The integrated deterministic model for a vendor manage inventory in a two-stage supply chain

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 restriction

On the inventory routing problem with stationary stochastic demand rate

One of the most significant paradigm shifts of present business management is that individual businesses no longer participate as solely independent entities, but rather as supply chains (Lambert and Cooper, 2000). Therefore, the management of multiple relationships across the supply chain such as flow of materials, information, and finances is being referred to as supply chain management (SCM). SCM involves coordinating and integrating these multiple relationships within and among companies, so that it can improve the global performance of the supply chain. In this dissertation, we discuss the issue of integrating the two processes in the supply chain related, respectively, to inventory management and routing policies. The challenging problem of coordinating the inventory management and transportation planning decisions in the same time, is known as the inventory routing problem (IRP). The IRP is one of the challenging optimization problems in logis-tics and supply chain management. It aims at optimally integrating inventory control and vehicle routing operations in a supply network. In general, IRP arises as an underlying optimization problem in situations involving simultaneous optimization of inventory and distribution decisions. Its main goal is to determine an optimal distribution policy, consisting of a set of vehicle routes, delivery quantities and delivery times that minimizes the total inventory holding and transportation costs. This is a typical logistical optimization problem that arises in supply chains implementing a vendor managed inventory (VMI) policy. VMI is an agreement between a supplier and his regular retailers according to which retailers agree to the alternative that the supplier decides the timing and size of the deliveries. This agreement grants the supplier the full authority to manage inventories at his retailers'. This allows the supplier to act proactively and take responsibility for the inventory management of his regular retailers, instead of reacting to the orders placed by these retailers. In practice, implementing policies such as VMI has proven to considerably improve the overall performance of the supply network, see for example Lee and Seungjin (2008), Andersson et al. (2010) and Coelho et al. (2014). This dissertation focuses mainly on the single-warehouse, multiple-retailer (SWMR) system, in which a supplier serves a set of retailers from a single warehouse. In the first situation, we assume that all retailers face a deterministic, constant demand rate and in the second condition, we assume that all retailers consume the product at a stochastic stationary rate. The primary objective is to decide when and how many units to be delivered from the supplier to the warehouse and from the warehouse to retailers so as to minimize total transportation and inventory holding costs over the finite horizon without any shortages

An algorithm model for solving the single-period inventory transportation problems in the construction industry

Vendor managed inventory (VMI) is an illustration of effective partnering and collaboration practices between upstream and downstream points in a supply chain. VMI policy is an integrating decision between a supplier and the customers in which the supplier takes the accountability of sustaining the customers’ inventory while confirming that no stock-out. The supplier indicates when each delivery time takes place, so that the customers are no longer responses to the customers' orders. Under the VMI system, the planning is proactive as it is based on the available information rather than reactive to customers' orders. Consequently, in this paper, we expected that the demand at the construction sites are constant and stationary, and the construction consolidation centre (CCC) is implementing a VMI system. The concentration of this paper is to minimize the transportation and inventory holding costs of the customers for a two-stage supply chain system in the construction industry. The problem is to identify what is the delivery quantities to the construction sites, what is delivery times and which routes should be used to deliver products to the customers at the construction site for the single-period deterministic inventory routing problem (SP-DIRP) in the construction sector. Furthermore, realistic side-constraints such as driving time restrictions, storage capacities constraints and constant replacement intervals are considered. Results of a simplified real-life case implementing the proposed linear mixed-integer program are shown and discussed in detail

Deterministic Inventory Routing Problem (DIRP): a literature review

Vendor managed inventory (VMI) is a management policy in which the supplier implements the responsibility of maintaining the inventory at the retailers to confirm that they will not run out of stock. Under the VMI policy, the supplier takes responsibility for managing the retailers. Moreover, delivery times to the retailers are no longer set by responding to retailers' orders, instead the supplier establishes when each delivery takes place. Therefore, this study reviewed the main publications on single item (vehicle) related to deterministic inventory routing problem (DIRP). Under this approach, the order delivery is in congruence with deterministic demand rates. The objective of DIRP is to resolve a flow strategy that can minimise operational and inventory holding costs without having a stock-out according to a retailer planned schedule.In this study, the focus in on the single-warehouse, multipleretailer vendor managed inventory (SWMR-VMI), where all retailers face a deterministic and constant demand rate

Considering inventory distributions in a stochastic periodic inventory routing system

Dealing with the stochasticity of parameters is one of the critical issues in business and industry nowadays. Supply chain planners have difficulties in forecasting stochastic parameters of a distribution system. Demand rates of customers during their lead time are one of these parameters. In addition, holding a huge level of inventory at the retailers is costly and inefficient. To cover the uncertainty of forecasting demand rates, researchers have proposed the usage of safety stock to avoid stock-out. However, finding the precise level of safety stock depends on forecasting the statistical distribution of demand rates and their variations in different settings among the planning horizon. In this paper the demand rate distributions and its parameters are taken into account for each time period in a stochastic periodic IRP. An analysis of the achieved statistical distribution of the inventory and safety stock level is provided to measure the effects of input parameters on the output indicators. Different values for coefficient of variation are applied to the customers’ demand rate in the optimization model. The outcome of the deterministic equivalent model of SPIRP is simulated in form of an illustrative case

A statistical comparison of two safety stock replenishment mechanisms in a cyclic stochastic IRP

Preventing stock-out in a replenishment system, in which customer demand rates are stochastic with constant averages, can be accomplished via safety stocks hold at each customer. These safety stocks should be replenished back to their initial levels each time they are used. However, sometimes it occurs that a truck does not have enough capacity to carry the amount of the product the visited customers need to restore their stocks and safety stocks to their required levels. Therefore, the carried extra amount of the product, intended to replenish safety stocks, should be divided amongst the customers in some optimal or fair manner. To achieve this fair allocation, we propose and analyze two policies, the first called Fair-share and the second Ratio methods. Details on how these two methods are implemented, to achieve the level of service expected at each customer, are discussed and illustrated. In addition a simulation model is developed and used to compare the performance of each policy in the long run

A Modelling of Genetic Algorithm for Inventory Routing Problem Simulation Optimisation

This paper presents the simulation optimization modelling for Inventory Routing Problem (IRP) using Genetic Algorithm method. The IRP simulation model is based on the stochastic periodic Can-Deliver policy that allows early replenishment for the retailers who have reached the can-deliver level and consolidates the delivery with other retailers that have reached or fallen below the must-deliver level. The Genetic Algorithm is integrated into the IRP simulation model as optimizer in effort to determine the optimal inventory control parameters that minimized the total cost. This study implemented a Taguchi Method for the experimental design to evaluate the GA performance for different combination of population and mutation rate and to determine the best parameters setting for GA with respect to the computational time and best generation number on determining the optimal inventory control. The result shows that the variations of the mutation rate parameter significantly affect the performance of IRP model compared to population size at 95% confidence level. The implementation of elite preservation during the mutation stage is able to improve the performance of GA by keeping the best solution and used for generating the next population. The results indicated that the best generation number is obtained at GA configuration settings on large population sizes (100) with low mutation rates(0.08). The study also affirms the premature convergence problem faced in GA that required improvement by integrating with the neighbourhood search approach

Analysing the effectiveness of vendor-managed inventory in a single-warehouse, multiple-retailer system

This paper considers a two-stage supply chain, consisting of a single warehouse and multiple retailers facing deterministic demands, under a vendor-managed inventory (VMI) policy.It presents a two-phase optimisation approach for coordinating the shipments in this VMI system.The first phase uses direct shipping from the supplier to all retailers to minimise the overall inventory costs.Then, in the second phase, the retailers are clustered using a construction heuristic in order to optimise the transportation costs while satisfying some additional restrictions.The improvement of the system's performance through coordinated VMI replenishments against the system with direct shipping only is shown and discussed in the comparative analysis section

Essays in Measuring, Controlling, and Coordinating Supply Chain Inventory and Transportation Operations

Supply chain collaboration programs, such as continuous replenishment program (CRP), is among the most popular supply chain management practices. CRP is an arrangement between two partners in a supply chain to share information on a regular basis for lowering logistics costs while maintaining or increasing service levels. CRP shifts the replenishment responsibility to the upstream partner to avoid the bullwhip effect across the supply chain. This dissertation aims to quantify, measure, and expand the benefits of CRP for the purpose of reducing logistics cost and improving customer service. The developed models in this dissertation are all applied in different case studies supported by a group of major healthcare partners. The first research contribution, discussed in chapter 2, is a comprehensive data-driven cost approximation model that quantifies the benefits of CRP for both partners under three cost components of inventory holding, transportation and ordering processing without imposing assumptions that normally do not hold in practice. The second contribution, discussed in chapter 3, is development of a verifiable efficiency measurement system to ensure the benefits of CRP for all partners. Multi-functional efficiency metrics are designed to capture the trade-off in gaining efficiency between multiple functions of logistics (i.e. inventory efficiency, transportation efficiency, and order processing efficiency). In addition, a statistical process control (SPC) system is developed to monitor the metrics over time. We discuss suitable SPC systems for various time series behaviors of the metrics. The third contribution of the dissertation, discussed in chapter 4, is development of a multi-objective decision analysis (MODA) model for multi-stop truckload (MSTL) planning. MSTL is becoming increasing popular among shippers while is experiencing significant resistance from carriers. MSTL is capable of reducing the shipping cost of shippers substantially but it can also disrupt carriers’ operations. A MODA model is developed for this problem to incorporate the key decision criteria of both sides for identifying the most desirable multi-stop routes from the perspective both decision makers