Inventory is a modern trend. For example, why does every car or a truck carry a spare tyre? It is because, in case of any puncture, the rider can change the tyre and immediately be on his way. He need not have to be stranded for a more stretched time. To avoid similar circumstances in business, companies carry inventory both for raw materials and finished goods..
We can say that Inventories are one of the main ingredients for any physical distribution system. We cannot distribute any product without any inventory. However, costs and investments are involved in inventories. They also directly influence the movement and transportation and cost. If inventory policy of a company dictates maintenance of large stocks, then transportation characteristic will be FTL (Full truck Load) shipments. This would result in economies of scale. The logistics manager is responsible for all these costs. Responsibility lies in him for making decisions concerning the size, depth or location of these inventories, the lot size, route and mode of transport. His primary objective should be in optimizing distribution costs. He has to find an economical balance between transportation and inventory cost where inventories represent an important alternative to creating time and place utility in the product Inventory management can be defined as the sum total of those related activities essential for the procurement, storage, sale, disposal or use of material. This can be understood by answering the following questions -when is a refrigerator not a refrigerator? In terms of physical distribution, a refrigerator is not a refrigerator when it is in Delhi, whereas when the demand is in Chandigarh. Further more, if the color required is grey and the refrigerator is blue then also the refrigerator is not a refrigerator. To conclude, utilities are created in goods when the right product is available at the right place, at the right time, at the right quantity and is available to the right customer. Inventory management deals itself with all these problems, placing importance on the quantities of goods needed Inventory managers have to keep stock when required and utilize available storage space resourcefully, so that the stocks do not exceed the available storage space. They are responsible in maintaining accountability of inventory assets. They have to meet the set budgets and decide upon what to order, when to order, how to order so that stock is available on time and at an optimum cost. Inventory managers have acknowledged that some of these objectives are contradictory; but their job is to achieve a economic balance between these conflicting variables. But to achieve this economic balance, a clear understanding of many interconnected variables is required -functions, types of costs, problems and the like. The following sections provide an insight into these variables. Further, it elaborates upon various aspects of inventory control in physical distribution system.
Objectives 1. To Know about complete inventory in ksrtc, chitradurga. 2. To support for inventory management helps to record and track materials on the basis of both quantity and value in ksrtc, chitradurga. 3. To improves cash flow, visibility, and decision making. 4. To track quantity and value of all your materials, perform physical inventory, and optimize warehouse resources 5. To identify the problems of inventory management in ksrtc, chitradurga 6. To offers suggestions for the effective working of ksrtc, chitradurgaScopeInventory management is a very important function that determines the health of the supply chain as well as the impacts the financial health of the balance sheet. Every organization constantly strives to maintain optimum inventory to be able to meet its requirements and avoid over or under inventory that can impact the financial figures.
The study of the inventory management towards ksrtc under taken onl;y on the context of chitradurga city only Many more researchers have been carried out at global level to trace and evaluate the problems of inventory management in transportation and some have took over to study the inventory management. While our study are carried with some of constraints namely time and resources, in restricted only to the chitradurga city 1.2 Literature review In reviewing related literature, we focus on work featuring periodic review inventory models with a convex (and nonlinear) ordering cost function or with an average cost criterion. Broader coverage of the subject of inventory theory may be found in Veinott (1966), Porteus (1990), Zipkin (2000), and Porteus (2002). References on Markov decision process (MDP) models more generally, including models under average cost criteria, include Heyman and Sobel (1984), Puterman (1994), Arapostathis et al. (1993), Hernandez-Lerma and Lasserre (1996), Sennott (1999), and Feinberg and Shwartz (2002). 1.2.1 Inventory control with a convex ordering cost function
Convex ordering cost functions appeared early in the literature on inventory control and production planning. Several studies in this area consider deterministic models, unlike our framework which features stochastic demand. For example, Veinott (1964) studies a production and inventory model with a convex ordering cost function (or rather a convex production cost function) in a
nite-horizon setting with deterministic future demand; here, uncertainty is dealt with by sensitivity analysis. In a subsequent survey of inventory theory, Veinott (1966) discusses other work with convex ordering cost functions in a deterministic setting, much of which was published in the 1950s. A few additional references along these lines are given in Sethi et al. (2005, p. 11). In a stochastic and dynamic setting, Karlin (1958) considers basic inventory control models featuring three types of ordering cost functions, associating each type of function with an optimal decision rule having a particular structure: 1. A linear ordering cost function is associated with what are now widely known as base stock rules, which have the form: order up to meet a target inventory level s
when the current period's inventory level is below s
; i.e., order the quantity (s I) if the current inventory level is I < s
, and otherwise order 3A convex ordering cost function is associated with what have been called (in Porteus 1990) generalized base stock rules, which have the property that the order-up-to level is a nondecreasing function of the current inventory level, while the order quantity is a nonincreasing function of the current inventory level. Here, Karlin's criterion for evaluating a given policy is the discounted expected cost
incurred, which by nature diminishes the emphasis on the (perhaps very) long term. By contrast, we are concerned with the structure of optimal policies under an average cost criterion, which is intended to ignore the short-term, transient behavior of the system and focus on the steady state. Also notable is that Karlin assumes strict convexity of the ordering cost function, apparently making extensive further speci-
cation of optimal policies cumbersome in general. We instead assume a piecewise linear form that implies an intuitive and relatively simple optimal policy structure. Further results for stochastic inventory control with a strictly convex ordering cost function under a discounted cost criterion may be found in Bulinskaya (1967). The case of a piecewise linear and convex ordering cost function is discussed in the survey of stochastic inventory theory by Porteus (1990). He describes the structure of a
nite generalized base stock rule by reference to a hypothetical situation involving alternative production technologies. Each technology has a linear cost and a
xed per-period capacity|except the most expensive technology, which is uncapacitated. This leads to a convex and piecewise linear ordering cost function, as in our setting, on the assumption that a particular technology is utilized only if all cheaper technologies are being used to capacity. The decision rule is then de
ned by a nonincreasing set of base stock levels corresponding to the technologies in increasing order of marginal cost. There may therefore be a range of inventory levels for which we do not utilize a given technology, though we utilize all cheaper technologies to capacity. Porteus asserts the optimality of a
nite generalized base stock policy under a discounted expected cost criterion, but he o ers no proof or reference for this proposition. The only optimality proof we have found that allows an ordering cost function with any number of linear pieces is in Bensoussan et al. (1983), under the
nite-horizon total expected cost criterion. Unlike Bensoussan et al., we deal with an average cost criterion, and we also allow unbounded marginal holding and backlogging costs|as well as a marginal ordering cost equal to zero for the cheapest source. Considerable attention has been given to stochastic models with piecewise linear and convex ordering cost functions for the special case with two linear pieces. Sobel(1970) studies such a model in which the location of the kink in the function is chosen at the outset and thereafter is
xed from period to period. He argues for the optimality of a
nite generalized base stock policy when the location of the kink is given|under discounted cost criteria, and also under an average cost criterion for the case of discrete demand. His
nite-horizon results are used in Kleindorfer and Kunreuther (1978). Henig et al. (1997) consider a similar model with an ordering cost function equal to zero for up to R units, with a cost of c per additional unit. (This is in the context of supply and transportation contracts, in which the available volume R per period may be speci
ed by a long-term agreement; li