Supply Chain Management and Inventory Management - Research Paper Example

? Supply Chain Management Literature Review of of Literature Review Inventory management is a very important element of business in our modern era. This is because of the importance of inventory in the business cycle of corporate entities around the globe. Business decisions aim at achieving profitability, liquidity and expansion (Chakraborty, 2003: 1). This implies that the management of every business must make a conscious effort to take decisions and actions that will bring income and support in the growth of the business. Any business that fails to do so is prone to failure and its survival as a going concern is in doubt. To attain the three main objectives of a business, the business needs to attain sufficient revenue and resources in the form of fixed assets and working capital (Muller, 2003: 1). Working capital include the resources used in the trading activities of a business. Working capital is important because it is the resource base that is used to attract more profits through trade. It is through profitability that a business can earn liquid resources to fund its existence. On the other hand, there is the need growth and expansion, which is characterized by the holding up of wealth in fixed assets. This is also a vital part of the growth of an organization because holding more assets enables a business to expand appropriately and remain a stronger going concern. This means that fixed assets are essential and necessary for the expansion and promotion of business. However, locking up too much of your capital in fixed assets leads to over-capitalization and this leads to a situation where there will be limited working capital for trading purposes (Chakraborty, 2003: 2). On the other extreme, holding too much working capital leads to over-trading which stands in the way of growth and survival of the business. There is therefore the need for businesses to draw a balance between the extent of locking up capital in assets and in trading activities. Inventory or stocks is an important element of business because in most production entities, inventory represents over 50% of the cost of production (Bragg, 2004: 1). This therefore means that inventory is a major factor that determines the nature, existence and operations of a business. The elements of inventory costs include cost of purchase, cost of storage, labor to receive, checking quantity, retrieval, selection, packing, shipping and accounting for it (Muller, 20030 p2). These costs sum up to very high amounts that are material in the financial statements of the business. Inventory is vital in the cost structure of businesses. This is because it can be a point for the lock up of immense working capital which can affect trading as well as the capitalization of the the business. Inventory therefore has to be monitored regularly and closely to ensure that it does not become an avenue for the inefficient use of a business' wealth. The inventory must therefore be monitored and managed through various units of an organization like purchasing, production planning, inventory control, receipt and storage and distribution of inventory. (Bose, 2006: 31). This is done by the use of various techniques and skills in managing the purchase and movement of stocks. Bose (2006: 31) argues that some techniques can be used by managers to ensure the efficient use of resources for the monitoring and control of the inflow and dispatch of stocks as a component of supply chain management. These techniques include Just-in-Time (JIT), Economic Order Quantity (EOQ) and Re-order Point/Point of Safety Stock. These techniques help managers to take decisions that help the management of organizations to ensure that the right levels of stocks are kept at any given point in time to ensure that production goes on without the lock up of capital. On the other hand, it enables the management to order for stocks at the right time to ensure that there are no stock-outs which leads to idle time in businesses. Re-order Point The re-order point concept advocates for businesses to hold a level of stocks as safety stocks. When the stock level falls to the safety level, it must be replenished immediately (Toomey, 2003: 78). According to Toomey (2003: 78), Reorder Point can be calculated as: Anticipated Demand during lead time + Safety Stock Level For example, when a business has a forecast unit rate of 100 units/week and a lead time (or waiting time between order and deliver of 4 weeks and a safety stock level of 200, the Re-order point is as follows: 100(4) + 200 = 600 Units. Interpretation: The business needs to ensure that stocks do not fall below 200 units. This will ensure that the issue of stock-outs are kept at prevented in the production process of the business and revenue will always be earned without having idle times. Economic Order Quantity (EOQ) Economic Order Quantity (EOQ) refers to a situation where management demand items in a way “that shows a constant rate and when the entire quantity ordered arrives in bulk at one point in time” (Anderson & Williams, 2008: 448). This means that EOQ is best used when a business requires a constant flow of stocks for production. This will enable the company to order in bulk to save costs. However, EOQ ensures that the orders made are in the right quantity to prevent a situation whereby the business will order too much and have to store all off them and bear all the risk. EOQ therefore gives a mathematical formula that enables businesses to calculate the right quantity of stocks that they need to order at regular intervals to ensure the optimum quantity of stocks and draw the balance between lead times and the lock up of capital in stocks. The formula for the calculation of EOQ is: v(2CD/IV) (Jawadekar, 2007: 139). In this case, the variables can be explained as follows: C is the monetary ordering cost for the order D is the annual demand in units I is the annual inventory carrying cost expressed as a percentage of unit value V is the average monetary value of one unit of inventory An example is that when a business has an average monetary cost of ordering set at $100 and the annual demand is 10,000 units of the product, whilst the carrying cost of each unit is 20% of the total cost and the unitary value stands at $200, the following will be the Economic Order Quantity v[(2(100) (10,000))/(0.2)(200)] which will give a value of v50,000 which is 223.61 Thus the economic order quantity will be approximately 224 units. Interpretation: It will be advisable for the business to to order 224 units of the product at regular intervals. This way, it will have a good balance between the prevention of stock outs and the optimum use of capital. Due to changes in circumstances and situations on the trading calendar, there is the need for adjustments in calculating EOQ from time to time. This is done in the practical sense by using 'what if' analysis. This form of sensitivity analysis can be appointed to identify and analyze the effects of various changes in values in transactions (Jawadekar, 2007: 139). This can be a good tool for the calculation of economic order quantities whenever there are changes in demands or production volumes. This can be done by modifying the basic assumptions of the EOQ model in order to satisfy the immediate needs at hand. Continuous V Periodic Review Policy In order to ensure that the business is keeping a good balance between stocks held for production and the lock up of capital, there is the need for some form of review policy to ensure that the right thing is being done. Continuous review refers to a situation where inventory is reviewed in relation to the production process and decision making on a daily basis (Simcha-Levi et al, 2003: 58). On the other hand, a business might opt to do this review on a a regular basis to ensure that the quanity and time of order for inventories are changed to reflect current situations (Simcha-Levi et al, 2003: 58). Just-in-Time This is the most modern method of managing inventory (Griffin, 2007: 411). It is a concept that originates from Japan whereby inventory are received just when they are needed for the production process (Schermerhorn, 2009:182). In this case, there is no need for storage costs and this leads to a situation whereby the company only has to pay for the cost and delivery of stocks and use them in the production cycle immediately when they are received. In the usage of the JIT model, a manager will have to order in smaller quantity and this will be done more frequently. The challenge with JIT lies in the reliability of the suppliers and/or the delivery system. This is because the slightest delays in the transfer of inventory can lead to idle times. Most manufacturers that use JIT therefore maintain very strong links with suppliers through electronic means and other forms of real-time communication to ensure that suppliers are notified on time when stocks are needed. On the other hand, suppliers also need to use very efficient and reliable transport agencies to ensure that the goods they deliver arrive on time and in good condition. Supply chain management revolves around the relationship between a business and its suppliers and clients. It relates to the acquisition and delivery of inventory and the sale and delivery of finished products to customers. This therefore implies that it entails how inventory will be brought into the organization and how the final product will be sent to the final consumer. Figure 1: The Supply Chain Process From figure on, we notice clearly that businesses normally operate by taking inputs from suppliers, processing them and sell them to consumers. From the diagram, there is an indication that consumers also have some effects on production and this has a corresponding effect on suppliers. In practice, the retrospective effect of consumers on production and the supply chain usually comes about due to seasonality and changes in demand (Wallace & Layton, 2003: 2). In other words, when there are increases or decreases in demand by consumers, there will be the need to expand or reduce production as appropriate. In this sense, the changes must be identified and appropriate adjustments in the production process and cycle must be made as and when this should be done. Due to the fact that managers are human beings with the limitation of not knowing the future, there is the need for managers to use various techniques of forecasting to postulate and anticipate the changes in demand and make appropriate adjustments (Van der Heijden, 2011: 302). Due to the unpredictability of the future, the best these managers can do is to try to evaluate the changes in future demand. And this comes with a high degree of errors and uncertainties which can lead to problems and challenges because of their uncontrollable nature. “Forecasting aim to give the best estimate of future demand and predict changes” (Emmett, 2005: 46). This means that forecasting is the use of appropriate models and concepts to attempt to predict what future demands will be and also identify uncertainties and errors that may come up and make changes as and when necessary. The results of predictions and forecasting will lead to changes in the production capacity. In other words, the business will have to find ways and means of either cutting down or improving the rate of production. This change in production will prompt a chain reaction that will affect the inventory demands. This will mean that the business will have to modify the volume of stocks that they need to request from their suppliers. This therefore shows the importance of demand forecasting in supply chain management. Forecasting is done by using objective and subjective methods (Emmett, 2005: 46). This means that forecasting uses scientific methods like statistical analysis and other mathematical methods for the prediction of demand at different points in the future. This objective method is complemented by subjective methods since these objective methods cannot be used to accurately predict the future. There is therefore the need for the statistical methods to be supported by some non-scientific tools. Thus for example, if a clothing manufacturer is trying to tell the demand for its product in the coming December, it will have to use the facts from historical sales to get a picture of what will happen this December. In this quest, he might be able to scientifically deduce how much people buy in December each year over the previous years. This historical data can give an objective overview of what to expect. However, there might be some uncertainty that this historical data cannot foresee. For example if there is a major economic crisis which will affect the purchasing power of consumers en mass, there will be a difficulty in using the inferences from statistical analysis of the historical data. On the other hand, there could be some positive activities that can affect demand, like a major event like a global sporting activity held in the country the manufacturer operates. This will bring in a lot of tourists and can be planned for. In this sense, the use of statistics might not be the best tool for the prediction of demand since this will not give a good and perfect picture of the activities at hand. There is therefore the need for the business to make estimates based on personal perception and judgments to ensure that an idea of demand is speculated and production is planned to meet it. The use of statistics relies on consistent trends. Consistent trends that have empirical basis leads to systematic component in statistics (Botha, 1981: 355). This means that when data about a particular demand activity is collected over a given period of time, the different values can help a business to establish a given pattern in the way consumers behave and based on this, decisions about production can be taken. Bomer (2009: 34) identifies that there are static and adaptive methods in statistical trend analysis. The static methods include establishing a given trend, estimating seasonal factors and extracting some form of relationship or regression from the data available (Bomer, 2009: 34). On the other hand, adaptive methods involve the establishment of a mathematical framework for analysis of variations (Altekar, 2005: 17). Stated differently, the adaptive methods involve the creation of models for the analysis of various different variables with a degree of accuracy based on relationships established from previous data gathered on the demand at hand. In practical terms, managers use four separate steps to forecast demand (Nadar & Vijayan, 2009: 48). First of all, they identify the objectives of the matter at hand. In most cases, this is to estimate the demand for a given subject matter. Secondly, there is the need to ascertain the determinants of the demand for the product or product group. The next step is to select appropriate methods of demand forecasting using the various statistical tools and methodologies available. The fourth step is to prepare demand forecasting and interpret findings. Preparing demand forecasting and interpreting findings is often dependent on the observation of data collected sequentially over a period of time (Cryer & Chan, 2008: 1). Most managers use the static methods to identify the trends of demand, study them and make inferences as and when necessary for the forecasting of demand. However, for the sake of making deeper inferences, managers need to define a framework within which analysis and postulations can be made in forecasting demand. This is done by the use of exponential smoothing which uses the weighted average of past time series values to create an equation within which variables can be inputed to define demand trends for the future (Shim & Siegel, 1999: 190). Popular examples of the use of exponential smoothing is by the use of the Holt method. Holt's method combines the concept of exponential smoothing with linear trend analysis (Simchi-Levi et al, 2003). Mathematically, the Holt method can be defined as Lt = ?D1 + (1+?) (Lt-1 + Bt-1) Bt = ? (Lt – Lt-1) + (1 - ?) Bt-1 Dt+n = Lt + nbt Lt is the level term of the equation. In other words, it determines the basis of the relationship. In a situation where there is no relationship, the L is the base relationship of the various variables. Bt is the estimate of underlying trend of the series. Once it is identified, it can been put into the equation based on the correlation between the elements of the static trends that were established by observing the statistical data of past demand. Thus ? and ? are variables that define the trend and are required to lie in between 0 and 1. This can be mathematically derived from observations of trends and various analysis of the relationships between several variables. The Holt model is therefore a mathematical tool that gives a basis for the scientific formulation and analysis of different relationships between variables in research (Hydnman, 2008: 51). It gives a framework for the analysis of relationships between variables and can be used in situations where there are no trends (Atanackov & Boylan, 2011: 55 - 57). It is therefore appropriate for the estimation of demand in cases where there was no past sales like the launch of a new product. However the criticism of the Holt model and other advanced models of statistical forecasting of demand like the Winter model is that they are both mathematically inclined and most managers have difficulties using them to calculate trends in demand. This therefore makes it a problematic tool for the forecasting of demand in most businesses. In conclusion, demand forecasting is an important element of supply chain management, since it enables a business to vary its production capacity as and when necessary. This variation can help meet variances in demand and keep a business profitable and competitive. Demand forecasting makes use of both subjective and objective tools. However, due to the inability of human beings to predict the future accurately, there are often variances in forecasts when compared to actual figures. The use of statistical methods and tools help in the improvement of margins. Some statistical trends are static, in that they are based purely on historic data and do not attempt to postulate. Adaptive methods use statistical models to extrapolate and attempt to identify the effects of several variables to identify future demand. 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