Quantity discount is a usual term in business and has been a topic of interest for a long time, but have received very little attention. Many vendors offer different schemes to their customers to increase the existing size of order, which results in higher annual sale for the vendor and a lower purchasing price for the retailer. Therefore, the buyer adjusts his/her selling price, which influences the demand for the product. The objective of this paper is to develop an inventory model for deteriorating products with quantity discount and partial backlogging to determine the optimal ordering quantity for the retailer optimizing the total cost or profit of the associated model. A numerical example is also given to illustrate the model and its significant features.

Cash plays essential role in modern business and economy and it is the life blood of all businesses. Therefore, cash management is the art of managing a company’s short-term resources for its ongoing activities, mobilizing funds and optimizing liquidity. Inefficient cash management may lead the company to bankruptcy. In this paper, we highlight different perspectives in which we can control the corporate cash including Cash Conversion Cycle, Cash Holding and Creditworthiness. The term Cash Conversion Cycle can be considered as a length of time between purchase of raw-materials and collection of cash from debtors. Cash holding is one of the most important financial decisions that the manager of the concerned organization has to make for the organizations. In this paper, we try to interlink among different items to control cash so that bankruptcy could be prevented and profitability would be improved by investigating on five companies from Consumer Durable sector.

Aluminium alloy AlMg4.5Mn has got comprehensive acceptance in the manufacturing of light weight frames, marine structures which require high strength and worthy corrosion revulsion. The present investigation work focuses on friction stir welding process in which influences of operating parameters have been studied on friction stir welded butt joints. The FSWed joints specimens have been produced by experimentation at three levels of tool traverse speed and tool rotational speed keeping input force and tool tilt angle constant. Mechanical properties and microstructure of welded joints have been investigated in the present study. Change in the microstructure at different zones which transforms the mechanical properties of welded joints was due to the asymmetrical flow of material and thermal cycles around the pin. The second phase beta grains are formed as the very high temperature reached due to input parameters combinations. Traverse speed (TS) and tool rotational speed (TRS) are taken in a range of 16-40 mm/min and 500-1400 rpm, respectively. The best combination of parameters results in higher tensile strength which is well supported by the micro-hardness curve and the compact grains microstructure profile. Microstructure at different points and physical properties exhibited by the welded joint are well brought into line to summarize the effects of different parameters.

Data mining is the technique to find hidden patterns from a very large volume of historical data. Association rule is a type of data mining that correlates one set of items or events with another set of items or events. Another data mining strategy is clustering technique. This technique is used to create partitions so that all members of each set are similar according to a specified set of metrics. Both the association rule mining and clustering helps in more effective individual and group decision making for optimal inventory control. Owing to the above facts, association rules are mined from each cluster to find frequent items and then loss profit is calculated for each frequent item. Initially, the clustering algorithm is used to partition the transactional database into different clusters. Apriori, a classic data mining algorithm is utilized for mining association rules from each cluster to find frequent items. Later the loss profit is calculated for each frequent item. The obtained loss profit is used to rank frequent items in each cluster. Thus, the ranking of frequent items in each cluster using the proposed approach greatly facilitate optimal inventory control. An example is illustrated to validate the results.

There is a tremendous growth of the use of the component based software engineering (CBSE) approach for the development of software systems. The selection of the best suited COTS components which fulfils the necessary requirement for the development of software(s) has become a major challenge for the software developers. The complexity of the optimal selection problem increases with an increase in alternative potential COTS components and the corresponding selection criteria. In this research paper, the problem of ranking and selection of Data Base Management Systems (DBMS) components is modeled as a multi-criteria decision making problem. A ‘Fuzzy Distance Based Approach (FDBA)’ method is proposed for the optimal ranking and selection of DBMS COTS components of an e-payment system based on 14 selection criteria grouped under three major categories i.e. ‘Vendor Capabilities’, ‘Business Issues’ and ‘Cost’. The results of this method are compared with other Analytical Hierarchy Process (AHP) which is termed as a typical multi-criteria decision making approach. The proposed methodology is explained with an illustrated example.

In this paper, a period review inventory model with controllable lead time has been considered where shortages are partially backlogged. The backorder rate is dependent on the backorder discount and the length of the protection interval, which is sum of the review period and the lead time. Two cases have been discussed for protection interval demand which are (a) Demand distribution is known (Normal Distribution) (b) Demand distribution is unknown (Minimax distribution). Further, algorithms have been developed which jointly optimize the backorder discount, the review period and the lead time for each case. Numerical examples are also presented to illustrate the results.

The evaluation and selection of energy technologies involve a large number of attributes whose selection and weighting is decided in accordance with the social, environmental, technical and economic framework. In the present work an integrated multiple attribute decision making methodology is developed by combining graph theory and analytic hierarchy process methods to deal with the evaluation and selection of energy technologies. The energy technology selection attributes digraph enables a quick visual appraisal of the energy technology selection attributes and their interrelationships. The preference index provides a total objective score for comparison of energy technologies alternatives. Application of matrix permanent offers a better appreciation of the considered attributes and helps to analyze the different alternatives from combinatorial viewpoint. The AHP is used to assign relative weights to the attributes. Four examples of evaluation and selection of energy technologies are considered in order to demonstrate and validate the proposed method.

Six Sigma is a strategic approach of significant value in achieving overall excellence. It helps to accomplish the organizations strategic aim through the effectual use of project controlled methodology. As Six Sigma is a project controlled approach, it is necessary to prioritize projects which give utmost economic benefits to the firm. In real practice, Six Sigma projects selection is very tough assignment because poor project selection also happens even in the well-managed organizations and this can weaken the success and trustworthiness of the Six Sigma practice. The present study aims to develop a project selection approach based on a combination of fuzzy and MADM technique to help organizations determine proper Six Sigma projects and identify the priority of these projects mainly in automotive companies. VIKOR and TOPSIS methods have been used to select the proper Six Sigma project composed with fuzzy logic. In this context, seven critical parameters have been considered for selection of finest alternative. The weights of evaluation criteria are obtained using the MDL (modified digital logic) method and final ranking is calculated through primacy index obtained by using fuzzy based VIKOR and TOPSIS methodology. A factual case study from automotive industry is used to investigate the efficacy of the planned approach.

The aim of this study is to correlate work piece material hardness with surface roughness in prediction studies. The proposed model is for prediction of surface roughness of tool steel materials of hardness 55 HRC to 62 HRC (±2 HRC). The machining experiments are performed under various cutting conditions using work piece of different hardness. The surface roughness of these specimens is measured. The result showed that the influence of work piece material hardness on surface finish is significant for cutting speed and feed in CNC end milling operation. It is also observed that the surface roughness prediction accuracy of Adaptive neuro fuzzy inference system using triangular membership function is better than Gaussian, bell shape membership function and regression analysis. Surface roughness prediction accuracy with material hardness as input parameter is 97.61%.

The objective of this paper is to optimize the process parameters by combined approach of VIKOR and Entropy weight measurement method during Electrical discharge machining (EDM) process of Al-18wt.%SiCp metal matrix composite (MMC). The central composite design (CCD) method is considered to evaluate the effect of three process parameters; namely pulse on time (Ton), peak current (Ip) and flushing pressure (Fp) on the responses like material removal rate (MRR), tool wear rate (TWR), Radial over cut (ROC) and surface roughness (Ra). The Entropy weight measurement method evaluates the individual weights of each response and, using VIKOR method, the multi-objective responses are optimized to get a single numerical index known as VIKOR Index. Then the Analysis of Variance (ANOVA) technique is used to determine the significance of the process parameters on the VIKOR Index. Finally, the result of the VIKOR Indexed is validated by conformation test using the liner mathematical model equation develop by responses surface methodology to identify the effectiveness of the proposed method.