Inventory is a modern trend. For example, why does every car or a truck carry aspare tyre? It is because, in case of any puncture, the rider can change the tyre andimmediately be on his way. He need not have to be stranded for a more stretchedtime. To avoid similar circumstances in business, companies carry inventory both forraw materials and finished goods..

We can say that Inventories are one of the main ingredients for any physicaldistribution system. We cannot distribute any product without any inventory.However, costs and investments are involved in inventories. They also directlyinfluence the movement and transportation and cost. If inventory policy of a companydictates maintenance of large stocks, then transportation characteristic will be FTL(Full truck Load) shipments. This would result in economies of scale. The logisticsmanager is responsible for all these costs. Responsibility lies in him for makingdecisions concerning the size, depth or location of these inventories, the lot size,route and mode of transport. His primary objective should be in optimizingdistribution costs. He has to find an economical balance between transportation andinventory cost where inventories represent an important alternative to creating timeand place utility in the product

Inventory management can be defined as the sum total of those related activitiesessential for the procurement, storage, sale, disposal or use of material. This can beunderstood by answering the following questions -when is a refrigerator not arefrigerator? In terms of physical distribution, a refrigerator is not a refrigerator whenit is in Delhi, whereas when the demand is in Chandigarh. Further more, if the colorrequired is grey and the refrigerator is blue then also the refrigerator is not arefrigerator. To conclude, utilities are created in goods when the right product isavailable at the right place, at the right time, at the right quantity and is available tothe 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 storagespace resourcefully, so that the stocks do not exceed the available storage space.They are responsible in maintaining accountability of inventory assets. They have tomeet the set budgets and decide upon what to order, when to order, how to order sothat stock is available on time and at an optimum cost. Inventory managers haveacknowledged that some of these objectives are contradictory; but their job is toachieve a economic balance between these conflicting variables. But to achieve thiseconomic balance, a clear understanding of many interconnected variables isrequired -functions, types of costs, problems and the like. The following sectionsprovide an insight into these variables. Further, it elaborates upon various aspects ofinventory control in physical distribution system.

Objectives1. 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 resources5. To identify the problems of inventory management in ksrtc, chitradurga6. To offers suggestions for the effective working of ksrtc, chitradurga

ScopeInventory 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 con-

trol 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 oers 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; like Sobel, they aim

to optimally choose R.) They argue for the optimality of a

nite generalized base

stock policy speci

ed by two base stock levels (and the parameter R) with respect

to the discounted expected cost. They conjecture that the same type of policy isoptimal with respect to an average cost criterion. This conjecture is repeated in

Geunes (1999) and in Serel et al. (2001). Yang et al. (2005) consider a model with

capacitated \in-house" production and an uncapacitated \outsourcing" option. In

their default setting, the capacity level uctuates randomly and there is a

xed cost

of outsourcing as well as a per-unit cost. These complexities aside, they oer in eect

an argument for average optimality, in a discrete setting, of a

nite generalized base

stock policy when the ordering cost function is nondecreasing, convex, and piecewise

linear with two linear pieces. Our argument accommodates a cost function with any

(

nite) number of linear pieces, and we also allow non-discrete demand distributions.

Stochastic production smoothing models such as that of Beckmann (1961) are also

relevant here. Beckmann's model incorporates a linear cost of production along with

per-unit costs of increasing and decreasing the production level relative to the level

chosen for the preceding period. Thus we have in eect an inventory model in which

the ordering cost function is convex and piecewise linear with two linear pieces, such

that the location of the kink in the function may change from period to period. It

is even allowed that the function may decrease up to the kink, signifying that it is

very costly to reduce production. (This characteristic is also allowed in Sobel 1970.)

Later work on production smoothing models in this vein includes Sobel (1969) and

Sobel (1971). In our model, by contrast, the ordering cost function is nondecreasingand

xed across periods, while we allow any number of linear pieces.

Huh et al. (2008) is a study aimed at developing a framework under which the

optimality of particular inventory control policy structures may be immediately ex-

tended from

nite-horizon to in

nite-horizon (including average cost) settings. Their

framework includes the possibility of a nondecreasing, piecewise linear and convex

ordering cost, though they do not discuss the speci

c structure of optimal policies

for this situation. Their framework also requires bounds on the marginal holding

and backlogging costs, whereas our arguments do not require such bounds. Further-more, in the course of our technical argument we oer some structural insight for our

problem that may be of wider use and that is not present in their paper.

1.2.2 Inventory control under average cost criteria

As observed in the general treatments of Markov decision processes in Heyman and

Sobel (1984, p. 171) and Puterman (1994, p. 331), an average cost criterion may

be appropriate for modeling systems in which decisions are made frequently. These

authors also note the complexity of technical analysis under average cost criteria;

problematic characteristics of inventory models in particular include the possibility

of state spaces and feasible action sets that are unbounded (and perhaps continuous),

as well as unbounded cost functions.

In Section 1.2.1 above, we have discussed work on inventory control under an

average cost criterion when the ordering cost function is convex (and nonlinear).

Here, we mention work on stochastic inventory control models with other types of

ordering cost functions.

The case of an inventory model with a linear ordering cost function (as in the

rst case examined in Karlin 1958, mentioned above) is studied in Vega-Amaya andMontes-de-Oca (1998). They argue that the base stock structure remains optimal un-

der an average cost criterion. Our model encompasses linear (nondecreasing) ordering

cost functions, but we assume backlogging of unmet demand while they assume lost

sales.

Literature on average optimality in models with a

xed cost of ordering in addition

to a linear component (as in the second case examined in Karlin 1958) is briey

reviewed in Feinberg and Lewis (2006). They cite several papers arguing that the

(s; S) structure remains optimal. One such paper of particular signi

cance for us

is Zheng (1991), which employs a relaxation technique that we use as well. Other

studies cited include Iglehart (1963), Veinott and Wagner (1965), Beyer and Sethi(1999), and Chen and Simchi-Levi (2004). The forthcoming volume of Beyer et al.

(2009) also promises to discuss this case.

The work by Huh et al. (2008) mentioned above stands to establish average opti-

mality of policy structures, for a wide class of cost functions and other model param-

eters, whenever the structures are known to be optimal in a

nite-horizon setting.

Limitations of Study Time was the main constraint. The researcher has to complete her work with in the stipulated time limit and the sample size

was restricted to 50 respondents. The studies limited to ksrtc in Chitradurga. The respondents were of different perceptions and hence the researcher has a tedious task at

hand in locating the respondents from Chitradurga. The respondents were not open up to the questions asked by the researcher.

Research MethodologyThe research methodology includes the information regarding the sample size design, data

collection methods and the analytical tools used.

Here also the same is explained in detail regarding the sample size, data collection methods and

the analytical methods used in this research.

Data Collection MethodsThere are so many ways in which data shall be collected. The methods that have been used for

collecting the data are:

Primary Research

Questionnaire Method Direct Interview Method

Secondary Research

Books Internet Articles Newspapers Magazines

Both of these methods have been used for collecting the data. Questionnaire method was used for

collecting the primary data. This has been done by firstly preparing an appropriate structured

questionnaire..

Secondary data for the purpose of research has been mainly taken from internet and various

magazines. Various journals have been used for the purpose of reference.

Sample Design

Sample size : 50Sample selection criteria : Random selectionSample selection area : Chitradurga

Company profile

Karnataka State Road Transport Corporation or KSRTC(Kannada-ಕರ್ನಾ��ಟಕ ರಾ�ಜ್ಯ� ರಸ್ತೆ� ಸಾ�ರಿಗೆ ಸಂ�ಸ್ತೆ�) is a state-owned

road transportation company in Karnataka. KSRTC was set up in 1961 under the provisions of Road Transport

Corporation Act, 1950. It is wholly owned by the Government of Karnataka. The Government of India is also a

shareholder in this corporation. KSRTC is also known for its introduction of Volvo B7RLE low body city buses. These

buses are air conditioned, with improved tyre suspensions, a far cry from the other old members of fleet.

Till August 1997, KSRTC had a fleet of 10,400 buses, operating about 9500 schedules. In August 1997, KSRTC was

divided and a new corporation by the name Bangalore Metropolitan Transport Corporation (BMTC). In November

1997, another new road transport corporation called North Western Karnataka Road Transport Corporation

(NWKRTC) was formed to cater to the transportation needs of North Western parts of Karnataka. Recently, the North

Eastern Karnataka Road Transport Corporation (NEKRTC) was also formed with its corporate office in Gulbarga. The

company runs a fleet of buses of all types like ordinary,semi-luxury, deluxe, and air-conditioned Volvo "Airavat"

buses. The KSRTC operates services within Karnataka as well as far flung destinations such

as Trivandrum, Mumbai, and Vijayawada, among others. The KSRTC runs different type of buses to suit every

section of the society. Their main mission is “Meeting Challenges with Innovation”.

As of 2010, KSRTC was the largest state-owned Volvo fleet operator in India.[2]

KSRTC services almost all villages in Karnataka. At present, 92% villages are served by KSRTC (6743 out of 7298

Villages) and 44% in other areas (6743 out of 7298).[3] KSRTC operates 6463 schedules in a day covering an

effective distance of 23.74 lakh km with a total fleet of 7599 buses. It transports, on an average, 24.57 lakh

passengers per day.

[edit]Awards and recognition

1. Union Transport Minister’s Trophy for lowest accident during 1996-99, 1997-00 and 1998-01.

2. Parisara Award 2001 by the State Govt.

3. Safety Award for 2001-02 by the Chartered Institute of Logistic and Transport, India.

4. IRTE Pince Michale International Road Safety Award 2001.

5. PCRA Award 2001-02 and 2002-03.

6. Golden Peacock International Award for 2002 (runners-up).

7. Golden Peacock Environment Management Award for 2003 (winner).

8. Golden Peacock Eco Innovation Award 2004 (second time).

9. Urban Mobility India -2009

10. mBillionth South Asia Awards -2010

11. Earth Care Award-2010

12. Chief Minister Rathna Award-2009-10

13. 'Public Sector bus fleet operator of the year' award from Apollo group