Research Contributions

Research on the integration o f inventory and transportation management through the Inventory Routing Problem approach has been undertaken previously to solve various dimensions o f the problem using several mathematical modelling methods. However, existing replenishment policies in previous studies mostly view the IRP as an extension o f the vehicle routing problem. Therefore, the solution method is more towards solving solely based on routing that satisfies the constraints and performance measurement. Only a small number o f studies have considered including inventory policy solution methods such as (5, S), EOQ and zero ordering policy. However, few studies have explored the flexibility offered by the IRP approach to assist the central decision-maker in balancing the inventory cost and the transportation cost. Further, the optimal route decision is commonly based on distance travelled or just used a static route for each replenishm ent period. Also, few studies have taken into account the vehicle efficiency factor in making the decision. Thus, this thesis offers a number of contributions to the literature since it fills identified gaps through a number o f analyses in Chapter 4 through to Chapter 6.

In general, this thesis has presented an extensive numerical study which has quantified

terms o f operating costs and the vehicle effectiveness along with a dynamic routing strategy with regard to vehicles’ energy consumption in making the replenishment.

Moreover, this study provides insights into the application o f the IRP approach as a potential business process reengineering solution in the healthcare industry, specifically in the context o f M alaysia’s private healthcare industry. The problem o f replenishing m ultiple-retailers who face a stochastic demand based on the case study organization has been simplified and studied via simulation in order to evaluate the effect o f flexibility to make early replenishments on transportation and inventory costs.

Such flexibility is im plem ented in the model based on the periodic (s,c,S) inventory policy. Accordingly, this study contributes to the literature by widening the application o f well-known jo in t replenishment approaches to periodic scenarios with multi-retailers and a single item. However, the parameters that trigger the replenishment have been slightly modified to fit implementation in the IRP scenario where the responsibility for making decisions on the time and the delivery quantity is that o f the supplier, not the retailer. The flexibility o f scheduling an early replenishment to consolidate replenishments with other retailers is quantified by the

“can-deliver” level value.

The first part o f the analysis in Chapter 5 presents an extensive numerical study o f the effect o f inventory control parameter settings on the trade-off between inventory holding cost, inventory shortage cost, and transportation cost. This is a further contribution o f the study since investigation o f replenishment flexibility obtained from (s,c,S) policy w ith a wide range o f the flexible parameter (“can-deliver” level, c) has not been carried out before in solving the multiple-retailer scenario. Another contribution o f the study is that the model also considered shortage cost per unit shortage supply in the objective function where the researchers mostly use the delivery cost as the shortage cost in the model. The findings show that by having another indicator that triggers an early replenishment before the inventory level reaches the reorder level is beneficial for reducing the transportation cost and overcoming the out o f stock problem. The result also shows that the early replenishment strategy does not significantly influence the inventory holding cost at

the retailers. Hence, the study indicates that the periodic “can-deliver” policy provides a significant cost saving and outperforms the (s,S) policy.

The study continued w ith an examination o f the effect o f flexibility with regard to vehicle effectiveness and total cost by modifying the routing strategy and taking into account distance and load factors to decide the dynamic replenishment sequence o f retailers during the delivery trip in Chapter 6. The analysis was performed by investigating vehicle perform ance and route selection based on the Travelling Salesman Problem (TSP), and Overall Vehicle Effectiveness (OVE) and Modified Overall Vehicle Effectiveness (MOVE) metrics. Another contribution o f the study was its identification o f the relationship between Key Performance Indicator (KPIs) used to evaluate vehicle performance and single OVE and MOVE performance metrics. This relationship had previously been questioned by the Freight Logistic Research Group in 2004.

Incorporation o f the OVE and M OVE performance measurements in the IRP model to examine total vehicle effectiveness and the impact o f vehicle energy consumption with regards to the route selection during the replenishment period is a further contribution o f the study to the existing body o f literature. In addition, as far as the researcher is aware, this is the first study to explore the effective replenishment decision incorporating inventory control and vehicle effectiveness strategy that consider both economic and environmental factors in the decision. Thus, a new transportation cost function is developed, which includes fixed delivery cost and variable transportation costs related to the weight-distance and number o f retailers visited during the replenishment. The analysis o f the comparison between the MOVE and OVE metric and the TSP approach in terms o f the optimal inventory control parameters and total cost provides further insight into the effect o f different routing strategies incorporated in the IRP model.

In addition, this study also contributed to research methodology since comprehensive analyses were perform ed to determine an appropriate simulation tool to conduct the study as well as identify a warm-up period and the number o f replications required to obtain an accurate result for the simulation analysis. Further, the normality test was

that the observations were normally distributed. This is important when computing the half-width confidence interval to determine the appropriate number o f replications using Student’s t distribution.