important role in the success of the organizations in the new business environment. It is not clearly possible for the organizations that store hundreds of inventory items to economically design an inventory management policy for each inventory item separately. To have an efficient control of a huge amount of inventory items, traditional approach is to classify the inventory into different groups. Different inventory control policies can then applied to different groups. The wellknown ABC classification is simple to understand and easy to use. Moreover, various inventory items may play quite different roles in the business of the organization. Hence, the managers need to classify these items in order to control each inventory category properly based on its importance rating. In this thesis we consider a model of college hostel mess stores items (grocery and vegetables) for inventory management through ABC analysis. This research is composed of the following sections: In the first section, the criteria affecting the evaluation of the inventory control system of the studied mess stores and the priority of each one of them will be identified, in the second section, the priority of each criterion such as cost of item, annual demand for an each item hence find annual consumption cost in each inventory category (A, B, C) is Calculated based on conventional model, in the third section, presents an alternative way of classifying the different productive items of accompanies and this ABC model compares with the classic Pareto classification, which ranks productive items according to their importance in terms of frequency and costs whereas rankings obtained using the classical method are based on information about costs and demand over a period in the past "A-items" that result from this new classification.
iven that at present, all the organizations maintain thousands different types of inventory, it is likely to lose the effective inventory management. Therefore, it is particularly important for all the organizations to establish the appropriate inventory control systems or to evaluate and improve the existing inventory control systems. Because on the one hand, the organization encounters the inventory-related costs, including Cost of Holding, Cost of Ordering, Cost of Shortage the increase of each one due to the lack of a suitable inventory control system will have negative effects on the profitability of the organization. On the other hand, since the number of inventory items is largely increasing due to the increase of the customers' demands for different products, the organizations should have a quick and effective response to the customers' demands to survive and maintain their competitive advantage. The establishment or improvement of an appropriate inventory control system can lead the organization in this path. Considering that today, the organizations save a large percentage of their total investment in the inventories, it has become of a special importance to all organizations to properly manage the inventory and establish a proper inventory control system. According to what was mentioned, all the organizations need an appropriate inventory control and planning system in order to effectively manage their resources and inventories. Therefore, in order to create a perfect inventory control system, various inventory items should be classified into the significant categories based on appropriate criteria and standards. Various models and methods have been so far presented to classify inventory among which, ABC analysis approach is one of the most common methods which is widely used for planning and inventory control (Kilgour & et al 2006). Inventory classification based on ABC analysis allows the organization to classify its inventory into the significant categories. Generally, the above approach has been formed based on the Pareto Principle which is also known as "20-80" law. Regarding the organizations' inventory, this principle will be expressed as follows: In the manufacturing organizations, there are only a few inventories which mostly contribute to the cost of the annual consumption of the organization's inventory system and there are only anew inventories which a little contribute to the dollar value of the annual consumption of the inventory system. Given that the primary purpose of the inventory classification based on this approach is to create appropriate control levels for each inventory category, this question will be raised that whether the inventory classification based on single criterion ABC analysis will be able to meet all the needs of the organization's inventory control system. As a result, the organizations can apply proper control policies by identifying the most effective criteria in their inventory classification. This study has also tried to present proposed ABC model for the hostel mess stores, in order to evaluate the inventory control system of the studied in that. For this purpose, first, the criteria affecting the evaluation of the inventory control system, classification of inventory and the priority of each One of the criteria in the studied mess stores and the priority of each one of the criteria in each inventory category (A, B, C) have been identified and model will be proposed.
Literature Review
A study is conducting in TCE men's hostel for my thesis. In TCE mans hostel there are two stores are available. One is for variety mess store and another one is for value mess store. Totally 550 students are in value mess and 650 students in variety mess. Here I identified the problem in inventory in both stores. Due to incorrect optimal order quantity and insufficient forecasting the more inventories held in both mess. In the mess stores they used previous experience for order the items. They did not use any formulation or techniques such as P MODEL, Q MODEL system for find the optimal order quantity. So that only inventory problem arises there. So here in my thesis I will use both the system and find EOQ for all items thereby reduce the inventory level and reducing annual consumption cost of mess stores. In order to find the EOQ, it is very important to know about that are the various items affecting the inventory cost in stores. So ABC analysis is requiring knowing about the inventory affecting items. Form the ABC analysis we have easily know the items which are contribute in inventory, only the A types item. So, in this paper presents only the ABC classification of stores items (grocery & vegetables) of hostel mess stores.
IV. In this proposed methodology the various data's such as unit price of an item, annual demand of an item were collected from mess stores and based on this data, grocery and vegetables were segregated for doing the ABC analysis of an each and individual items.
V. From this ABC analysis of vegetables items A type items have more annual consumption costs. So here 4 items have classified under A category out of 28 items.
In this study, the indices affecting the evaluation and control of the inventory control system of TCE men's hostel stores. the results of ABC classification that these criterions of "the required accuracy in ordering" in the inventory category A and the criterion of "the effect of inventory on the process" in the inventory categories B and C have the highest importance. From this ABC analysis of grocery items A type items have more annual consumption costs so here 13 items have classified under A category out of 57 item and of groceries. For vegetables here 4 items have classified
items have more annual consumption costs and creates more inventory in stores. So Economic Order Quantity and re-order level will be calculated for these A type items hence reduce inventory and annual consumption cost.
1 : ABC analysis for grocery | ||||||
Annual | Cumulative | |||||
Price/unit | Unit/year | consumption | values of Annual | |||
Sl.No Description | (Rs.) | (kg) | Rs./year | Consumption(Rs.) Classification | ||
24 | Peanut oil | 85.9 | 15600 | 134004 | 1340040 | A |
1 | Rice(Ponni) | 34.6 | 21600 | 747360 | 2087400 | A |
2 | Idly rice | 28 | 14400 | 403200 | 2490600 | A |
35 | Ghee | 370 | 1080 | 399600 | 2890200 | A |
8 | Black gram | 62.9 | 4800 | 301920 | 3192120 | A |
28 | Wheat flour | 34.7 | 7200 | 249840 | 3441960 | A |
26 | Papadam | 100 | 2400 | 240000 | 3681960 | A |
54 | Garlic | 119 | 1800 | 214200 | 3896160 | A |
5 | Red gram | 64.6 | 3240 | 209304 | 4105464 | A |
6 | Green gram | 71.9 | 2880 | 207072 | 4312536 | A |
4 | Basmati rice | 84.75 | 2440 | 206790 | 4519326 | A |
21 | Fried gram | 53.5 | 3600 | 192600 | 4711926 | A |
25 | Sesame oil | 258 | 720 | 185760 | 4897686 | A |
41 | Boost | 334 | 540 | 180360 | 5078046 | B |
42 | Bounvita | 330 | 540 | 178200 | 5256246 | B |
37 | Prunes | 408 | 360 | 146880 | 5403126 | B |
48 | Vermicelli | 54 | 2440 | 131760 | 5534886 | B |
20 | Asafoetida | 600 | 180 | 108000 | 5642886 | B |
36 | Cardamoms | 599 | 180 | 107820 | 5750706 | B |
27 | Maida flour | 35.5 | 3000 | 106500 | 5857206 | B |
39 | Tea powder | 290 | 360 | 104400 | 5961606 | B |
51 | Ground nut | 82 | 1200 | 98400 | 6060006 | B |
2 : ABC analysis of vegetables | ||||||
Annual | Cumulative Value | |||||
Price/unit | consumption | of Annual | ||||
Sl.No Description | (Rs.) | Unit(kg)/year | Rs./year | Consumption(Rs.) Type | ||
1 | Onion | 19 | 30000 | 570000 | 570000 | A |
3 | Tomato | 13 | 10800 | 140400 | 710400 | A |
4 | Potato | 23 | 6000 | 138000 | 848400 | A |
20 Cauli flower | 24 | 2600 | 62400 | 910800 | A | |
25 | Amaranth | 19 | 3000 | 57000 | 967800 | B |
2 | Shallot | 38 | 1200 | 45600 | 1013400 | B |
28 | Drumstick | 60 | 720 | 43200 | 1056600 | B |
Ladies | ||||||
8 | finger | 15 | 2400 | 36000 | 1092600 | B |
19 | Beans | 19 | 1800 | 34200 | 1126800 | B |
7 | Brinjal | 13 | 2440 | 31720 | 1158520 | B |
10 | Cabbage | 10 | 2400 | 24000 | 1182520 | B |
9 | Green chilli | 20 | 1080 | 21600 | 1204120 | B |
17 | Ginger | 54 | 360 | 19440 | 1223560 | B |
27 | Panner | 25 | 720 | 18000 | 1241560 | C |
12 | carrot | 16 | 960 | 15360 | 1256920 | C |
Coriander | ||||||
15 | leaves | 20 | 720 | 14400 | 1271320 | C |
26 | Mushroom | 20 | 720 | 14400 | 1285720 | C |
An Improvement to Multiple Criteria ABC Inventory Classification. European Journal of Operational Research 2010. p. .
Multi-Item Inventory Aggregation Into Groups. Journal of Operational Research Society 1981. p. .
Controlling Inventory by Combining ABC Analysis and Fuzzy Classification. European Journal of Operational 2008.
Multicriteria Inventory Classification Using a Genetic Algorithm. European Journal of Operational Research 1998. p. .
Multi-item classification and generic inventory stock control policies. Production and Inventory Management Journal 1988. 29.
Operations related groups (ORGs): a clustering procedure for production/ inventory systems. Journal of Operations Management 1990. 9 p. .