A Review on Retail Inventory Management with Purchase Dependency

Transcription

1 A Review on Retail Inventory Management with Purchase Dependency Pradip Kumar Bala 1 1 Xavier Institute of Management, Bhubaneswar, OM&DS Area, Bhubaneswar, INDIA [email protected] Abstract In this paper, a review has been made on retail inventory management with purchase dependency. The article also looks into the application of data mining in retail inventory management with purchase dependency. Index Terms Association Rule, Data Mining, Demand Dependency, Purchase Dependency, Retail Inventory I. INTRODUCTION Demand dependency in retail sale refers to the relationship between periodic demands of two or more items. Periodic demand implies accumulated demand in a day or week or month or so. Demand dependency fails to learn about the individual customer s purchase pattern or consumer behavior. In purchase dependency, attempt is made on capturing the purchase pattern or consumer behavior of the customers through data mining ([1], [2], [3] and [4]). Purchase dependency refers to non-purchase of an item due to non-availability of another item as the customer wants to purchase both of them. With an objective of searching literature on inventory management with purchase dependency, review has also been made on inventory management with demand dependency. In fact, most of the existing literatures are with demand dependency and there is hardly any literature available for purchase dependency. II. RETAIL INVENTORY MANAGEMENT According to [5], the retail industry faces stockout rates of 5-10% which results in sales losses of up to 4% corresponding to hundreds of millions of dollars for large retailers. The most significant cause for stockout situations has been attributed to inefficiencies in in-store logistics due to the lack of inventory visibility. The paper presents a product availability monitoring system which anticipates stockouts before they occur and triggers the personnel to replenish the shelf. According to [6], retailers of products with limited shelf life are faced with the dilemma of stocking the right mix of standard product and its customized stock keeping units, in each product category and the paper models the retailer multi-item inventory problem with demand cannibalization and substitution. Reference [7] has proposed a generic modeling framework to address a number of issues, including stockouts, which continues and extends a recent stream of research aimed at integrating insights from modern inventory theory into the supply chain network design domain. A very important decision during the designing phase of a retail chain is to fulfill the client service target with the total minimum cost as a trade-off between inventory management policies for each shop and delivery policies from the central warehouse. This problem has been analyzed and modeled in [8] and it has been found that in some instances, total costs can be reduced while increasing customer responsiveness. J.C. Penney has become a successful retailer due to its supply chain infrastructure, which includes the teams of people, the quality laboratories, the transportation providers and technologies that work in tandem with internal and external sources worldwide to get its products to the market [9]. In [10], reinforcement learning (RL) techniques have been used to determine dynamic prices in an electronic monopolistic retail market. The market has been considered to consist of two natural segments of customers, captives and shoppers. Under certain logical assumptions about the arrival process of customers, inventory replenishment policy, and replenishment lead time distribution, the system becomes a Markov decision process thus enabling the use of a wide spectrum of learning algorithms. This model and methodology can be used to compute optimal reorder quantity and optimal reorder point for the inventory policy followed by the seller and to compute the optimal volume discounts to be offered to the shoppers. The paper of [11] addresses a periodic-review pricing and inventory control problem for a retailer which faces stochastic price-sensitive demand. Any unsatisfied demand is lost, and any leftover inventory at the end of the finite selling horizon has a salvage value. Shortage cost has been taken into account along with other relevant costs. Both variable and fixed ordering costs have been considered. With an objective to maximize discounted expected profit over the selling horizon by dynamically deciding on the optimal pricing and replenishment policy for each period, it was found that under a mild assumption on the additive demand function, at the beginning of each period, an (s, S) policy is optimal for replenishment, and the value of the optimal price depends on the inventory level after the replenishment decision has been done. Stocking large quantities of inventory may stimulate demand in many retail sales along with the improvement in service levels. For products having demand rates that 84

2 increase with inventory levels, [12] analyzes the effect of stocking decisions on firm profitability to develop managerial insights regarding the structure of the optimal inventory policy, and to understand how this policy differs from traditional approaches. Reference [13] develops a conceptual framework that relates information-integration initiatives to the profitability of manufacturer. The framework allows such initiatives to impact inventory management and revenueenhancing measures that, in turn, increase manufacturer profit margins, or affect profit margins directly. Many large retailers also manufacture many of their products. Hence, such manufacturer-cum-retailers will find this framework useful. Reference [14] develops an optimal replenishment policy for items with inventory-level-dependent demand and fixed lifetime under the LIFO (last-in-first-out) policy. Another example of manufacturer-cum-retailer is MelCo, a large company that manufactures tools and fastening systems and distributes over 3000 products to the construction, mining, and hardware industries in several countries. Reengineering of the stock replenishment system at MelCo has been described in [15]. A central warehouse located in Melbourne supplies a retail network of nearly 30 branch service stores in Australia. Unsatisfactory inventory control was resulting in inaccurate and inappropriate stock levels that inhibited stock distribution and resulted in excess inventory. MelCo developed a system where inventory description, planning, and distribution are directly linked in a network that covers all the branches. Reference [16] makes an attempt to coordinate supply chain when demand is shelf-space dependent. The paper considers a manufacturer or wholesaler who supplies some item to retailers facing demand rates depending on the shelf or display space that is devoted to that product by a retailer and its competitors. The work in [17] develops efficient algorithms to determine optimal pricing and replenishment strategies for integrated supplier and retailer as well as independent supplier and retailer. The research in [18] considers the problem of determining (for a short lifecycle) retail product replenishment order quantities that minimize the cost of lost sales, back orders, and obsolete inventory. The problem has been modeled as a two-stage stochastic dynamic program. A heuristic has been proposed and conditions have been established under which the heuristic finds an optimal solution. In many cases, consumer demand for a specific product depends on price as well as the in-store stock of the product. Reference [19] studies an optimal control problem of pricing and inventory replenishment in a system with sequential inventories when consumer demand depends on the in-store inventory level. Reference [20] develops a decision support system (DSS) for military clothing retailers that determines appropriate inventory levels. These recommended inventory levels varies from week to week because the 85 forecast demand varies. The DSS also determines how much should be ordered when inventories fall below the specified levels. Inventory levels and order quantities are calculated separately for each of the more than 1,000 items typically handled at a retail site. A simulation model has been developed for balanced inventory flow replenishment in clothing ordering and distribution [21]. Initial investigation with the simulation model in [21] has confirmed that a steady flow of orders allows retail sites to maintain low inventories with little risk of stock outage, while the manufacturer enjoys a steady, predictable production load that reduces manufacturing cost. The paper in [22] evaluates the performance of a multiitem joint replenishment inventory model assuming that the customer who cannot be satisfied by the retailer will be lost. In [23], an analytical model for coordinating inventory and transportation decisions in vendor-managed inventory (VMI) systems has been developed. Reference [24] generalizes the inventory-leveldependent demand inventory model to explicitly model the demand rate as a function of the displayed inventory level and then investigates the product assortment and shelf-space allocation problems by extending this model into the multi-item, constrained environment. Reference [25] gives a model for managing multi-item retail inventory systems with demand substitution where customers for retail merchandise are often satisfied with one of several items. The paper in [26] considers the problem of optimizing assortments in a multi-item retail inventory system. In addition to the usual holding and stock-out costs, there is a fixed cost for including any item in the assortment. Assortment planning at a retailer entails both selecting the set of products to be carried and setting inventory levels for each product. Reference [27] studies an assortment planning model in which consumers might accept substitutes when their favorite product is unavailable. It develops an algorithmic process to help retailers compute the best assortment for each store. Reference [28] considers a retail inventory system in which customer orders arrive at random and each order specifies a list of items. Although customers accept later dispatch of an item, there is a cost advantage in having items available for immediate dispatch. Time-weighted holding and shortage costs are incurred for each item. Using a base stock system and with an objective to find the item base stocks that jointly minimize the total cost of the system, the stock of each item is controlled. III. DEMAND DEPENDENCY IN RETAIL INVENTORY MANAGEMENT (A) Demand Dependency in Retail Inventoyr Demand dependency has been taken into account to a limited extent in a few articles. Reference [29] says that demand correlation among different inventory items is common in real-life multi-item inventory systems,

3 although this phenomenon has not received sufficient attention in the existing inventory literature. In this paper, using Markovian model for a two-item inventory system with correlated demands and coordinated replenishments, an expression for the long-run total cost per unit of time under the can-order replenishment policy has been obtained. A multi-item inventory system with dependent item demands represented by a multivariate normal distribution and filled under a First-Come-First-Served rule has been considered in [30]. Through a periodic review order-up-to policy, each item is managed independently and same cycle time is followed for all the items. The work aims at maximizing the joint demand fulfillment probability. Mutual increase in the demand of one commodity due to the presence of another has been accommodated for the first time in the model of [31]. The paper considers the level interdependency between two items (demand of an item depending on the inventory level of that item and inventory level of the other item) and gives a new approach towards a two-item inventory model for deteriorating items with a linear stock-dependent demand rate. In level dependency of two items as explained in the paper, the demand of an item depends in its own inventory level and the inventory level of the other item. The presence of one item reduces the demand for another item. The paper gives a new approach towards a two-item inventory model for deteriorating items with a linear stock dependent demand rate. It has assumed a linear demand rate, that is, demand increases linearly with its own inventory level and decreases linearly with the inventory level of the another item. A steady state optimal solution has been achieved under proper restrictions. There are cases when optimal singular control does not exist. For such cases, sub-optimal steady state solutions have been achieved. All the solutions have been achieved using mathematical analysis. This work has been extended further in [32] which gives the generalized model for any number of items with leveldependency and price-dependency of the items. (B) Classification and Clustering for Multi-Item Inventory Management Using classification and clustering, a group of items are treated in the similar or same way for the purpose of inventory management. Although classification and clustering do not aim at capturing dependency in purchase or demand of the items, some sort of dependency is expected to be captured while grouping in these processes. Grouping of items done with classification or clustering can be useful for replenishing the inventory of the items. A few articles in the literature have been found where items are classified or clustered in groups for the purpose of joint replenishment policy ([33], [34], [35], [36]). Using multi-item classification, generic inventory stock control policies have been derived [33]. Clustering of items has been done in production and inventory systems [34]. 86 Multi-criteria inventory classification has been done by parameter optimization using genetic algorithm [35]. The paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed, such that it produces valid chromosomes given that the parent chromosomes are valid. The new crossover technique is applied to the problem of multi-criteria inventory classification. The results have been compared with the classical inventory classification technique using Analytical Hierarchy Process. Artificial neural networks (ANNs) are used for ABC classification of stock keeping units (SKUs). ANN has been used in a pharmaceutical company [36]. Two learning methods were utilized in the ANNs, namely back propagation (BP) and genetic algorithms (GA). The reliability of the models was tested by comparing their classification ability with two data sets (a hold-out sample and an external data set). Furthermore, the ANN models were compared with the multiple discriminate analysis (MDA) technique. The results showed that both ANN models had higher predictive accuracy than MDA. The results also indicate that there was no significant difference between the two learning methods used to develop the ANN. IV. DATA MINING FOR RETAIL INVENTORY MANAGEMENT Motivated by the works as mentioned in section II where classification and clustering have been applied for inventory management, investigation has been made into the data mining applications in inventory management. Classification and clustering have been used for small volume of data much before data mining techniques came up. For the purpose of classification and clustering of large number of candidates (products or customers) with large profile of each candidate, data mining techniques are used with scalable algorithms. There are many other types of data mining techniques, like, association rule mining, temporal data mining, sequence mining etc. (A) Data Mining Applications in Multi-Item Inventory Management Although considerable progress has been made in the development of algorithms for extracting knowledge from databases, the progress has not been matched by research on decision-making alinternational Journal of Recent Trends in Engineering, Vol 3, No. 2, May 2gorithms, which make use of the knowledge obtained by the machine learning algorithm [37]. According to [38], data mining uses data analysis tools to discover patterns and relationships in data as a basis for predictions. Data mining can be used for anything from asset and inventory management to predictive maintenance to banking and finance.

4 A close look at the different retail organizational functions suggests that Business Intelligence (BI) tools like dinternational Journal of Recent Trends in Engineering, Vol 3, No. 2, May 2ata warehousing, data mining, and OLAP can play a crucial role in almost every function [39]. On the supply side, using BI, retailers will be able to identify their best vendors and determine what separates them from not so good vendors. At the same time, BI can give retailers better understanding of inventory and its movement and also help improve storefront operations through better category management. In the last decade, there has been a rapid development of information systems for effective linkages with the suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance activities. Reference [40] describes the use of traditional statistical techniques to evaluate the best neural network type where a neural network-based prototype model was conceived. The prototype was successful in reducing the total level of inventory by 50% in an organization, while maintaining the same service level to the customers which is given by the probability that a particular customer's demand will be satisfied. Association rules are used as input to many decisionmaking processes. In [41], a method to select inventory items from the association rules has been proposed for cross-selling consideration. This gives a methodology to choose a subset of items which can give the maximum profit with the consideration of cross-selling effect. However, this does not give any policy on inventory replenishment. Reference [42] considers the problem of constructing order batches for distribution centers using a data mining technique and presents a clustering procedure for an order batching problem in a distribution center with a parallelaisle layout. A data mining technique of association rule mining is adopted to develop the order clustering approach. Reference [4] for the first time shows with a case that purchase dependency can be depicted by association rule mined from the sale transaction data. A model for consumer insight mining for retail multiitem inventory management has been developed in [43]. The paper gives the model for retail inventory management in the context of purchase dependency. Reference [3] further extends this model where negative purchase dependency can also be taken care of for managing inventory. CONCLUSIONS From the literature review, it is found that the problem of purchase dependency in inventory management has remained un-recognised and unaddressed. Except the work in [3], [4] and [43] there is no available literature with an objective to extract and use all possible purchase dependencies of various types for managing inventory with a large number of items. Regression analysis can be used to find dependency in periodic 87 (daily/weekly/monthly etc.) demands of the items. But for a very large number of items, regression analysis is not practical as it is required to analyze for all possible combinations of the items. Moreover, regression analysis does not capture information at the level of sale transaction of a customer and hence, it does not provide any knowledge of purchase dependency as a consumer insight. Although lost sale cost has been considered for designing inventory in some of the existing literatures as given in the survey, lost sale cost arising out of purchase dependency has not been addressed in any available literature for designing multi-item inventory replenishment policy. 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