Posted in Operations & IT Articles, Total Reads: 2499
, Published on 30 November 2011
Fierce competition in today’s global markets, introduction of products with short life cycles and heightened expectations of customers have forced manufacturing organizations to invest in and focus attention on their logistics systems (Julien Bramel, David Simchi-Levi 1997). To be highly responsive to fast changing customer demand, supply chains of these organizations include multiple distribution centers supplying to retail outlets. Due to its proximity to customers, such a supply chain network quickly responds to changing customer demands, market conditions and regulatory norms. At the same time, though, the costs in supply chain increase due to increased inventory and logistics costs. Existence of multiple transport modes, multiple transporters, different lead times and costs of each transporter and the quantity discounts make the transport decisions highly complex and non-intuitive. High shipment quantities, price and lead time culminate in high cost of storage and in-transit inventory. Fast changing technology, shortened product lifecycles and high perishability make products quickly obsolete and hence, obsolescence costs are important. VAT (value added tax) induced differential taxation impacts overall supply chain value and hence it is a critical consideration factor while making logistics decisions. Varying degree of local market development, seasonality and discounts cause spatial and temporal variation in demand and price. Due to all the above factors, managing inventory and logistics is a complex problem to be tackled by the central decision maker in today’s organizations.
This article proposes a practical solution to this problem which involves increased co-ordination in the logistics network through redistribution of inventories. Inventory redistribution involves transporting inventories among distribution centers so as to maximize overall supply chain value. It reduces inventory carrying and obsolescence cost and simultaneously increases revenue at the cost of extra transportation. The sheer complexity of decisions and quantitative nature makes this problem amenable to application of operations research (OR) techniques. This article shows a generic approach to model this problem as a linear programming (LP) problem with an application to the supply chain of India’s largest supplier of computer hardware and electronic gadgets.
Logistics Management: Inventory Redistribution Model
The tactical plans periodically prepared to replenish inventories at major distribution centers to fulfill demands over planning horizon are called Distribution Logistics plans. The distribution logistics plans with flows among DCs are called Inventory Redistribution plans. As an illustration, consider a national level supplier organization having distribution centers (DCs) located across country. These DCs supply to customers in different part of country. Each of these DCs has an initial stock of inventory. Depending on demand forecast for planning horizon, each location can have either excess or deficit of stock over demand. Inventory redistribution plan is about sending right quantity of a right SKU from a right supply location to a right demand location at a right time. Inventory redistribution enables circulation of inventory among DCs. It also allows for transport of inventory from slow inventory depleting DCs to fast inventory depleting DCs. This enables increased and quicker selling of inventory and not only yields additional revenue but also reduces overall inventory carrying and obsolescence cost at the expense of marginal transportation cost. Inventory redistribution plan gives the optimal mode of transport between each pair of DCs. As opposed to a priori categorization of DCs as supply and demand DCs in the Days Inventory Norm method, inventory redistribution planning automatically decides these choices and thus yields optimal solution.
In this section, we describe a LP model to determine optimal decisions in inventory redistribution plan. The essential terminology regarding the model is presented below:
The physical situation depicted by the above LP is shown in the diagram below. It captures the flow of goods in a typical inventory redistribution case. Black lines indicate initial supply stock at each DC. Green lines indicate the quantity of good shipped. These are reaching destination DCs in different time periods due to lead time of transportation. Yellow lines indicate the inventory flows. Red lines indicate the quantity of goods sold. We can conceptualize this diagram as a Network Flow structure having multiple supply sources and multiple demand sinks.
Figure 1: Flow of Goods in Inventory Redistribution
The inventory redistribution strategy proposed in this article was applied at the largest producer and supplier of computer hardware and electronics gadgets in India (call it “Company A” for the purpose of anonymity). Company A has 28 DCs located across India to cater to customer demands. Given the dynamically changing nature of the industry, the organization was facing problem of increased inventory, obsolescence and lost sales costs at DCs. Since the SKUs were highly priced, the proportion of inventory carrying and obsolescence costs in total logistics costs was very high (about 60%-70%). Even so, the logistics decisions in client organization were carried out on ad-hoc basis, without proper planning methodology. Since they were done manually, they required a long decision time and were also suboptimal (i.e. having higher cost). Faced with this situation, the Company A needed to answer many questions: Firstly, it wanted to know how to transport goods across DCs, which DCs should serve as supplier and which should serve as demand location. Secondly, it wanted to know what mode of transport should be chosen (i.e. air or surface freight). Once the mode is decided, the organization wanted to decide which particular transporter should be picked from among many offering competitive prices. Further, the organization wanted to incorporate a transport strategy to maximize revenue using differential pricing across states due to different margins and tax rates.
The inventory redistribution strategy discussed in this article was formulated as a MILP and applied to the supply chain of Company A. The problem involved about a few thousand variables and constraints. A MS Access database was built based on the data collected from the company. Finally, MS Excel based model was built using OpenSolver (a free Excel add-in) as an engine to solve MILP.
The model helped the organization reduce its annual logistics cost by 8%-10%. The inventory redistribution model enabled Company A to obtain optimal decision report regarding following important decision variables:
Supply and demand depots
Optimal shipment quantity from supply depot to demand depot
Optimal mode of freight
It provided the optimal (highest profit) solution and hence maximized Company A’s supply chain value as compared to existing one. Also, the MS Access and MS Excel base decision support system enabled quick and accurate decisions, considerably reducing decision making time. As a result of its spectacular performance, this model was accepted by Company A for actual implementation in logistics planning and decision making purposes.
In this article, we have proposed a concept of inventory redistribution planning as a practical way to maximize supply chain profitability of an organization. We have seen that this situation can be quite realistically modeled as a LP and can also be solved quite efficiently using well known software such as MS Excel. The model proposed in this paper is quite general and can be employed as a decision making tool by the managers of vast majority of supply chains ranging from FMCG to highly perishable and pricy products. In the conclusion, we can say that inventory redistribution is a prudent and highly beneficial approach to logistics management.
Gerard Cornuejols, Reha Tutuncu, “Optimization Methods in Finance”, Cambridge University Press, 2007, pp. 255-263
Jeremy F. Shapiro, “Modeling the Supply Chain”, Second Edition, Cengage Learning, 2007
Julien Bramel, David Simchi-Levi, “The Logic of Logistics: Theory, Algorithms, and Applications for Logistics Management”, Springer Series in Operations Research, Springer-Verlag New York, 1997
 In this method, gross requirements for planning horizon are calculated at each DC based on predefined days inventory norms.
This article has been authored by Ashwin D. Zade from from IIM, Lucknow. These are the personal views of the author
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