Optimizing costs for vaccine control using the reorder point approach

Vaccines are biological products that have an important role in human immunity. In Indonesia, some vaccines are categorized as compulsory vaccines and additional vaccines. The demand for additional vaccines is less predictable because they are not mandatory for use. This of course makes the amount of demand for vaccines less predictable. Also, the price of additional vaccines is not cheap when compared to the price of mandatory vaccines. So that the management of vaccines in the pharmacy warehouse is needed so that the amount of supply and demand is balanced so that the costs incurred will be more optimal. The information system regarding vaccine reordering is carried out using a reorder point so that the pharmacy warehouse can order according to the right need and at the right time.  The data used are demand data, prices, storage costs, and message costs. The results of calculations using reorder points within four months with a total purchase for the Rotavirus vaccine was 62 for IDR 28,274,948 and 70 for the hospital of IDR 31,801,500 with a difference of IDR 3,528,552. The calculation result using the reorder point for the Hexaxim vaccine with a total purchase for 4 months was 61 with a nominal value of IDR 58,380,060 while the calculation in the hospital was 67 with a nominal value of IDR 63,971,000 so that a nominal difference of IDR 5,590,940 was obtained.  Use of the return point can be used to alarm when and how many vaccines to order. This can be seen from the cost difference between the pharmacy warehouse and the calculation using the reorder point for the Hexaxim vaccine and the Rotavirus vaccine

On the Development of a Multilayered Agent-based Heuristic System for Vehicle Routing Problem under Random Vehicle Breakdown

With the recent technological advancement, the Dynamic Vehicle Routing Problem (DVRP) is becoming more applicable but almost all of the research in this field limited the source of dynamism from the order side rather from the vehicle, in addition to the adoption of inflexible tools that are mainly designed for the static problem. Considering multiple random vehicle breakdowns complicates the problem of how to adapt and distribute the workload to other functioning vehicles. In this ongoing PhD research, a proposed multi-layered Agent-Based Model (ABM) along with a modelling framework on how to deal with such disruptive events in a reactive continuous manner. The model is partially constructed and experimented, with a developed clustering rule, on two randomly generated scenario for the purpose of validation. The rule achieved good order allocation to vehicles and reacted to different problem sizes by rejecting orders that are over the model capacity. This shows a promising path in fully adopting the ABM model in this dynamic problem

The impact of City Logistics on Retailers inventory management: an exploratory analysis

City Logistics (CL) can be defined as a comprehensive solution aimed at “totally optimizing” the logistics and transport activities in urban areas, by considering the environment, the congestion and energy consumption. Many CL initiatives have been implemented to improve the efficiency and the effectiveness of the urban logistics processes. The adoption of such initiatives by logistics service providers implies a reshaping of supply chains configuration in terms of vehicles used as well as consolidation and reception of goods. Therefore, CL initiatives are likely to have an impact on the inventory policy of the retailers, in terms of order frequency, time windows for receiving the deliveries, and batching lots. In this context, there is a lack of studies investigating the influence of CL on retailers’ inventory management practices. In order to bridge this research gap, this paper proposes an exploratory analysis of the perception of the CL issues by apparel and grocery store managers and owners. To this end, a survey is submitted to retailers of different sizes and type (e.g. multi-brand vs. mono-brand) located in the limited traffic zone (LTZ) of Turin (Italy). The objective of this analysis is twofold. First, the survey aims at confirming the findings from inventory policy literature and outlining different profiles of retailers based on the factors that characterize their inventory policy. Second, the shopkeepers’ perception, both positive and negative, of three different CL innovations is explored. Results show that there is a wide variety of inventory management practices even within an enclosed environment such as the one of a city’s LTZ, and that the adoption of CL innovations by retailers might depend strongly on their inventory policy. Therefore, logistics service providers and local administrations need to take into account such diversity if they intend to scale up CL innovations

Fit evaluation of virtual garment try-on by learning from digital pressure data

Presently, garment fit evaluation mainly focuses on real try-on, and rarely deals with virtual try-on. With the rapid development of E-commerce, there is a profound growth of garment purchases through the internet. In this context, fit evaluation of virtual garment try-on is vital in the clothing industry. In this paper, we propose a Naive Bayes-based model to evaluate garment fit. The inputs of the proposed model are digital clothing pressures of different body parts, generated from a 3D garment CAD software; while the output is the predicted result of garment fit (fit or unfit). To construct and train the proposed model, data on digital clothing pressures and garment real fit was collected for input and output learning data respectively. By learning from these data, our proposed model can predict garment fit rapidly and automatically without any real try-on; therefore, it can be applied to remote garment fit evaluation in the context of e-shopping. Finally, the effectiveness of our proposed method was validated using a set of test samples. Test results showed that digital clothing pressure is a better index than ease allowance to evaluate garment fit, and machine learning-based garment fit evaluation methods have higher prediction accuracies

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches

A Design and Development of Decision Support System for Thailand Garment Industry

ได้รับทุนอุดหนุนการวิจัยจากสำนักงานคณะกรรมการวิจัยแห่งชาติ ปีงบประมาณ พ.ศ.2553-255

E-grocery challenges and remedies: Global market leaders perspective

The purpose of the study is to identify logistic elements germane to e-grocery businesses, and to reveal the challenges collateral with each logistic element. Further, it strives to create a better understanding of specific remedies that have been employed by top e-grocery retailers to overcome existing challenges while aligning identified challenges with Turban’s framework. Extensive semi-structured interviews were conducted with management staff in three of the top ten global online grocery retailers and another that was a market leader in a European country. The qualitative data collected was transcribed and coded using a non-hierarchical axial coding to identify emerging themes in content analysis. The results expose a range of challenges that could be compartmentalised into three broad categories, in harmony with the different stages of the order fulfilment process. Interestingly, the study found that most challenges were operational rather than tactical or strategic in nature. While the study expands existing knowledge, its revelation that most challenges lie in the management of roles and responsibilities domain is instructive. This makes it imperative for practitioners to focus on this specific area if meaningful improvement in e-grocery retailing performance is to be realised. This research offers a systematic understanding of supply and distribution challenges, including remedies utilised to ameliorate the effect of the challenges from the perspectives of the top companies in the industry. These remedies can be invaluable for existing and emerging e-grocers

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches