Integrated Production-Inventory Supply Chain Model
Read this article. An integrated production-inventory model is constructed to address supplier, manufacturer, and retailer uncertainties. According to the author, what are the three types of uncertainties in supply chain management?
Introduction
Supply chain management has taken a very important
and critical role for any company in the increasing globalization and
competition in the market. A supply chain model (SCM) is a network of
suppliers, producers, distributors, and customers which synchronizes a
series of interrelated business process in order to have (1) optimal
procurement of raw materials from nature, (2) transportation of raw
materials into a warehouse, (3) production of goods in the production
center, and (4) distribution of these finished goods to retailers for
sale to the customers. With a recent paradigm shift to the supply chain
(SC), the ultimate success of a firm may depend on its ability to link
supply chain members seamlessly.
One of the earliest efforts to
create an integrated SCM has been developed by Oliver and Webber,
Cohen and Baghanan, and Cachon and Zipkin. They developed a
production, distribution, and inventory (PDI) planning system that
integrated three supply chain segments comprising supply,
storage/location, and customer demand planning. The core of the PDI
system was a network model and diagram that increased the decision
maker's insights into supply chain connectivity. The model however was
confined to a single-period and single-objective problem. Viswanathan
and Piplani were concerned an integrated inventory model through
common replenishment in the SC. Khouja was the first to consider a
three-stage supply chain with one or more firms at each stage. Agarwal
et al. have developed a dynamic balancing of inventory model in
supply chain management. Rau et al. developed an integrated SCM of a
deteriorating item with shortages. Lee added a new dimension to the
single vendor-single buyer problem by setting the number of raw
material shipments received by the vendor per cycle to be a decision
variable. Ben-Daya et al. have developed an integrated
production-inventory model with raw material replenishment
considerations in a three-layer supply chain. Sana has integrated a
production-inventory model of imperfect quality products in a
three-layer supply chain. Recently, Pal et al. have developed a
three-layer supply chain model with production-inventory model for
reworkable items. All of the abovementioned SCMs are considered with
constant, known demand and production rates in a crisp environment.
Different
types of uncertainty such as fuzziness, randomness, and roughness are
common factors in SCM. In many cases, it is found that some inventory
parameters involve fuzzy uncertainty. For example, inventory-related
costs such as holding cost and setup cost, demand, and selling price
depend on several factors such as bank interest, stock amount, and
market situation which are uncertain in a fuzzy sense. To be more
specific, inventory holding cost is sometimes represented by a fuzzy
number, and it depends on the storage amount which may be imprecise and
range within an interval due to several factors such as scarcity of
storage space, market fluctuation, human estimation, and/or thought
process. The following papers have been developed in these environments.
Wang
and Shu developed a fuzzy decision methodology that provides an
alternative framework to handle SC uncertainties and to determine SC
inventory strategies, while there is a lack of certainty in data or even
a lack of available historical data. Fuzzy set theory is used to model
SC uncertainty. A fuzzy SC model based on possibility theory is
developed to evaluate SC performances. Based on the proposed fuzzy SC
model, a genetic algorithm approach is developed to determine the
order-up-to levels of stock-keeping units in the SC to minimize SC
inventory cost subject to the restriction of fulfilling the target fill
rate of the finished product. The proposed model allows decision makers
to express their risk attitudes and to analyze the trade-off between
customer service level and inventory investment in the SC, so that
better SC inventory strategies can be made.
Das et al. have
presented a joint performance of an SC with two warehouse facilities in a
fuzzy environment. A realistic two-warehouse and
multi-collection-production-inventory model with
constant/stock-dependent demand, defective production system, and fuzzy
budget constraint has been formulated and solved in an SC context. Later
Chen et al. developed a multi-criteria fuzzy optimization for
locating warehouses and distribution centers in a supply chain network.
Peidro
et al. developed a fuzzy linear programming model for tactical
supply chain planning in a multi-echelon, multi-product, multi-level,
multi-period supply chain network in a fuzzy environment. In this
approach, the demand, process, and supply uncertainties are jointly
considered. The aim is to centralize multi-node decisions simultaneously
to achieve the best use of the available resources along the time
horizon so that customer demands are met at a minimum cost. This
proposal is tested using data from a real automobile SC. The fuzzy model
provides the decision maker with alternative decision plans with
different degrees of satisfaction.
Chu developed the supply
chain flexibility that has become increasingly important. This study
thus builds a group decision-making structure model of flexibility in
supply chain management development. Recently, Jana et al. have
developed a fuzzy simulation via contractive mapping genetic algorithm
approach to an imprecise production-inventory model under volume
flexibility. This study presents a framework to evaluate the supply
chain flexibility that comprises two parts: (1) an evaluation hierarchy
with flexibility dimensions and related metrics and (2) an evaluation
scheme that uses a three-stage process to evaluate the supply chain
flexibility. This study then proposes an algorithm to determine the
degree of supply chain flexibility using a fuzzy linguistic approach.
Evaluations of the degree of supply chain flexibility can identify the
need to improve supply chain flexibility and identify specific
dimensions of supply chain flexibility as the best directions for
improvement. The results of this study are more objective and unbiased
for two reasons. First, the results are generated by group
decision-making with interactive consensus analysis. Second, the fuzzy
linguistic approach used in this study has more advantages to preserve
no loss of information than other methods. Additionally, this study
presents an example using a case study to illustrate the availability of
the proposed methods and compare it with other methods.
Kristianto
et al. developed an adaptive fuzzy control application to support a
vendor-managed inventory (VMI). This paper also guides the management
in allocating inventory by coordinating with suppliers and buyers to
ensure minimum inventory levels across a supply chain. Adaptive fuzzy
VMI control is the main contribution of this paper.
However, the
uncertainty theory was developed by Liu, and it can be used to
handle subjective imprecise quantity. Much research work has been done
on the development of the uncertainty theory and related theoretical
work. You proved some convergence theorems of uncertain sequences.
Liu has defined uncertain process and Liu has discussed
uncertain theory. In this paper, we developed for the first time a
three-layer supply chain model under delay in payment in an uncertain
environment.
In the traditional economic order quantity (EOQ)
model, it often assumed that the retailer must pay off as soon as the
items are received. In fact, the supplier offers the retailer a delay
period, known as trade credit period, in paying for the purchasing cost,
which is a very common business practice. Suppliers often offer trade
credit as a marketing strategy to increase sales and reduce on-hand
stock level. Once a trade credit has been offered, the amount of the
tied up retailer's capital in stock is reduced, and that leads to a
reduction in the retailer's holding cost of finance. In addition, during
the trade credit period, the retailer can accumulate revenues by
selling items and by earning interests. As a matter of fact, retailers,
especially of small businesses which tend to have a limited number of
financing opportunities, rely on trade credit as a source of short-term
funds. In this research field, Goyal was the first to establish an
EOQ model with a constant demand rate under the condition of permissible
delay in payments. Khanra, Ghosh, and Chaudhuri have developed an
EOQ model for a deteriorating item with time-dependent quadratic demand
under permissible delay in payment. Also, Maihami and Abadi have
established joint control of inventory and its pricing for
non-instantaneously deteriorating items under permissible delay in
payments and partial backlogging.
The proposed model considers a
three-layer supply chain involving the supplier, manufacturer, and
retailer who are responsible in transforming the raw materials into
finished product and making them available to satisfy the customer's
demand time. Inventory and production decisions are made at the
supplier, manufacturer, and retailer levels in uncertain environments.
The problem is to coordinate production and inventory decisions across
the supply chain so that the total profit of the chain is maximized.