of forecasting model are the type of product, time spent in forecasting and main difficulties are the availability of software.

Crude oil price volatility dynamics are governed by nonlinear and chaotic behaviour. This paper presents and compares the performance of four hybrid systems used to estimate and predict crude oil price volatility data. A GARCH family model is employed to estimate oil price volatility data and the Elman artificial neural network (ENN) system is used to model and predict the obtained data. Indeed, unlike previous studies found in the literature, recurrent artificial neural networks are chosen in this paper to model and predict future crude oil price volatility data estimated by GARCH family models since they are nonlinear systems capable of learning noisy and nonstationary data. In particular, four hybrid systems are tested and compared; including the GARCH-ENN, EGARCH-ENN, APARCH-ENN, and TARCH-ENN system. Using Brent crude oil price data, the obtained out-of-sample simulation results indicate that all hybrid systems provide very accurate forecasts of Brent future volatility. In addition, they show evidence of the superiority of the GARCH-ENN system over the EGARCH-ENN, TARCH-ENN, and APARCH-ENN systems. The presented four hybrid systems achieved very low forecasting errors. Thus, they could be effective in oil industry management and applications.

Hub covering problem is one of the most popular areas of research due to wide ranges of applications in different service or manufacturing industries. This paper considers a multi-objective hub covering location problem under congestion. The proposed study of this paper considers two objectives where the first one minimizes total transportation cost and the second one minimizes total waiting time for all hobs. The resulted multi-objective decision making problem is formulated as mixed integer programming. Simulated annealing is used to solve the resulted model and the performance of the proposed model is compared against two other alternative methods, particle sward optimization and NSGA-II. The results are compared in terms of four criteria including quality metric, mean ideal distance, diversification metric and spacing metric. The results indicate that the proposed model could perform better than the other two alternative methods in terms of quality metric but the results are somehow mix in terms of other three criteria.

An improper machine setting in a hard disk drive assembly process could reduce the read/write area of hard disk drives. This paper presents the methodology to increase the read/write area of hard disk drives by finding an optimal machine setting that minimizes the track zero. The Six Sigma improvement approach was applied. The design of experiment technique helped indicate the optimal levels of significant factors, which were the number of screw turn, the rotating pin height, and the cylinder force, that yield the minimum track zero. The results showed that the mean of track zero was decreased from 16,185 to 15,120 tracks and the standard deviation was decreased from 1,116 to 633 tracks resulting in the increase of the process capability index (Ppk) of the track zero performance from 0.54 to 1.52.

This study examined empirically the impact of cash management on the performance of manufacturing companies in Nigeria-A study of Cadbury Nigeria Plc. The researcher used both secondary and primary data for data collection. For clear analysis, the study centres on two broad variables; the dependent variable which is performance and the independent variable which is Cash management. Two different hypotheses were formulated and tested using descriptive statistics and correlation coefficients techniques respectively in order to establish whether there is a significant relationship between cash management, performance and liquidity. The results of the study suggested that a significant relationship exists between cash management on performance of manufacturing companies in Nigeria. It was also discovered that mere availability of cash (liquidity) without proper management does not necessarily translate into favorable performance for manufacturing companies. Hence, need for effective cash management for better performance.

This paper investigates the instantaneous economic order quantity model by allocating the percentage of units lost due to deterioration in an on-hand inventory by framing promotional effort cost and variable ordering cost. The objective is to maximize the net profit so as to determine the order quantity, promotional effort factor, the cycle length and number of units lost due to deterioration. For any given number of replenishment cycles the existence of a unique optimal replenishment schedule are proved and mathematical model is developed to find some important characteristics for the concavity of the net profit function. Numerical examples are provided to illustrate the results of proposed model, which benefit the retailer and this policy is important, especially for wasting of deteriorating items. Finally, sensitivity analyses of the optimal solution with respect to the major parameters are carried out.

In this paper, we develop an inventory model for deteriorating items with exponential time dependent demand rate. In this proposed model, shortages are allowed and partially backlogged. The model is explained with the help of numerical examples. We also show that total cost function is convex with time. Sensitivity analysis of the optimal solution with respect to various parameters of the system is given and results are discussed in details. Taylor’s series expansion is used for finding closed form optimal solution. Some results have been also obtained.

This paper addresses the application of group Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to Multiple Criteria Decision Making (MCDM), for assisting decision makers by evaluating, ranking and selecting material handling equipment (MHE). The present study considers engineering economy as one of the erudite tool for performance evaluation of said equipment in the integrated and synergetic way. Lastly, incremental analysis is used for final ranking of the equipment under inquisition.

This paper considers a multi period serial production systems for one product and deals with the problem of planned lead-time calculation in a Material Requirement Planning (MRP) environment under probabilistic lead times. It is assumed that lead times for all stages have the same distribution with different parameters. A MRP approach with periodic order quantity (POQ) policy is used for the supply planning of components. The objective is to minimize the sum of fixed ordering, holding and backlogging costs. A mathematical model is suggested and then an optimal planning lead-time, ordering quantity and periodic time are determined.