Published at : 21 Jul 2020
Volume : IJtech Vol 11, No 3 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i3.3750
|Kinley Aritonang||Department of Industrial Engineering Faculty of Industrial Technology, Universitas Katolik Parahyangan|
|Marihot Nainggolan||Department of Industrial Engineering Faculty of Industrial Technology Universitas Katolik Parahyangan|
|Adrianus Vincent Djunaidi||Department of Industrial Engineering Faculty of Industrial Technology Universitas Katolik Parahyangan|
this study, an integrated inventory model was developed among one vendor, multi
buyers, and multi products. The total inventory cost to be minimized in this
model is a combination of the vendor’s and the buyers’ total inventory costs.
The total vendor inventory costs consist of setup costs and holding costs and
the total inventory cost of the buyer consists of ordering costs, holding
costs, stockout costs, and crashing lead time costs where the crashing lead
time cost is approximated with an exponential function. Three decision
variables will be calculated: the number of buyer orders, the lead time of each
buyer, and the frequency of vendor shipments to all buyers in one production
cycle. In this study, the optimal solution of each decision variable has been
developed and applied to a case to show the use of models for finding optimal
solutions. The sensitivity has also been performed to show the effects of some
factors on the decision variables.
Crashing lead time; Integrated inventory model; Inventory management; Multi products; Multi buyers
There are many ways that companies maintain customer trust; one of them is trying to consistently fulfil the customer’s demand. This goal utilizes a supply chain in many fields, such as the Malaysian automotive industry (Ashari et al., 2018), school feeding program in the Philippines (Miro et al., 2018), Malaysian public sector projects (Riazi and Nawi, 2018), etc. Supply chains rely much on inventory management. According to Tersine (1994), important component in inventory management is lead, which usually consists of several components: order preparation, supplier lead time, delivery time, and setup time. In real world conditions, one or more the lead time components can usually be reduced by incurring the additional costs, usually known as the crashing lead time costs (Jha and Shanker, 2009). With the option to reduce it, lead time can also be one of the decision variables in inventory management where a company wants to have a shorter lead time. Therefore, if the company has the option to reduce its lead times, it can minimize the total cost of inventory. Liao and Shyu (1991) consider the possibility of crashing lead time and developed an inventory model that makes the lead time as a decision variable by assuming the number of products ordered is known and the demand follows normal distribution.
The lead time is assumed to consist of n components which each component has the crashing lead time costs. Liao and Shyu’s model has been continued by Ben-Daya and Raouf (1994). Their inventory model made the lead time and number of products ordered decision variables. Ouyang et al. (1996) then developed an inventory model that could reduce the lead times, and considered the occurrence of a stockout with backorder and lost sales at the same time (partial backorder). They also assumed that lead time consists of n independent components. Moon and Choi (1998) developed a crashing lead time inventory model that considered the stockout (partial backorder). They determined that the number of ordered products, lead time, and reorder points were the decision variables. Chang and Chang (2001) developed a crashing lead time inventory model by considering the quantity discount and later developed a crashing lead time inventory model that considered the resources limitations and the seasonal demand (Chen and Chang 2007).
In the supply chain, the increased competition and customer demands make a company continue to improve the efficiency of its operational activities to reduce the costs. This requires a good coordination or integration in its supply chain. In an integrated inventory model, the total cost to be minimized is the combined total cost of several parties involved. Pan and Yang (2002) developed an integrated inventory model between one vendor and one buyer that considered the crashing lead time. Ouyang et al. (2004) developed Pan and Yang’s research by considering the stockout (fully backorder). Yang and Pan (2004) again developed an integrated inventory model between one vendor and one buyer that considered crashing lead time and the investment cost for quality improvement.
Jha and Shanker (2009) developed an inventory model that integrates one vendor and one buyer. In the model, there is one additional decision variable that must be determined: the number or frequency of delivery from vendor to buyer in one production cycle. They assumed that the vendor uses a batch production system, while the buyer uses a continuous review policy or inventory system of method Q. Jha and Shanker (2013) further developed an inventory model that integrates one vendor and several buyers, considering the possibility of reducing lead times accompanied by the service level limits. However, this model has never been developed into a multi-product inventory model. Zhu and Xu (2012) developed the optimized design of a closed-loop supply chain on an uncertain environment where dynamic state of location, facility extension, and capacity improvement were considered. Vijayashree and Uthayakumar (2016) also developed a model of inventory with the possibility to reduce the lead time. The model did not consider the stockout costs and assumed that the cost of crashing lead time is approximated by an exponential function with the maximum value of LE and can be reduced to a minimum value of LS. By reducing the lead time, a company can reduce the amount of safety stock, reduce the possibility of stockouts, and increase the service levels. Then, they improved the model by considering three lead time crashing cost functions (2017). An integrated inventory model finds the optimal solutions of order quantity, lead time, total cost for the buyer, total cost for the vendor, and the total number of deliveries. These two papers present an integrated single vendor and single buyer inventory model to minimize the sum of the cost of ordering, setup, holding, and crashing by simultaneously optimizing the optimal order quantity, lead time, and number of deliveries. The main contribution of these two proposed models is to find and minimize the integrated total cost for the single vendor and single buyer. The study of a single vendor–buyer model with stochastic demand and transportation cost was also investigated by Ivan and Hui (2018). In this study, the transportation cost was a function of shipping weight, distance, and transportation modes. A heuristic model was developed to minimize the integrated total relevant cost, and the decision variables were the buyer’s order quantity and the vendor’s production quantity per cycle. They then considered defective items and error in inspection (2019). The results showed that lead-time can be shortened with smaller production lot sizes. Mofokeng and Chinomona (2019) found that partnership, collaboration, and integration can generally influence the performance of supply chain, particularly within the small and medium enterprise (SME). The research explained some general factors that affect the supply chain performance.
No single study takes into full consideration the factors of one vendor, multi buyers, multi products, crashing lead-time, and stockout. In this study, the continuation of an integrated inventory model has been carried out by considering all these factors. The total inventory cost, as the supply chain performance and to be minimized in this model, is a combination of the vendor’s and the buyer’s total inventory costs.
The following are the
conclusions of the study: (1) The equation of the total inventory cost between
a vendor and many buyers and products, the existence of a stock out, and the
possibility for buyers to reduce the lead time has been modeled; (2) The total
order of all buyers that can minimize the total inventory cost between vendor
and buyers has been derived; (3) Lead time that can minimize the total
inventory cost between vendor and buyers by considering the possibility for
buyers to reduce their lead time was derived; (4) The frequency of shipping in
a production cycle that can minimize the total inventory cost between vendors
and buyers has also been derived costly, and it does not affect the interior
spaces. Also, this method can be applied to existing buildings which reduces
the need for mechanical heating or cooling systems and associated costs. Last,
considering the quality of materials and their associated infiltration rates,
designers can draft intelligent façades to increase the energy efficiency of
new and existing buildings by adapting the geometries to account for wind
pressure and direction.
The authors would like to thank to Department of
Industrial Engineering, Faculty of Industrial Technology - Universitas Katolik
Parahyangan, Bandung for all supports.
|R1-IE-3750-20200531210100.jpg||Research Model in Jpeg|
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