How the Trade-off between Storage Density and Accessibility Can Be Resolved - Assignment Example

1) Critically discuss how the trade-off between storage density and accessibility can be resolved within Regional Distribution Centres (RDCs). The trade-off between storage density and accessibility has to be attained within Regional Distribution Centres for getting optimum level of performance through the supply chain in various businesses. Management of stock requires a balanced approach, by managing goods inventory with routine needs, and equilibrating the urgency to minimise the cost of stock-keeping with the transportation availability. The trade-off can result in keeping sufficient stock but not at the cost of facing cash crunch. It is observed that storage cost increases when transportation is available but on the other hand, no cost cutting is experienced when the stock density is good, given the better accessibility through transportation of goods (Skipper, 2008). Holmes (2011) explains the trade-off between storage density and immediate accessibility of products throughout the Wal-Mart’s network. Wal-Mart’s strategy has been to leverage through the dense chain of its stores. The stores are vertically involved into distribution. Analysis of its distribution and warehouse network reveals that the general goods are supplied by Wal-Mart’s own regional distribution centres (RDCs) while the groceries for supercentres are delivered via its own food distribution centres. When stores are densely stocked, it is not hard to start a distribution chain that keeps stores near to a distribution centre and when the Wal-Mart stores are near to a distribution centre, it can spare the transporting costs. Other than that, such closeness permits Wal-Mart to react fast to demand jolts. Fast reaction is hugely known to be a leading trait of the Wal-Mart model. It needs to be appreciated that Wal-Mart restocked its shelves with American flags on the very day of 9/11. While stating the benefits of storage density of Wal-Mart, the job is tough as the Company does not reveal inside information out for information purpose. A direct analysis of Wal-Mart’s data pertaining to logistics cost to density is, therefore, not easy. Even if the Company readily reveals the information over the supply chain network, the leverage it is getting by reacting fast to demand jolts can not be approximated with the available accounting figures. Wal-Mart’s revealed priority can only be ascertained indirectly. Density allows leverage but it comes at a price. A testing of Wal-Mart’s attitude of unseen trade-off against the seen cost can help in getting the clue over how it succeeds in getting the leverage from the trade-off (Holmes, 2011). In the case of Wal-Mart, the high storage density is created when stores are in close proximity and their market fields converge. It offers the opportunity to new stores to benefit from the convergence, increasing their sales from established stores. The limit of such leverage can be measured by sourcing relevant data from different providers such as getting store level sales output from ACNielsen and regional data from the U.S. Census at a quite zeroed-in degree of geographical location. This information can be used to approximate a model of demand wherein consumers select among all the Wal-Mart stores in the general region where they reside. The demand model suits the data finely. Further, inherent meanings can be explained for the limit of leveraging from the store density with specific revelations made by Wal-Mart in its yearly reports. Analysis of the sales model helps in finding how Wal-Mart faces crucial diminishing returns in sales from its storage density because of the nearness of the stores to the adjoining area (Holmes, 2011). Wal-Mart worked on a changing structure for its stores across the time ranging from 1962-2005. The structural model of Wal-Mart developed by Holmes (2011) is quite huge. It can find out the correct position of each single store and the position of each distribution centre, the kind of store (routine Wal-Mart or supercentre), and the type of distribution centre (routine merchandise or food). The model considers wage and land cost variations over locations. The model discusses that although high store density could provide leverage to Wal-Mart yet there could be losses due to high population density—more than from high wages and land cost—as the Wal-Mart model might not function rightly in totally urban environment. Considering the huge computations of various viable combinations of store opening sequences, it is not easy to solve Wal-Mart’s logistics optimisation. Holmes (2011) searches possibilities, ruling out deviation from the selected policy of attaining optimisation by taking a perturbation approach. When the selection set is continuous, a perturbation approach normally means equality limitations (i.e., first-order situations). In the case of perturbation approach, a known selection offers inequality restraints. As per Holmes (2011) approximation, if a Wal-Mart store is nearer by 1 mile to a distribution centre, across a year’s time, Wal-Mart gets the cost leverage near to $3,500. This approximation goes hugely beyond possible savings in trucking costs alone. Considering the magnitude of miles involved in Wal-Mart’s functions and its huge number of stores, the approximation means that economies of density comprise a great part of Wal-Mart profitability. It infers that Wal-Mart can trade-off between storage density and accessibility by opening more stores in the same regions it has been serving as against enlarging its location base and maintaining store density the similar (Holmes, 2011). 2) Prepare a review of the key performance indicators (KPIs) required to effectively run distribution centres. Key Performance Indicators Certain indicators need to be developed for effective running of the distribution centres, irrespective of the business although some KPIs would not be required at all in certain business. Let’s consider the opinion of the fleet-management consultant, Roger Thompson, who has been vice president, management, fleet and facilities with Bucher, Willis & Ratliff Corp. Roger Thompson has zeroed-in some indicators that speak about the efficiency in managing stock. Irrespective of the industry segment, Roger Thompson has built some commonalities that can be used to fleets over the board, taking from various elements. The first indicator to be observed is the largest share of the inventory on hand. Stock-carrying rate is another indicator. Another one of the critical indicators of a well-controlled stock shifting function is the stock turn rate (Skipper, 2008). It shows that fixing of indicators is not an easy task. It is just like aiming at a dwindling object (Skipper, 2008). Adjustment between storage and possibility of availability of a component depends on the dollar value of the components released from the stock, which should be divided by the dollar value of all the components released in the previous year. Generally, this should comprise 50 to 60% so that the next time the stocked part is required to be accessed, there is at the minimum possibility of 50 to 60% that the component would be available in the stock (Skipper, 2008). Stock Movement Rate Another indicator is the stock-movement rate, which can be derived by dividing the number of stock lines with no shifting for the previous 12 months with the number of stock lines. Performance is good if it is less than 5%. Inventory Turn Rate Inventory turn rate is also one of the leading indicators, which can be derived by including the worth of all components released from stock to be divided by the dollar amount of average yearly stock (Skipper, 2008). Inventory Accuracy Accurate record of the stock means there is no mismatch between stated quantity and actual quantity on the shelf. Inaccurate stock presence wastes time. The sales person’s time is wasted in unproductive job of checking the item on the shelf. It also results in loss of money in arranging new item for the same. Customers also get disappointed when the item is not delivered in the given time. It is also a loss to the company’s image. Therefore, accurate reporting of the stock is a critical KPI. The more correct the stock record, the more efficient is the stock keeping unit and the distribution centre. By using bar coding and wireless technologies, increased accuracy and efficiency can be brought about in the distribution centres (Zaw, Zubair & Marilyn, 2009). Space Utilisation Only an efficient storage system can help in using space efficiently. Space usage is a means of occupying and using space at the desired rate for attaining optimised use of space. It helps the Distribution Centres (DCs) to perform more efficiently. The increased the space usage rate, the more efficient is the DC. It creates the need to install dependable storage systems for efficient and effective space usage. For instance, it is ideal to install a vertical carousel system for small medical goods. By installing the right storage system, efficiency in space usage can be achieved. It is more critical keeping in mind the rising land prices to use the given area efficiently by making optimum usage of the overall land mass (Zaw, Zubair & Marilyn, 2009). Taking the example of machine usage through forklift and delivery truck, the increased usage rate on machine will minimise the unit cost, thus, benefitting the company. If there is labour problem, utilisation of machine will reduce considerably. Utilisation cost may increase if additional labour help is sought to complete the job as regular staff is not reporting for work. It can be risky to the image of the company for not packing and delivering ordered items on time. As such the role played by machines and labour is very critical in the effective functioning of the DCs (Zaw, Zubair & Marilyn, 2009). Process Cost Another KPI is the process cost, which is the cost of distributional processes. If the process cost is relatively higher to other DCs, it means the functioning of that DC is not at the desired performance level. It will cause inefficiencies. Therefore, the process cost of a DC is quite significant in raising the performance level through effective management (Zaw, Zubair & Marilyn, 2009). Quality and Service Just effective delivery of goods from the DCs to the customer is not sufficient. Delivery of goods needs to be satisfactory to the customer demand. It is an important indicator from performance perspective. Customer needs should be fulfilled by providing quality service by the DCs. A warehouse and DC, therefore, should attend to both quality and service aspects to ensure its effective management (Zaw, Zubair & Marilyn, 2009). Throughput Level Throughput level will approximate the performance of a DC. If a distribution centre has high throughput parameter, it means that it is an efficient and income-generating DC (Zaw, Zubair & Marilyn, 2009). Benefits and Limitations of the KPIs Uses 1) The Key Performance Indicators provide a roadmap for warehouses and distributions centres to approximate their efficiencies. The higher the performance level of a distribution centre to adhere to the prescribed path of a KPI, the more efficient and generating is the distribution centre. 2) The Key Performance Indicators boost the morale of employees working in the DCs to attain the company goals. Morale of the employees gets heightened because they are provided with straight instructions as to what will help better the management of the DC. The staff remains focussed to achieve the set targets (Zaw, Zubair & Marilyn, 2009). Limitations 1) Any approximation of the efficiency of a KPI may be inaccurate, as it is not easy to approximate the effectiveness of a KPI correctly. Therefore, the key performance approximations of some warehouses and distribution centres may be difficult to assess. 2) Making assessment of a KPI is time-taking process, as an additional attempt has to be made to assess the efficiency of a warehouse or distribution centre in the context of the KPI. It requires additional human resource power and scholarship to perform appraisal of a KPI. This may result in additional costs to the company. 3) Also, KPIs vary from one DC to another, as various companies would focus on distinct goals. Thus, it is not always fair to relate different companies on the same KPIs (Zaw, Zubair & Marilyn, 2009). References Holmes, T. J., 2011. The diffusion of Wal-Mart and economies of density. Econometrica, 79 (1), 253–302. Available from: http://www.econ.umn.edu/~holmes/papers/ecta7699.pdf [accessed 23 August 2012]. Skipper, G.C., 2011. How to keep the right parts on hand. Construction Equipment, 111(8) 8. Available from: http://proquest.com [accessed 23 August 2012]. Zaw, Zubair and Marilyn., 18 January 2009. Key performance indicators for managing warehouse and distribution centre. Dcm-e blogspot. Available from: http://dcm-e.blogspot.in/2009/01/key-performance-indicators-for-managing.html [accessed 23 August 2012]. Read More