In the manufactory, a product has to pass through various successive stages starting from purchase of raw material and semi finished and/or finished components, inspection while receiving, storages of these materials, processing, quality control till desired shape and then dispatch for distribution. During all these stages huge inventory is need to maintain to meet the fluctuation between demand and supply.
Inventory includes all material that an organization/ manufacturing concern obtains in advance of need, holds until it is needed, and then uses, consumes, incorporates into a product, sells or otherwise disposes of. The inventory have to fulfill five functional roles: economies of scale, balance of supply and demand, specialization in manufacturing, protection from uncertainties and inventory as buffer Inventories tie money impacts an organization's financial status. Extreme high and low inventory level can be as problematic. Too much inventory involves unnecessary costs related to issues of storage, markdowns and obsolescence, while too little results in stockouts or disrupted production. As Inventory contributes to a greater part of total cost of a product or service and by efficient and effective Inventory Management, this cost can be reduced to a greater extent and customer satisfaction can/must be enhanced.
Due to dynamic nature of demand, it is very critical for the researchers to formulate ordering policy which will accurately incorporate the ever-changing realistic situations. Numerous ordering policies have been proposed, and some seems to be better than others. Unfortunately, polices that are good overall are not always the best choice for a particular application. For this reason, a great deal of research has gone into trying to make more effective use of existing models. Selection of an appropriate ordering policy directly affects profitability and customer satisfaction. But it is actually complicated to identify a correct ordering policy which reflect precise situation of the organization.
The literature lacks an identification of the selection criteria for the selection of ordering policy for a given application. Since these criteria have a very important role in the selection of ordering policy. Therefore proper identification of all such selection inventory parameters is a matter to be explored.
The paper is organized as follows: Section 2 reviews the literature in terms of ordering policies and selection parameters. Inventory parameters are identified has been identified in section 3. Section 4 reveals the major inferences drawn from the critical review of the available literature. Conclusion has been given at the end of paper.
LITERATURE REVIEW
A manufacturing concern has to maintain inventory in advance, holds until it is needed. By Efficient management this cost can be reduced to a greater. Purchase personnel have to take a number of decisions to optimize inventory costs including determining order quantity, review period, reorder point and handling the inventory.
In this dynamic fashion era, it is difficult for an enterprise to forecast the exact amount of inventory but they make an effort to maintain inventory close to exact inventory level. In this paper, we reviews the related literature published since Kaio & Oskai (1978) to include all the models, articles and parameters which are important for the selection of an inventory model.
Kaio & Oskai (1978) have developed two models for an ordering policy: 1. the original unit is re- spare takes over just after delivery, 2. the cycle delivered spare is put into inventory until the original unit fails. Dave, Upendra (1980) has developed probabilistic inventory model for deteriorating items system for stochastic demand. Mak, K. L. (1982) has developed a production lot size model which incorporates an unfilled-order backlog for an inventory system with exponential decaying items.
Matta & Sinha (1987) develop a simulation model to compute the optimal values of policy parameters for the centralized and distributed inventory systems to determine the conditions that favor the selection of one inventory system over the other. Arcelus & Srinivasan (1988) analyze the analytical and computational implications of different cost structures and their effect on inventory policy with the objective of maximization of the three most widely used indices of short-term asset performance profits, residual income and return on investment.
Heng et. al. (1991) derive a generalized inventory model with finite production rate, taking into consideration the effect of decay with the objective to minimize total cost by selecting the optimal lot size and order level. Hariga, Moncer A. (1994) has developed dynamic programming models for exponentially deteriorating items and perishable products having fixed life time.
Chiu, Huan Neng (1995) determine a best (Q, R) ordering policy under a positive order lead-time when the total expected average cost per unit time is minimized. Goyal & Gunasekaran (1995) present an integrated production-inventory-marketing model for deteriorating items considering the impact of marketing instruments such as price per unit and advertisement frequency on the demand of a deteriorating item to optimize the production batch size and raw material order size for the maximum net profit. Weng, Z. Kevin (1995) presents models for determining optimal all-unit and incremental quantity discount policies and investigates the effect of quantity discounts on increasing demand.
Giri et al. (1996) discusses an inventory model with an inventory-level-dependent demand rate followed by a constant demand rate for items deteriorating at a constant rate to find the retailers' optimal policy. Sarker et al. (1997) have been developed inventory model for various deteriorating items in which the demand is considered as a composite function consisting of a constant component, and a variable component which is proportional to the inventory level in the periods when there is a positive inventory build-up.
Andijani & Al-Dajani (1998) develop an optimal production policy of an inventory system with deteriorating items using linear quadratic regulator (LQR) technique to minimize the cost associated with inventory and production rate. Ouyang & Wu (1998) develop an algorithmic to find the optimal order quantity and optimal lead time for minimizing the sum of the ordering cost, holding cost, stock out cost and crashing cost, where both lead-time and the order quantity are considered as the decision variables. Cetinkaya & Parlar (1998) consider a single product, periodic review, stochastic demand inventory problem where backorders are allowed and penalized via fixed and proportional backorder costs simultaneously.
Schultz & Johansen (1999) develop a tailor-made policy-iteration algorithm to compute the parameters of the policy for each item. Ganeshan, Ram (1999) presents a near-optimal (s,Q) type inventory policy model incorporating three components: (i) the inventory analysis at the retailers, (ii) the demand process at the warehouse, and (iii) the inventory analysis at the warehouse for a production/distribution network with multiple suppliers replenishing a central warehouse, which in turn distributes to a large number of retailers.
Heijden, Matthieu van der (2000) develop an optimisation model that finds near cost-optimal control policies under fill rate constraints for large divergent networks within reasonable amount of time. Geunes & Zeng (2001) investigate the impact of inventory shortage policies on transportation costs for a two-stage base-stock distribution system under uncertain demand and develop model which provides a new method for setting stock levels that jointly minimizes inventory and transportation costs.
Yang & Wee (2002) develops a single-vendor; multi-buyers production-inventory policy for a deteriorating item with a constant production and demand rate to reduced the total joint cost for both the vendor and buyers. Chiang, Chi (2003) develop dynamic programming models for periodic inventory systems where regular orders as well as emergency orders can be placed periodically and devise optimal ordering policies that minimize the expected discounted cost over an infinite horizon, which includes item cost, inventory holding cost, and shortage cost. Mukhopadhyay et al. (2004) develop an inventory model for a deteriorating item with a price-dependent demand rate.
Mak et al. (2005) develop a mathematical model for an inventory system with items exhibiting lumpy demand patterns by an algorithm to determine the optimal control parameters.Sarker & Kindi (2006) develop an optimal ordering policy model (during the sale period) i.e. EOQ with a discounted price to maximize the annual gain. Hou, Kuo-Lung (2006) derives an inventory model for deteriorating items with stock-dependent consumption rate and shortages under inflation and time discounting over a finite planning horizon to determine the optimal order quantity and the optimal interval of the total cost function.
Kheljani et al. (2007) develop a supplier selection and order allocation model to minimize the average total cost incurred in the whole supply chain by applying mixed-integer nonlinear programming. Yin & Rajaram (2007) develop an algorithm to compute the optimal (s,S,p) type policy for a class of Markovian demand model (joint pricing and inventory control problem for a single product over a finite horizon and with periodic review) that follows a discrete uniform distribution.
Hill & Pakkala (2007) develop a solution procedure or model to find the item base stocks that jointly minimize the total cost of the system for a retail inventory system in which customer orders arrive at random and each order specifies a list of items. Tan et al. (2007) develop an expression for the expected cost benefits of imperfect Advance Demand information (ADI) for the myopic.
Chandra & Grabis (2008) develop a model for joint optimization of inventory and procurement costs for a single stage, long horizon inventory system by considering the variable lead-time and including the lead-time dependent procurement cost. Maity & Maity (2008) develop advertising and production policies for a multi-item production inventory system with deteriorating units, depreciation rate of sales; salvage value of deteriorated items, space capacity constraint, investment constraint and dynamic demand under the imprecise inflation and time discounting environment using UFM and GRG technique.
Giri, & Dohi (2009) derive the optimal inventory replenishment policies of two kinds of inventory models (Model 1: continuous review inventory model in which the stock level is continuously reviewed and the items are replenished by the regular order at that time if no shortage has occurred, Model 2: periodic review inventory model in which the stock level is reviewed only at a specified time. Haji et al. (2009) introduce a new ordering policy for inventory control in a two-echelon inventory system consisting of one central warehouse and a number of non-identical retailers.
Huang, Chao-Kuei (2010) develops an integrated inventory model to determine the optimal policy under conditions of order processing cost reduction and permissible delay in payments. Mohammaditabar, D. et al. (2011) have proposed a model that concurrently classifies inventory items and selects appropriate policies for each product group with the objective of having an effective inventory performance.
Table - 1 Inventory parameters used by various researchers
Parameters
Author
YEAR
Back-Ordering cost
Batch quantity/lot size/Order quantity
Demand (Deterministic)
Demand (Stochastic)
Depreciation rate
Deterioration
Holding cost
Inflation rate.
Insurance
Interest charges
lead time (Fixed)
lead time (manufacturing)
lead time (Procurement)
lead time (Demand)
lead time transportation
Material handling Cost
No.of orders/Frequency of Order
Order fill rates
Ordering cost
Penalty cost/Shortage Cost per item
Reorder point/reorder Level
Setup cost
Setup Cost (Holding cost)
Setup Cost (Ordering cost)
setup cost (Production)
Shipment cost/Transportation Cost
Taxes
Unit price
K. L. MAK
1982
X
X
X
Khalil F. Matta et al.
1987
X
X
X
X
Kheng Joo Heng et al.
1991
X
X
X
X
X
Kyung Mo Kim et al.
1991
X
X
X
X
X
X
Moncer A. Hariga
1994
X
X
X
X
Huan Neng Chiu
1995
X
X
X
X
X
K. L. MAK et al.
1995
X
X
X
X
X
X
Keisuke Matsuyama
1995
X
X
X
S. K. Goyal et al.
1995
X
X
X
X
Z. Kevin Weng
1995
X
X
X
X
X
Sven Axgter et al.
1996
X
X
X
X
X
X
Bhaba R. Sarker et al.
1997
X
X
X
X
Tadashi Dohi
1997
X
X
X
X
X
A. Andijani
1998
X
X
X
X
Jen-Ming Chen
1998
X
X
X
X
Liang-Yuh Ouyang et al.
1998
X
X
X
X
X
X
X
X
S. Viswanathan
1998
X
X
X
Helle Schultz
1999
X
X
X
X
X
Ram Ganeshan
1999
X
X
X
X
X
X
Hark Hwang
2000
X
X
X
X
X
X
P. C. Yang et al.
2002
B. Mahadevan et al.
2003
X
X
X
Sven Axsater
2003
X
X
X
X
X
X
X
Babak Ghalebsaz-Jeddi
2004
X
X
X
X
X
X
X
X
P.C. Yang
2004
X
X
X
X
X
X
X
X
X
S. Mukhopadhyay
2004
X
X
X
X
Hoon Jung et al.
2005
X
X
X
K. L. Mak et al.
2005
X
X
X
X
X
X
Kun-Jen Chunga et al.
2005
X
X
X
X
X
Bhaba R. Sarker
2006
X
X
X
X
Kuo-Lung Hou
2006
X
X
X
X
X
X
Chi Chiang
2007
X
X
X
X
X
J. Gheidar Kheljania et al.
2007
X
X
X
X
Roger M. Hill et al.
2007
X
X
X
Rui Yin et al.
2007
X
X
Tarkan Tan et al.
2007
X
X
X
X
X
Charu Chandra et al.
2008
X
X
X
X
X
X
X
D. Ding et al.
2008
X
X
Jui-Jung Liao
2008
X
X
X
X
X
X
X
K. Maity
2008
X
X
X
X
X
Choonjong Kwak et al.
2009
X
X
X
X
X
Fredrik Olsson
2009
X
X
X
X
X
Haisheng Yu
2009
X
X
X
X
X
X
X
X
X
Hung-Chi Chang et al
2009
Yugang Yu et al.
2009
X
X
X
X
X
X
X
Chao-Kuei Huang
2010
X
X
X
X
X
X
Christoph H. Glock
2011
X
X
X
X
X
Mohammaditabar, D. et al.
2011
X
X
X
X Inventory Parameter used in a research paper for developing inventory model
IDENTIFICATION OF SELECTION CRITERION
A total of 48 research papers starting from year 1982 to till last year 2011 have been referred for mostly available parameters. 29 criterions have been identified that have been used for selection of inventory policy/model by different researchers. A summary of all parameters has been prepared in Table 1 shows the number of articles which has included various inventory parameters in referred papers/articles. A bar chart that shows how many researchers have used various inventory affecting parameters in % has been prepared and given in figure - 1. About 83 % of papers use the parameter holding cost or carrying cost per unit item. 48% of papers consider Batch Quantity/Lot size/Order Quantity while 45% papers use ordering cost per unit item to develop inventory models.
Besides this criterion some other are also become important which are earlier assumed to be constant and not included by most of the researchers in their proposed models. But in the realistic situation, they also play a vital role. e.g. Wages of store and purchasing staff, Obsolesce, and some others depending upon type of industries and nature of product.
Figure 1 (Bar Chart showing paper using the parameters in %)
PROPOSED METHODOLOGY
Upto mid of this era about 1985, some of the parameters/Selection Criterion are consider for development of models under ideal condition but with the passage of time researchers start including other realistic parameters with reflects practical situation to some extent. In this current scenario, there is a great need to include all the relevant inventory parameters with due weightage which may be vary as per the organization, region, product, inventory system and such others. A number of inventory models are developed or developing stages in the current scenario but still no of them can be proposed for a realistic situation with very high confidence. There is a great need to develop models which may facilitate inventory personnel to optimize the selection of inventory policy especially for small and medium scale enterprises
OBJECTIVE
C1
Cm
C4
C3
C2
C5
M1
M2
M3
Mn
Figure 2 (Proposed Hierarchical Structure of Decision Problem)
An enterprise desires to select a suitable ordering policy. n policies (M1, M2, M3, and Mn) are under consideration to select the most suitable policy based on selection criteria (C1, C2, C3……………….Cm). The hierarchical structure of this decision problem is proposed in Figure 2. The decision-makers use the weight of the criterion to assess the importance of the criteria. Evaluate the ratings of ordering policies with respect to each criterion.
DISCUSSIONS
The literature reveals that there are unresolved problematic issues encountering various inventory models/policies. This paper has investigated lots of existing inventory models, their classification schemes, selection criteria and methodologies for their evaluation and ranking/Rating. The following major inferences can be drawn from the critical review of the available literature:
Researchers have developed great number of models and more models are developed or in developing stage, but no of them can be recommended as the sheer complexity of those models has caused difficulty in implementing them in realistic situation. Some inventory models appear to be better overall than others. Unfortunately, models that are good overall are not always the best choice for a particular data set, and it is not possible to know which model to use a priori. Even when an appropriate model is used, the predictions made by a model may still be less accurate than desired. Due to this reason, a great deal of research has gone into trying to make more effective use of existing models.
Researchers have proposed enormous classification of inventory models but there is a great need of classification of inventory affecting parameters.
Ordering policies consider some of inventory parameters as variable and assume others as constant but in actual practice these parameters behave in different fashion.
The existing ordering policies/models focuses on optimization of inventory model but no model presents guidelines for the selection of inventory models.
Purchasing personnel strongly believe that selection of an inventory model has been for long a formidable task and fraught with uncertainties. There are great variations of models available in literature.
CONCLUSION
In this article we have reviewed more than 50 research articles including various proposed ordering policies.
It is explored from the literature that there is excess of models from which the user can choose the ordering policy to optimize the inventory level.
This thorough study also highlights the limitations of the ordering policies as they incorporate certain assumptions without validations. These assumptions are the common deficiencies of most models due to assumption behave in a different manner whenever apply in actual condition.
Further, the ordering policies are not tested and validated for actual conditions. There is a need to develop a methodology about the selection of ordering policy and how the parameters should be estimated and what techniques should be employed to ensure that traditional ordering polices give the best possible results.
There is a need to develop framework so that ranking of ordering policies can be done for a particular application.
