Impact of Proper Inventory Control System of Electric Equipment in RUET: A Case Study

Transcription

1 Impact of Proper Inventory ontrol System of Electric Equipment in RUET: A ase Study Khairun Nahar*, Khan Md. Ariful Haque**, Mahbub A Jannat*, Sonia Akhter*, Umme Khatune Jannat* and Md. Mosharraf Hossain This paper is directed to develop effective inventory control strategy for electrical equipments in RUET. An efficient and effective inventory control strategy is essential to obtain maximum output from any production or service oriented organization. Store was stratified by using monetary value of consumption (AB analysis) and optimization was focused on material with highest consumption amount (A category material). Using forecasted demand which was derived by weighted moving average method and optimizing between ordering, carrying and material cost economic order quantity (EOQ) for joint replenishment was determined assuming demands, lead times, costs and inventory carrying percentage for all items are given and deterministic. ost of traditional order quantity and EOQ was compared, showing a significant amount of saving of 234,384 in later technique. This saving would release tied up capital which can be used as resource in other operations. Keywords: Inventory ontrol, AB Analysis, Forecast, EOQ, Store Management. 1. Introduction Inventory is essential to provide flexibility in operating a system. Hence inventory control is the technique of maintaining the size of the inventory at some desired level keeping in view the best economic interest of an organization. Many businesses have too much of their limited resource, capital tied up in their major asset, inventory, where some of them could be damaged, obsolete, or wrong purchased raw material/ finished goods or equipment s. To control desired inventory level associated costs such as set up costs, inventory carrying costs, material purchase costs, storage costs, stock-out costs, backorder costs etc. can be minimized by efficient inventory policies. *Khairun Nahar, shapla05.ipe@gmail.com **Khan Md. Ariful Haque, North arolina A & T State University, U.S.A. arif99ipe@yahoo.com *Mahbub A Jannat, *Sonia Akhter, *Umme Khatune Jannat, *Dr. Md. Mosharraf Hossain, (orresponding Author) mosharraf80@yahoo.com *Department of Industrial and Production Engineering (IPE), Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh.

2 2. Literature Review Various studies and researches had been conducted on last few decades among them several significant works are discussed to summarize the goal or purpose of this paper with clarity. roston s (1972; with corrections by Rao, 1973) key insight was that, when a system is being used for stock replenishment, or batch size ordering, the replenishment will almost certainly be triggered by a demand which has occurred in the most recent interval. The net effect of this phenomenon when forecasting demand for a product that is required only intermittently is that the mean demand is over-estimated and the variance is underestimated. Thus, an inventory decision based upon application of the usual exponential smoothing formulae will typically produce inappropriate stock levels. roston proceeded to develop an alternative approach based upon: an exponential smoothing scheme to update expected order size, an exponential smoothing scheme to update the time gap to the next order and an assumption that timing and order size are independent. Van Eijs et al (1992) divided the joint replenishment strategy into two types. One is direct grouping strategy and another is indirect grouping strategy. They mentioned direct grouping strategy as the replenishment cycles of the groups are not an integer multiple of a basic cycle, so the family replenishments are not equally spaced. Indirect grouping strategies outperform direct grouping strategies for high major setup cost, because different groups are jointly replenished when using an indirect grouping strategy. In direct grouping strategy the problem is to form (directly) a predetermined number of groups in such a way that the total costs of the items in the family are as low as possible. And indirect grouping Strategies is less flexible in setting replenishment cycles, since these cycles are restricted to integer multiples of the basic cycle time. Johnston and Boylan (1996a) and Syntetos and Boylan (2005) notably made a number of extensions and improvements to the original roston method. Syntetos and Boylan (2001) had shown that the original roston estimators were biased; they then (Syntetos and Boylan, 2005) developed a new method, which refers to as the bias-adjusted roston method, and evaluated its performance in an extensive empirical study. Out-of-sample comparisons indicate that the new method provides superior point forecasts for faster intermittent items; that is, those with relatively short mean times between orders. Helo (2004) mentioned that as a result of the level of inventory in the entire supply chain is reduced and inventory turnover increases, while inventory carrying cost and working capital cost decreases. But he does not mentioned that maximum possible level of inventory could reduce the possibility of stock out hence reduce the customer dissatisfaction and reduce the possibility of loss of market of a product. Min and Yu (IEB2004) said that collaborative planning, forecasting and replenishment (PFR) that was proven to be successful in minimizing safety stocks, improving order fill rates, increasing sales, and reducing customer response time. The broad subcategories of methodological classification are descriptive (conceptual) and normative (analytical) studies. The descriptive studies often illustrate the numerous managerial benefits of using PFR or evaluate the outcomes of PFR. On the other hand, normative studies designed quantitative models to assess the positive impact of PFR (or information sharing across the vertical supply chain) on various supply chain performances in comparison to old legacy systems or less structured forecasting procedures. These quantitative models can be broken down into mathematical models and simulation experiments. The core mathematical models also include various forecasting techniques which may be categorized as: time series and causal methods. 2

3 Shenstone and Hyndman (2005) showed that there is no possible model leading to the roston forecast function unless we allow a sample space for order size that can take on negative as well as positive values. Rahman (2008) stated the models which are extended to forecast demand from an incomplete dataset by the assumption that the original dataset contains missing values. The forecast by a multiplicative exponential smoothing model is used to compare all the forecast. The performances are tested by several error measures such as relative errors, mean absolute deviation, and tracking signals. A newsvendor inventory model with emergency procurement options and a periodic review model are studied to determine the procurement quantity and inventory costs. The inventory cost of each demand forecast relative to the cost of actual demand is used as the basis to choose an appropriate forecast for the dataset. The result reveals that forecasting models using Bayesian ARIMA model and Bayesian probability models perform better. The flexibility in the Bayesian approaches allows wider variability in the model parameters helps to improve demand forecasts. This models are particularly useful when the past demand information is incomplete. There are other important sources of uncertainty which have received relatively little attention. Praharsi et al (2010) developed an innovative heuristic that offer a different approach to solve a joint replenishment problem. The innovative heuristic can be implemented for classical as well as for the decentralized models. For the centralized policy, the innovative heuristic does not work well because the policy does not use an integer multiple. For stochastic demand such as Poisson or Negative Exponential distribution, the innovative heuristic can be implemented in random variants which are generated by Monte arlo simulation. 3. Research Methodology The approach of this paper work was a deductive approach. Qualitative method in secondary data collection that provides a deeper understanding of the problem is used. The paper covers both secondary and primary data. Here secondary and primary data sources are used with an aim of strengthening the content of the entire work. The authors used the secondary data first which provide more information to make comparison, interpretation and understanding the primary data. Data is analyzed with the traditional method of AB analysis. Forecasting is done for the A category items using the 3 period moving average method. Multiple items joint replenishment policy is used to determine the Economic Order Quantity (EOQ) for those items. Then comparison between EOQ and Actual Order Quantity is done. 4. Results 4.1 AB Analysis The AB analysis is a business term used to define an inventory categorization technique often used in materials management. It is also known as selective inventory control. Example of AB class is: A ategory: 20% of the items accounts for 70% of the annual consumption value of the items. B ategory: 30% of the items accounts for 25% of the annual consumption value of the items. ategory: 50% of the items accounts for 5% of the annual consumption value of the items. 3

4 There are 111 items of electric equipment in the store house from to fiscal years. According to annual demand/ consumption per unit of these items that are categorized ( A, B & category), from which targeted A category is found for further analysis, forecast demand and determine EOQ; and compared those corresponding values for fiscal year omponent ode Price/units Table 1: ategory A Items from Total List Demand Units/ Year onsumption umulative onsumption umulative onsumption % , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Percentage of onsumption A atergory B atergory atergory 71% 9% 20% Figure 1: Pie hart for onsumption Percentage 4.2 Demand Forecast Having a forecast of demand is essential for determining how much capacity or supply will be needed to meet demand. Two aspects of forecasts are important. One is the expected level of demand; the other is the degree of accuracy that can be assigned to a forecast (i.e., the potential size of forecast error). The expected level of demand can be a function of some structural variation, such as a trend or seasonal variation. This paper utilized weighted moving average method for forecasting demand. F t = w t (A t ) + w t-1 (A t-1 ) +. + w t-n (A t-n ) Where, w t = Weight for the period t, w t-1 = Weight for period t-1, etc. A t = Actual value in period t, A t-1 = Actual value for period t-1, etc. 4

5 Table 2: alculation of Demand Forecasting ( A ategory material) Product ode Demand(D t ) Forecast(F t ) Pack oil oil Pack Weight is used as 0.5, 0.3 and 0.1 from latest to earlier 4.3 Economic Order Quantity (EOQ) To calculate economic order quantity multiple item joint replenishment policy is used. Joint replenishment can occur when a firm is either purchasing a number of items from an outside vendor or purchasing internally. The fixed cost is analogous to the major setup cost incurred in manufacturing several items with a common setup. By grouping families of these items, valuable capacity that would otherwise be spent on several unnecessary setups might be saved. Therefore it is necessary to decide how much of each item should be purchased during any given setup (order). The objective of this model is to minimize the total relevant costs for a group of items jointly purchased which is done by determining economic order quantities for a group of items minimizing the total cost of inventories and setups per period. This model offers an equation to determine the economic order quantity which is given bellow assuming demands, lead times, costs and inventory carrying percentage for all items are given and deterministic. The optimal value of all items in taka ordered during a cycle is (1) Where, Q (Tk.) = Total value of all items ordered during a cycle. Q i (Tk.) = Value of item i (in Tk.) ordered during a cycle. A (Tk.) = value of all items in the group ordered. S = Fixed cost of placing an order for a group of items. s i = Item dependent marginal cost of placing an order associated with an additional item i. I = Inventory carrying charge expressed as a decimal. Q i = Quantity of item i ordered during a cycle. S + s i = Total cost of placing an order S = Salary of employees + Administrative expenses s i = (Labor cost + Transportation cost) for individual item S + s i = Tk [Data is taken from the organization] 5

6 A =Tk. 390, [From table 1] I = 0.1 [Given] = 337, Q 1 (Tk) = = 45, ; Q 1 = = units Similarly, Order quantity of each item per cycle (in table 3) is calculated. Table 3: Order Value in & Quantity of A lass Items Ordered per ycle ode No. of items demand a i () Unit cost i () demand quantity Order size Q i () , , , , , , , , , , , , , , , , , , , , , , , , , , , , Total 39, Size quantity Q i In the Table 3, the value of Unit cost i is obtained by the following way: i = urrent market price + [urrent market price * (11% Taxes + 10% Profit)]. For example, item 102 costs, i = [320*(11%+10%)] =387.2 Table 4: omparison between EOQ and Actual Order Quantity of A lass Items omponent code Economic Order Quantity (EOQ) Stocked quantity from previous year, S E= (EOQ-S) Actual Order quantity at ( ), A (A-E) Unit Price in Saved amount , , , , (-10.54) , , (-49.33) , (-25.01) , , (-13.45) , (-108.8) (-22.01) ,150 Total 234,383 6

7 The relationship between Actual Order Quantity and Economic Order Quantity of A lass items (for the year ) is shown in the following graph: Economic order quantity Actual order quantity Figure 2: Representation of Actual Order Quantity and Forecasted Quantity. From the above figure it is shown that actual order quantity is greater than calculated forecasted demand of items and zero actual order quantity means no order has been placed. 5. onclusion A systematic ordering strategy, multiple item joint replenishment policy, is used the economic order quantity using forecasted demand for each product of ategory A items and comparing with the existing value it has been found that the saved amount is 234,383. It has also seen that the maximum amount of product availability can be achieved with minimum total cost. Reducing the amount of inventory, capital tied up can also be released and this capital can be used for further improvement of the total inventory system. References roston, J.D. (1972) Forecasting and stock control for intermittent demands, Operational Research Quarterly, Vol. 23, pp DeLurgio, S.A. (1998), Forecasting Principles and Applications, New York, NY: Irwin-McGraw-Hill. Johnston, F.R. and Boylan, J.E. (1996a), Forecasting for items with intermittent demand, Journal of the Operational Research Society, Vol. 47, pp

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