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

Dynamic pricing models for electronic business

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing

Multi-objective optimisation using agent-based modelling

ENGLISH ABSTRACT: It is very seldom that a decision-making problem concerns only a single value or objective. The process of simultaneously optimising two or more con icting objectives is known as multi-objective optimisation (MOO). A number of metaheuristics have been successfully adapted for MOO. The aim of this study was to investigate the feasibility of applying an agent-based modelling approach to MOO. The (s; S) inventory problem was chosen as the application eld for this approach and Anylogic used as model platform. Agents in the model were responsible for inventory and sales management, and had to negotiate with each other in order to nd optimal reorder strategies. The introduction of concepts such as agent satisfaction indexes, aggression factors, and recollection ability guided the negotiation process between the agents. The results revealed that the agents had the ability to nd good strategies. The Pareto front generated from their proposed strategies was a good approximation to the known front. The approach was also successfully applied to a recognised MOO test problem proving that it has the potential to solve a variety of MOO problems. Future research could focus on further developing this approach for more practical applications such as complex supply chain systems, nancial models, risk analysis and economics.AFRIKAANSE OPSOMMING: Daar is weinig besluitnemingsprobleme waar slegs 'n enkele waarde of doelwit ter sprake is. Die proses waar twee of meer doelwitte, wat in konflik staan met mekaar, gelyktydig optimiseer word, staan bekend as multi-doelwit optimisering (MOO). 'n Aantal metaheuristieke is al suksesvol aangepas vir MOO. Die doelwit van hierdie studie was om ondersoek in te stel na die lewensvatbaarheid van die toepassing van 'n agent gebasseerde modelerings benadering tot MOO. As toepassingsveld vir hierdie benadering was die (s; S) voorraad probleem gekies en Anylogic was gebruik as model platform. In die model was agente verantwoordelik vir voorraad- en verkope bestuur. Hulle moes onderling met mekaar onderhandel om die optimale bestelling strategiee te verkry. Konsepte soos agentbevrediging, aggressie faktore en herinneringsvermoens is ingestel om die onderhandeling tussen die agente te bewerkstellig. Die resultate het gewys dat die agente oor die vermoe beskik om met goeie strategiee vorendag te kom. Die Pareto fronte wat gegenereer is deur hul voorgestelde strategiee was 'n goeie benadering tot die bekende front. Die benadering was ook suksesvol toegepas op 'n erkende MOO toets-probleem wat bewys het dat dit oor die potensiaal beskik om 'n verskeidenheid van MOO probleme op te los. Toekomstige navorsing kan daarop fokus om hierdie benadering verder te ontwikkel vir meer praktiese toepassings soos komplekse voorsieningskettingstelsels, finnansiele modelle, risiko-analises en ekonomie

Hybrid Model for It Investment Analysis: Application to Rfid Adoption in the Retail Sector

One of the major obstacles in Information Technology (IT) adoption is its return on investment analysis. IT benefits in organizations are hard to measure and are usually realized over time. System dynamics approach has been used in IT literature to identify the impact of IT on business processes. Given benefits of any IT system in organizations, however, there is a high degree of uncertainty in achieving such benefits. Managerial flexibility in decision making process of implementing a new IT helps managers to overcome this uncertainty over time. Traditional cost benefit analysis such as NPV that is typically used to value any technology is unable to value managerial flexibilities while real options theory offers a model that can value a new investment as uncertainties about the system decreases over time. In this dissertation, we are proposing a new hybrid model for IT return on investment (ROI) that combines system dynamics and real options as two major techniques in economics of IT. This robust hybrid model takes advantages of both techniques while overcoming their weaknesses. We propose a systems dynamic solution to simulate the way an IT influences and improves an organization to be able to estimate the parameters used in the real options model. The hybrid model is used to find the best time for investing in item-level RFID in the retail sector.The results of return on investment analysis on item-level investment show that the variable cost of investment that is the tag prices dominates the return on investment. Other factors such as product unit price and consequently type of retail stores are important as well. The system dynamics simulation provided some major parameters of the real options model such as the expected payoffs and volatility of the expected payoffs that were hard to find in the literature.Business Administration (MBA

Supply Chain Risk Assessment for Perishable Products Applying System Dynamics Methodology - A Case of Fast Fashion Apparel Industry

With the fast progress of science and technology and with the continuously growing customer expectations, share of merchandise exhibiting characteristics of perishability is on the rise. Perishable products, through their own nature, are subject to decay, deterioration or obsolescence. As a result, their usefulness, value or functionality is gradually reduced or even lost in a short window of time and cannot be regained if it is not used or sold within a specific time window. When producing perishable products, all stages of the supply chain are exposed to much higher uncertainty than in the case of durable products, which directly means higher risk. The phases of inventory planning, lead time control, and demand forecasting for perishable products play a critical role in the overall effectiveness of the supply chain. For this reason, the system dynamics methodology, a simulation and modeling technique developed specifically to address the long term and dynamic management issues, is adopted in this study. The focus of the proposed model is on the interaction between physical processes, information flows and managerial policies of a three-level supply chain for perishable products, in general, and fast fashion apparel supply chain, in particular, so as to create the dynamics of the variables of interest. The values of supply chain key factors such as, for example, inventory, backlogs, stock-outs, forecast error, cost, and profit for each time period are some of the outputs of the proposed model. Moreover, the Conditional Value at Risk (CVaR) measure is applied to quantify and analyze the risks associated with the supply chain for this type of product and also to determine the expected value of the losses and their corresponding probabilities. With the focus on three prominent categories of risks including risks of delays, forecast, and inventory, multiple business situations for effective strategic planning and decision making are generated and analyzed

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

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

Green supply chain quantitative models for sustainable inventory management: A review

[EN] This paper provides a systematic and up-to-date review and classification of 91 studies on quantitative methods of green supply chains for sustainable inventory management. It particularly identifies the main study areas, findings and quantitative models by setting a point for future research opportunities in sustainable inventory management. It seeks to review the quantitative methods that can better contribute to deal with the environmental impact challenge. More specifically, it focuses on different supply chain designs (green supply chain, sustainable supply chain, reverse logistics, closed-loop supply chain) in a broader application context. It also identifies the most important variables and parameters in inventory modelling from a sustainable perspective. The paper also includes a comparative analysis of the different mathematical programming, simulation and statistical models, and their solution approach, with exact methods, simulation, heuristic or meta-heuristic solution algorithms, the last of which indicate the increasing attention paid by researchers in recent years. The main findings recognise mixed integer linear programming models supported by heuristic and metaheuristic algorithms as the most widely used modelling approach. Minimisation of costs and greenhouse gas emissions are the main objectives of the reviewed approaches, while social aspects are hardly addressed. The main contemplated inventory management parameters are holding costs, quantity to order, safety stock and backorders. Demand is the most frequently shared information. Finally, tactical decisions, as opposed to strategical and operational decisions, are the main ones.The research leading to these results received funding from the Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". It was also funded by the National Agency for Research and Development (ANID) / Scholarship Program/Doctorado Becas en el Extranjero/2020 72210174.Becerra, P.; Mula, J.; Sanchis, R. (2021). Green supply chain quantitative models for sustainable inventory management: A review. Journal of Cleaner Production. 328:1-16. https://doi.org/10.1016/j.jclepro.2021.129544S11632

Integrated methodological frameworks for modelling agent-based advanced supply chain planning systems: a systematic literature review

Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects. Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited. Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging fieldPeer Reviewe