Production Planning and Inventory
Control in Automotive Supply Chain Networks
Introduction
In
today's global market, supply chain management (SCM) has provided several ad-vantages for companies’ strategy in order to improve
their competitiveness. Study on supply chain (SC) behavior is valuable for
understanding causal effects and vari-ous or even extreme scenarios. Inventory
control is one of the vital aspects in compa-nies supply chain network (SCN) in
order to promote their efficiency. Since the flow of inventories is at the
heart of each company,the role of inventory controlling tools is unavoidable in
every SCN. Just-in-Time (JIT), Material Requirements Planning (MRP) and its
modified version Manufacturing Resource Planning (MRPII) are well-known and the
most powerful controlling tools that have a substantial effect upon the failure
or success of an entire manufacturing system [2]. In this regard, the important
characteristics of material flow are directed toward planning and controlling
by MRP and JIT.
Previous
studies have been demonstrated the solely implementation of JIT or MRPII cannot
reduce deliveries costs [1]. Specially, when suppliers are located al-most
faraway from manufacturers plant, raw materials and parts should be delivered on
time to manufacturer. To do so, in our real world automotive manufacturer case study,
we propose a non-linear mathematical model in a hybrid MRPII/JIT manufac-turing
system to overcome this issue. In the first level of the proposed model, Eco-nomic
Orders Quantity (EOQ) for raw materials and parts are calculated based on JIT philosophy
with respect to numbers of pre-determined suppliers. The objective of the second
level is to reduce the delivery tardiness by considering the optimized delivery
time, which determined by customers.
In
the most basic definition, perhaps the central notion of mutual exclusivity of
MRP and JIT systems is “push” and “pull” terminology, which used to interpret
the control strategy of information and material flow [3]. MRP is a
manufacturing plan-ning and control strategy that determines a schedule for the
deterministic demand items from the stochastic demand within the BOM explosion
process, netting and offsetting [4]. Whilst, JIT production is relatively
younger in terms of use and simply means produc-ing with the right amount of
material at the right place and at the right time. However, in JIT implementation,
there is a lack of the support of a standardized software package due to its
initial detachment from information technologies [3].
Performance
comparison of different control techniques is provided by complex mathematical
analysis and simulation tools in manufacturing environments. This means are
able to compare manufacturing system performance ranging from simpler systems
with few processes to more complex manufacturing environments such as multistage
production environments with multiple lines as well as parallel configura-tions.
Optimization of these manufacturing environments out-performs simpler con-trol
principles in the various dimensions of inventory performance and service
level.
An
optimization model of a horizontally integrated push–pull hybrid production system
in a serial production line constructed by Cochran [7]. In this study,
pull-push control to production systems were found more efficient compare with
all-push and all-pull systems. Furthermore, Salum [8] study the application of
push–pull control to production systems by comparing the dual-resource
constrained/push–pull controlled system with the dual-resource
constrained/Kanban system. Hodgson [9] applied the Markov Decision Process
(MDP) as the alternative for production system in a push–pull multistage
production/inventory system in his study. Literatures in push-pull control underlie
various case studies such as telecommunication industry [10] and semiconductor
industry [11].
The
problem material delivery tardiness scheduling without engaging suppliers into
consideration in hybrid MRP/JIT has been studied by many studies [4-11]. Deli-very
tardiness is a significant issue since it may lead to impose penalties for
manufac-turers. In this study, a mathematical model based on tardiness control
at supplier is developed for minimizing delivery tardiness and delivery costs
as well. This model can be solved by a commercial solver to find local
optimums. We use Matlab 2011 to run the proposed model.
The
objectives of MRP and JIT integration in the supply chain were explained in Section
2. In this study, our objective is to find the minimum values for the delivery due
date of raw materials and semi-finish parts. Also, this model focuses on
minimiz-ing late delivery cost, effective JIT implementation, Kanban and
planning on the MRPII information feedback. Moreover, it may have an impact on
the bullwhip ef-fects and relevant indirect delivery costs. The proposed model
includes three nonli-near pro-gramming models that are applied in two stages of
the whole supply chain (supplier and manufacturer) based on two types of piston
in a spare parts automotive manufacturer.
Results and Discussion
To
solve this problem, equations 1-4 calculate the value of current system
parameters. Then, the optimum values of these parameters are obtained from
equations 5-9. In the case study under consideration the amount of annual
production for piston set (A) and piston set (B) are 2000 and 3000
respectively. Annual Re-Order Point (ROP) is an-nounced 3-4 months before
planning horizon. Table 1 shows the collected data for the corresponding case
study. Also, the developed framework in figure 2 summarizes the require steps
for solving this problem.
Conclusion
The proposed model optimizes due date delivery with respect
to suppliers’ perfor-mance index which can aid suppliers to better adjust
production planning and order-ing. Furthermore, the model enables manufacturers
to respond quickly to customer requirements, enhance productivity, reduce
material levels, reduce tardiness, holding cost reductions, leading to the
potential benefit of long term contracts with suppliers.
Ashkan Memari, Abdul Rahman Bin
Abdul Rahim, and Robiah Binti Ahmad
Faculty of Mechanical Engineering,
Universiti Teknologi Malaysia
81310 Skudai, Johor Bahru, Malaysia
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