Ordering Policy and Inventory Classification Using Temporal Association Rule Mining

2018 ◽  
Vol 6 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Order Quantity ◽  
Rule Mining ◽  
Ordering Policy ◽  
Temporal Association Rule ◽  
Optimum Order ◽  
Frequent Items

A modified framework that applies temporal association rule mining to inventory management is proposed in this article. The ordering policy of frequent items is determined and inventory is classified based on loss rule. This helps inventory managers to determine optimum order quantity of frequent items together with the most profitable item in each time-span. An example is illustrated to validate the results.

Ordering Policy Using Multi-Level Association Rule Mining

2018 ◽  
Vol 11 (4) ◽  
pp. 84-101 ◽  
Author(s):  
Reshu Agarwal ◽  
Sarla Pareek ◽  
Biswajit Sarkar ◽  
Mandeep Mittal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Order Quantity ◽  
Rule Mining ◽  
Ordering Policy ◽  
Optimum Order ◽  
Frequent Items ◽  
Multi Level ◽  
Real Scenario ◽  
Frequent Item Sets

In this article, an inventory model for a retailer's ordering policy is studied. Multi-level association rule mining is used to find frequent item-sets at each level by applying different threshold at different levels. During order quantity estimation, category, content, and brand of the items are considered, which leads to the discovery of more specific and concrete knowledge of the required order quantity. At each level, optimum order quantity of frequent items is determined. This assists inventory manager to order optimal quantity of items as per the actual requirement of the item with respect to their category, content and brand. An example is devised to explain the new approach. Further, to understand the effect of above approach in the real scenario, experiments are conducted on the exiting dataset.

Ordering policy using temporal association rule mining

2015 ◽  
Vol 1 (2) ◽  
pp. 157 ◽  
Author(s):  
Mandeep Mittal ◽  
Sarla Pareek ◽  
Reshu Agarwal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Rule Mining ◽  
Ordering Policy ◽  
Temporal Association Rule

Modified Ranking With Temporal Association Rule Mining in Supply Chains

2020 ◽  
Vol 11 (4) ◽  
pp. 58-71
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Rule Mining ◽  
Individual Item ◽  
Data Set ◽  
Abc Classification ◽  
Temporal Association Rule ◽  
Time Periods

This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

Loss Profit Estimation Using Temporal Association Rule Mining

2016 ◽  
Vol 3 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Timing Constraints ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Temporal Association Rule ◽  
Frequent Items

Temporal association rule mining is a data mining technique in which relationships between items which satisfy certain timing constraints can be discovered. This paper presents the concept of temporal association rules in order to solve the problem of classification of inventories by including time expressions into association rules. Firstly, loss profit of frequent items is calculated by using temporal association rule mining algorithm. Then, the frequent items in particular time-periods are ranked according to descending order of loss profits. The manager can easily recognize most profitable items with the help of ranking found in the paper. An example is illustrated to validate the results.

Optimal Order Quantity and Inventory Classification Using Clustering

2017 ◽  
Vol 4 (2) ◽  
pp. 41-52 ◽  
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Optimal Order ◽  
Order Quantity ◽  
Ordering Policy ◽  
Optimum Order ◽  
Frequent Items ◽  
Clustering Approach ◽  
Optimal Order Quantity ◽  
Data Objects ◽  
Research Studies

Clustering is the process of analyzing data to find clusters of data objects that are similar in some sense to one another. Some research studies have also extended the usage of clustering concept in inventory management. Yet, not many research studies have considered the application of clustering approach on determining both optimal order quantity and loss profit of frequent items. In this paper, ordering policy of frequent items in each cluster is determined and inventory is classified based on loss rule in each cluster. This helps inventory manager to determine optimum order quantity of frequent items together with the most profitable item in each cluster for optimal inventory control. An example is illustrated to validate the results.

Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery

2019 ◽  
Vol 18 (03) ◽  
pp. 1950028
Author(s):  
Sheel Shalini ◽  
Kanhaiya Lal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Time Window ◽  
Search Space ◽  
Temporal Association ◽  
Incremental Mining ◽  
Rule Mining ◽  
Novel Approach ◽  
Temporal Association Rule ◽  
Mining Algorithms

Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.

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