Learning curves monitor the performance of workers for the given new task as well as it is a mathematical representation of the same learning process which can be analyzed after frequent repetitions. Now-a-days learning curve is a promotion effective tool for management concern with designing and controlling the process of imperfect production and redesigning unbalanced business operations in the production of goods or services related to scheduling, uncontrolled inventory management, quality management as well as inspection. Learning effect has direct impact in calculation of profit or loss. Generally, a business seller, in order to increase his sale prefers to lend his products to buyers for a definite period of time. There is no penalty before or during this definite time period however after the duration of lending time period is over, he will assign some extra charges. For this action, seller offers a trade credit financing period to his buyer. Assuming when buyer receives a lot he separates the defective and non-defective items by a screening process and defective items are then sold at a discounted price. The percentage of defective items decreases per lot according to learning curve. Seller too plans which condition is beneficial for good coordination of retailers and analysts. Different cases are explained broadly in this model to get maximum profit. In this paper, a fiscal construction feature model for imperfect quality items with trade credit policy is analyzed under the effects of learning. Total profit function per cycle has been derived with the help of involvement of different costs and related parameters for the retailers and a numerical example given ahead shows the verification of results. The impacts of key parameters of the model are studied by sensitivity analysts to deduce managerial insights.

Most of the supply chain models have been developed under symmetric information structure i.e. players have complete knowledge of each other’s policies. But in most of the cases, players do not have complete information about the other players i.e. some information regarding their businesses is hidden. This paper studies supply chain model of imperfect quality items under asymmetric information in which unit price taken by the buyer and unit marketing expenditure are influencing product’s demand. This information is hidden to seller. The seller delivers the supply to the buyer. Each delivered lot goes through an inspection process at the buyer’s side. In the inspection process, the items are divided into two categories. The first category is perfect quality items while the second category is of imperfect quality items. After the inspecting the lots, the perfect quality commodities are to be sold at selling price and the imperfect items are supposed to get sold at a discounted price. The interaction between two constituents of the supply chain is modeled by non-cooperative Seller Stackelberg game approach in which the role of leader is played by the seller while the role of follower is played by the buyer. In our proposed model, the seller does not have information related to market demand but the buyer does. Numerical examples and sensitivity analysis explain the significance of the theory.

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.

The outset of new technologies, systems and applications in manufacturing sector has no doubt lighten up our workload, yet the chance causes of variation in production system cannot be eliminated completely. Every produced/ordered lot may have some fraction of defectives which may vary from process to process. In addition the situation is more susceptible when the items are deteriorating in nature. However, the defective items can be secluded from the good quality lot through a careful inspection process. Thus, a screening process is obligatory in today’s technology driven industry which has the customer satisfaction as its only motto. Moreover, in order to survive in the current global markets, credit financing has been proven a very influential promotional tool to attract new customers and a good inducement policy for the retailers. Keeping this scenario in mind, the present paper investigates an inventory model for a retailer dealing with imperfect quality deteriorating items under permissible delay in payments. Shortages are allowed and fully backlogged. This model jointly optimizes the order quantity and shortages by maximizing the expected total profit. A mathematical model is developed to depict this scenario. Results have been validated with the help of numerical example. Comprehensive sensitivity analysis has also been presented.

Timely identification of newly emerging trends is needed in business process. Data mining techniques like clustering, association rule mining, classification, etc. are very important for business support and decision making. This paper presents a method for redesigning the ordering policy by including cross-selling effect. Initially, association rules are mined on the transactional database and EOQ is estimated with revenue earned. Then, transactions are clustered to obtain homogeneous clusters and association rules are mined in each cluster to estimate EOQ with revenue earned for each cluster. Further, this paper compares ordering policy for imperfect quality items which is developed by applying rules derived from apriori algorithm viz. a) without clustering the transactions, and b) after clustering the transactions. A numerical example is illustrated to validate the results.

In the classical inventory models, most of the time the issue of quality has not been considered.
However, in realistic environment, it can be observed that there may be some defective items in
an ordered lot, because of these defective items retailer incurs additional cost due to rejection,
repair and refund etc. Thus, inspection/screening of lot becomes indispensible in most of the
organizations. Moreover, it plays a very essential role when items are of deteriorating in nature.
Further, it is generally assumed that payment will be made to the supplier for the goods
immediately after receiving the consignment. Whereas, in practice, supplier does offers a
certain fixed period to the retailer for settling the account. During this period, supplier charges
no interest, but beyond this period interest is being charged as has been agreed upon. On the
other hand, retailer can earn interest on the revenue generated during this period. Keeping this
scenario in mind, an attempt has been made to formulate an inventory model for deteriorating
items with imperfect quality under permissible delay in payments. Results have been validated
with the help of a numerical example using Matlab7.0.1. Comprehensive sensitivity analysis
has also been presented.