Deriving the cost function of an integrated dyadic supply chain with uncertainty in the supply (original) (raw)
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International Journal of Supply Chain Management, 2014
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International Journal of Engineering Technologies and Management Research, 2021
This article investigates a single-vendor single-buyer supply chain model where the market demand depends on time as well as selling price and product reliability. The vendor's production rate is not constant but depends on the market demand. The vendor's production process is not perfectly reliable; it may produce some percentage of defective items during a production run. The vendor takes up a lot-forlot policy for delivering the ordered quantity to the buyer who performs 100% screening after receiving each lot. The average total profit of the integrated supply chain is derived and a numerical example is taken to validate the developed model. The optimal results of the proposed model are also discussed for some particular cases. Sensitivity analysis is performed to investigate the influence of key modelparameters on the optimal results.
Modelling and analysis of serial supply chains in uncertain environments
1997
An SC is generally viewed as a network of facilities that performs the procurement of raw materials, their transformation into intermediate and end-products, and distribution and selling of the end-products to customers. The facilities in the SC network include raw material inventories, production facilities, in-process and end-product inventories and distribution facilities that encompass warehouses and retailers. The links in the SC network represent two flows in opposite directions: the flow of orders from customers towards suppliers, and the flow of materials, including raw materials, components, manufacturing parts and packaging materials, which is directed from the suppliers on its complexity and importance (Hicks, 1997a, 1997b, 1997c). Gattorna and Walters (1996) support the opinion that the theory on which SC management relies is the theory of integrated logistics and it has become just a preferred name for the "actualisation of integrated logistics theory". It combines such traditionally distinct activities as forecasting, purchasing, manufacturing, distribution, sales and marketing into one continuous process of interrelated activities. Christopher (1992) considers the Stage 4: external integration Material flow Customer service Customer service Customer service Figure 1.2. Stages in achieving an integrated SC, (Stevens, 1989) 1997). The strategic level considers usually time horizons of several years, while the operational level uses time units of an hour, day or week. The time horizons treated at the tactical level fall between these two time horizons. Each of the questions mentioned above is a complex problem on its own. In addition, there are interactions and inter-dependencies among these problems, that bring even more complexity in SC management and control. 1.2.5. Supply chain dynamics An SC is a dynamic system, and therefore its behaviour and performance have to be considered over time. For example, periodic economic swings are inevitable and However, it must be emphasised that they are complex and difficult problems to solve. 1.2.6. Uncertainty in a supply chain An important and real cause for complexity of SC management and control is the presence of uncertainties within environments in which SCs operate. Three distinct 1.4. Supply chain modelling SC modelling is a complex and challenging problem, supported by a desire to provide appropriate solutions to real-world problems. Main roles of SC models can be summarised as follows (Gattorna and Walters, 1996): (1) to better understand an SC, its structure, activities, inter-dependency between SC constituent parts, interrelationships with its operating environment, (2) to generate potential SC strategies, SC. Customer demand placed on one end of the SC, in combination with production/inventory policy, determine demand at upstream part of the chain. Even when customer demand is modelled by a standard, well-known probability distribution and the SC has a relatively simple structure, using straightforward operating policies still yield demand processes along the chain which are quite difficult to model analytically. In addition, treatment of shortages becomes more complex in an SC than the impact of uncertainties (Lee and Billington, 1992). SC modelling that is carried out in this research is motivated by two basic ideas: (1) including different sources of uncertainty inherent in real SCs and their environment into SC models can contribute to effective SC management and control and to better understanding of SCs and their processes, and (2) representing and treating uncertainty in a more natural way and in a form acceptable by practitioners can bring SC models nearer to real-world problems. The conceptual simplicity of using fuzzy sets to represent uncertainties in customer demand, external supplier reliability, supply along the chain and * lead times is underlined. In Chapter 5, new original SC fuzzy analytical models are presented. Optimal order-up-to levels and replenishment quantities are determined for all inventories in an
The Integrated Deterministic Model for a Vendor Managed Inventory in a Two-Stage Supply Chain
Jurnal Teknologi, 2015
In a two-stage supply chain system, vendor managed inventory (VMI) policies is an integrating decisions between a supplier and his retailers. The supplier assumes the responsibility of maintaining inventory at its retailers and ensuring that they will not run out of stock at any moment. This paper discusses an optimization approach, considering the model of static demand on the inbound as well as the outbound inventory for a two-stage supply chain implementing VMI. In the proposed solutions for coordinating the single-warehouse multiple-retailers (SWMR) system, retailers are first clustered to minimize the within-cluster travel costs and distances and are then replenished using an optimal direct shipping strategy satisfying some additional restrictions.