Development of a Web-Portal Health Information System for Barangay

In the Philippines, a barangay is the smallest administrative unit serving as suburban neighborhoods' first line of defense. According to Bautista, barangays conduct a manual file-based process of storing the community's health information. Therefore, the need for a single platform enables a small government unit to manage its resources while being transparent to its community. The study aims to develop a web- based barangay health information system portal for Barangay 69 District 1 in Tondo Manila. The system would be a reference tool for barangays as their platform provides inventory management, the barangay's health programs, and a dashboard for data visualization inventory management, tracking of Covid cases, administration of health activities, and a dashboard for data visualization. As a result, the web portal is functional, and different test scenarios show above-average results. The study concludes that the system provided a platform for the barangay and its residents. It also concludes that it is user-friendly and efficiently disseminates the barangay's health programs and activities. © 2023 IEEE.

IoE-Based Predictive Oxygen Inventory Management

The COVID-19 pandemic highlighted a major flaw in the current medical oxygen supply chain and inventory management system. This shortcoming caused the deaths of several patients which could have been avoided by accurate prediction of the oxygen demand and the distribution of oxygen cylinders. To avoid such calamities in the future, this paper proposes an Internet of Everything (IoE) based solution which forecasts the demand for oxygen with 80-85% accuracy. The predicted variable of expected patients enables the system to calculate the requirement of oxygen up to the next 30 days from the initiation of data collection. The system is scalable and if implemented on a city or district level, will help in the fair distribution of medical oxygen resources and will save human lives during extreme load on the supply chain. © 2023 IEEE.

The knock-on effects of COVID-19 pandemic on the supply and availability of generic medicines in Ethiopia: mixed methods study.

BACKGROUND: COVID-19 pandemic posed a major impact on the availability and affordability of essential medicines. This study aimed to assess the knock-on effects of the COVID-19 pandemic on the supply availability of non-communicable chronic disease (NCD) medicines and paracetamol products in Ethiopia. METHODS: A mixed methods study was conducted to assess the supply and availability of twenty-four NCD drugs and four paracetamol products listed on the national essential medicines list for hospitals. Data were collected from twenty-six hospitals located in seven zones of Oromia region in the southwestern part of Ethiopia. We extracted data on drug availability, cost and stock out for these drugs between May 2019 and December 2020. The quantitative data were entered into Microsoft Excel and exported to statistical package software for social science (SPSS) version 22 (IBM Corporation, Armonk, NY, USA) software for analysis. RESULTS: The overall mean availability of selected basket medicines was 63.4% (range 16.7% to 80.3%) during the pre-COVID-19 time. It was 46.3% (range 2.8% to 88.7) during the pandemic. There was a relative increase in the availability of two paracetamol products [paracetamol 500 mg tablet (67.5% versus 88.7%) and suppository (74.5% versus 88%)] during the pandemic. The average monthly orders fill rates for the selected products range from 43 to 85%. Pre-COVID-19, the average order fill rate was greater or equal to 70%. However, immediately after the COVID-19 case notification, the percentage of order(s) filled correctly in items and quantities began decreasing. Political instability, shortage of trained human resources, currency inflation, and limited drug financing were considered as the major challenges to medicine supply. CONCLUSION: The overall stock out situation in the study area has worsened during COVID-19 compared to pre-COVID-19 time. None of the surveyed chronic disease basket medicines met the ideal availability benchmark of 80% in health facilities. However, availability of paracetamol 500 mg tablet surprisingly improved during the pandemic. A range of policy frameworks and options targeting inevitable outbreaks should exist to enable governments to ensure that medicines for chronic diseases are consistently available and affordable.

Data Model Study for Advancement of Japanese PSI Management Introducing the Concepts of S&OP

Under the impact of COVID-19, the global economy exhibits obvious slowdown. In such situation, the issues on how to keep balance between supply and demand in SCM (Supply Chain Management) operation have been more apparent than before. For SCM rebuilding, S&OP received extensive attention worldwide. However, there are few examples of successful implementation of S&OP in Japan because Japanese have not been growing accustomed to the phrase S&OP, although the challenges in SCM operations and PSI (Product, Sales, Inventory) management are recognized. Thus, no clear and exact solution is found to guide the operation. In order to further improve the current management level, a new design proposal of data model is rendered to advance the current PSI management that introduces the concepts of S&OP. Especially, we will address the motivation why we need use multidimensional database architecture to design the S&OP process instead of using RDB (Relational Database) which is often used in ERP (Enterprise Resource Planning). © 2022 IEEE.

A resilient inventory management of pharmaceutical supply chains under demand disruption

Inspired by the global supply chain disruptions caused by the COVID-19 pandemic, we study optimal procurement and inventory decisions for a pharmaceutical supply chain over a finite planning horizon. To model disruption, we assume that the demand for medical drugs is uncertain and shows spatiotemporal variability. To address demand uncertainty, we propose a two-stage optimization framework, where in the first stage, the total cost of pre-positioning drugs at distribution centers and its associated risk is minimized, while the second stage minimizes the cost of recourse decisions (e.g., reallocation, inventory management). To allow for different risk preferences, we propose to capture the risk of demand uncertainty through the expectation and worst-case measures, leading to two different models, namely (risk-neutral) stochastic programming and (risk-averse) robust optimization. We consider a finite number of scenarios to represent the demand uncertainty, and to solve the resulting models efficiently, we propose L-shaped decomposition-based algorithms. Through extensive numerical experiments, we illustrate the impact of various parameters, such as travel time, product's shelf life, and waste due to transportation and storage, on the supply chain resiliency and cost, under optimal risk-neutral and risk-averse policies. These insights can assist decision makers in making informed choices. © 2023 Elsevier Ltd

Machine Learning Based Predictive Analytics for Agriculture Inventory Management System

When the globe was hit by the vicious Covid 19 pandemic, multiple industries faced the virus's wrath and that included the agricultural warehouse industry. Consequently, many warehouses which had received large shipment stocks of agricultural products were never to be used again as it had reached its expiration date. This led to major losses for the agricultural warehouses as well as losses in crops for farmers and large scale agriculturists. The main objective of this paper is to build a model which utilises 3 heavy-weight algorithms (Seasonal Autoregressive Integrated Moving Average-SARIMA, Long short term memory-LSTM and Holt Winters) and predicts the agricultural needs of retailers and consumers based on previous data from different warehouses. Deploying this system will not help in the regulation of goods in warehouses but will also aid in maximizing the profits and minimizing the losses for warehouses. The algorithm with the least MAE(Mean Absolute Error) value will be considered for forecasting the sales of the aforementioned product. © 2022 IEEE.

Identification of key drivers for improving inventory management in pharmaceutical supply chains

Existing literature on optimizing inventory levels in pharmaceutical supply chains has focused on a limited set of drivers. However, the global supply chain disruptions produced by the Covid-19 pandemic demonstrated the need for a more nuanced picture of the inventory management drivers in this sector to identify profitable inventory configurations while fulfilling demands and safety margins. To address this gap in the literature, this paper identifies key drivers impacting inventory levels and develops a framework for assessing inventory configurations in pharmaceutical supply chains. The framework is tested using a single case study approach. The case study showed that while external and downstream supply chain factors were recognized as being critical to pursuing inventory reduction initiatives, internal factors prevailed when making inventory management decisions. The framework developed in this paper may assist practitioners in identifying the most important factors impacting inventory levels within a specific pharmaceutical supply chain configuration and is in use in the industry today. © 2023, The Author(s) under exclusive licence to German Academic Society for Production Engineering (WGP).

CADENAS DE SUMINISTRO ANTIFRÁGILES: UN NUEVO PARADIGMA EN LA GESTIÓN DE INVENTARIOS

En el año 2012, Taleb planteó un innovador concepto, la 'antifragilidad', que es una capacidad potencialmente desarrollable por personas, sistemas, organizaciones. También por cadenas de suministro. La sub-disciplina de Gestión de Riesgos de la Cadena de Suministro (SCRM-Supply Chain Risk Management) está empezando a adoptar este nuevo enfoque, tan necesario en la actualidad, tanto por las características del contexto como por las prácticas empresariales más extendidas en la gestión de la cadena de suministro de los últimos 30 años. Este artículo presenta este concepto y sus implicaciones de gestión, junto con aplicaciones específicas en la gestión de inventarios.Alternate abstract:In 2012, Taleb put forward an innovative concept, 'antifragility', which is a capability potentially developable by people, systems, and organizations. Also, by supply chains. The sub-discipline of SCRM (Supply Chain Risk Management) is beginning to adopt this new approach, so necessary today, both because of the characteristics of the context and because of the most widespread business practices in supply chain management over the last 30 years. This article works on this concept and its management implications, together with specific applications in inventory management.

Supply Chain Network Model using Multi-Agent Reinforcement Learning for COVID-19

The COVID-19 vaccination management in Japan has revealed many problems. The number of vaccines available was clearly less than the number of people who wanted to be vaccinated. Initially, the system was managed by making reservations with age group utilizing vaccination coupons. After the second round of vaccinations, only appointments for vaccination dates were coordinated and vaccination sites were set up in Shibuya Ward where the vaccine could be taken freely. Under a shortage of vaccine supply, the inability to make appointments arose from a failure to properly estimate demand. In addition, the vaccine expired due to inadequate inventory management, resulting in the vaccine being discarded. This is considered to be a supply chain problem in which appropriate supply could not be provided in response to demand. In response to this problem, this paper examines whether it is possible to avoid shortage and stock discards by a decentralized management system for easy on-site inventory control instead of a centralized management system in real world. Based on a multi-agent model, a model was created to redistribute inventory to clients by predicting future shortage based on demand fluctuations and past inventory levels. The model was constructed by adopting the Kanto region. The validation results of the model showed that the number of discards was reduced by about 70% and out-of-stocks by about 12% as a result of learning the dispersion management and out-of-stock forecasting © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Quality and selling price dependent sustainable perishable inventory policy: Lessons from Covid-19 pandemic

This paper addresses the impact of the Covid-19 lockdown on the warehousing of perishable items facing demand-side shocks, mainly those with selling price and product quality dependent demand, for example, fresh fruits, meats, vegetables, packed foods, etc. Along with demand-side issues, such an inventory system consumes a significant amount of energy in terms of freshness, increasing carbon tax and dwindling the firm's total profit. We formulate two-warehouse inventory models of perishables items using the first-in-first-out (FIFO) dispatching policy under two different Covid-19 lockdown scenarios. The two-warehouse system primarily consists of an owned warehouse (OW) and a rented warehouse (RW). Two different lockdown scenarios are considered as;(i) the lockdown during the consumption of goods in OW and (ii) the lockdown during the consumption of goods in RW. The demand rate is assumed to decline and surge by a finite volume as lockdown is forced and relaxed. The proposed models help in assessing the impact of lockdown on (i) product quality, (ii) product cost, (iii) inventory level, (iv) freshness keeping efforts, (v) investment in green technologies, and (vi) carbon cap and trade policy. We determine the above six parameters to maximize the firm's total profit. The key findings of this model suggest that yield is primarily affected due to carbon cap and trade policy, lockdown period, item price, backlogging, and variation in the holding costs in OW and RW. These models may assist the small, medium, and large firms involved in perishable or cold supply chains to assess the effect of Covid-19 like disruption and take corrective measures to maximize their profit.

Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting

The COVID-19 pandemic poses new challenges on pharmaceutical supply chain including the delays and shortages of resources which lead to product backorders. Backorder is a common supply chain problem for pharmaceutical companies which affects inventory management and customer demand. A product is on backorder when the received quantity from the suppliers is less than the quantity ordered. Knowing when a product will be on backorder can help companies effectively plan their inventories, propose alternative solutions, schedule deliveries, and build trust with their customers. In this paper, we investigate two problems. One is how to use machine learning classifiers to predict product backorders for a pharmaceutical distributor. The second problem we focused on is what are the particular challenges and solutions for such task under a pandemic setting. This backorder dataset is different from existing benchmark backorder datasets with very limited features. We discuss our challenges for this task including empirical data pre-processing, feature engineering and understanding the special definitions of backorder under the pandemic situation. We discuss experimental design for predicting algorithm and comparison metrics, and demonstrate through experiments that decision tree algorithm outperforms other algorithms for our particular task. We show through explainable machine learning approaches how to interpret the prediction results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Improving supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development

Purpose: The purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization's agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance. Design/methodology/approach: A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics. Findings: As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the "frequency of new product development is at the top”, followed by "advances in product design and development” and "estimated versus actual time to market”. Research limitations/implications: It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison. Practical implications: The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison. Originality/value: A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance. © 2023, Emerald Publishing Limited.

Covid-19 Vaccine safety and adverse event analysis from Pakistan

Covid immunization commenced on 2nd Feb 2021 in Pakistan and as of 7th Sep 2021, over 84 million vaccine doses were administered in Pakistan, of which 72% procured by the government, 22% received through Covax and 6% were donated. The vaccines rolled out nationally included: Sinopharm, Sinovac and CanSinoBIO (China), AstraZeneca (UK), Moderna and Pfizer (USA), Sputnik (Russia), and PakVac (China/Pakistan). About half of the eligible population in Pakistan (63 m) had received at least one dose of Covid vaccine as of Sep 2021. Pakistan National Pharmacovigilance Centre (PNPC) in coordination with WHO, MHRA and Uppsala Monitoring Centre (UMC) established pharmacovigilance centers across Pakistan. The Covid vaccine AEFIs in Pakistan were mainly reported via NIMS (National Immunization Management System), COVIM (Covid-19 Vaccine Inventory Management System), 1166 freephone helpline and MedSafety. There have been 39,291 ADRs reported as of 30th Sept 2021, where most reported after the first dose (n = 27,108) and within 24-72 h of immunization (n = 27,591). Fever or shivering accounted for most AEFI (35%) followed by injection-site pain or redness (28%), headache (26%), nausea/vomiting (4%), and diarrhoea (3%). 24 serious AEFIs were also reported and investigated in detail by the National AEFI review committee. The rate of AEFIs reports ranged from 0.27 to 0.79 per 1000 for various Covid vaccines in Pakistan that was significantly lower than the rates in UK (~4 per 1000), primarily atrributed to underreporting of cases in Pakistan. Finally, Covid vaccines were well tolerated and no significant cause for concern was flagged up in Pakistan's Covid vaccine surveillance system concluding overall benefits outweighed risks.Copyright © 2022

INVENTORY MANAGEMENT OPTIMIZATION FOR CAPITAL GOODS MAINTENANCE

Spare parts play an important role in supporting capital goods maintenance, contributing to downtime reduction and lifetime extension. However, once systems are becoming more and more advanced, and their reliability has also increased, both trends enlarged the amount of components with low demand, and spare parts management are becoming more complicated. Consequently, given the mindset change about delivery times with Covid-19 pandemic and the concern related to global spending in aftersales services, together with customers demanding high uptime levels and TCO (Total Cost of Ownership) reduction, the search for more efficient methods to manage spare parts inventory has emerged. Based on the use of industry 4.0 techniques, the aim of this study is to propose a framework for spare parts provisioning, reducing both maintenance and downtime costs. Copyright, American Society for Engineering Management, 2022.

Inventory systems with uncertain supplier capacity: an application to covid-19 testing

The COVID-19 pandemic has forced governments to impose crippling restrictions on the day-to-day activities of citizens. To contain the virus and lift these restrictions safely, policymakers need to know quickly where the virus is spreading. This has been possible only through widespread testing. Not long after starting largescale testing in the early stages of the pandemic and more recently with a surge of new variants, countries hit a roadblock—the shortage of swabs used in the testing kits due to disruptions in the supply chain caused by COVID-19. This disruption translates to a variable production capacity of the swab suppliers. As a result, when countries order swabs from a swab supplier, their order might not be fully satisfied. Hence, adopting a proper swab inventory management model can help countries better manage COVID-19 testing and avoid widespread shortages of testing supplies. By considering two different swab demand patterns (i.e., stationary and stochastic) and two different production capacity scenarios for the swab supplier (i.e., ample and variable production capacity), we develop four analytical models, in which we consider all combinations of the above demand and capacity scenarios, to derive the optimal swab-procurement policy for a country. Given the rapid change of COVID-19 infection cases and the limited planning period, countries should aim for reactive scheduling. Through a comprehensive numerical study, we also provide guidelines on how countries should optimally react to these changes in the supply and demand of swabs. The research implications for managing inventory with stochastic supplier capacity and uncertain demand in a finite time horizon extend well beyond the application to COVID-19 testing.

Joint Decisions of Components Replacement and Spare Parts Ordering Considering Different Supplied Product Quality

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. Note to Practitioners—This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3). IEEE

Design of Enterprise Inventory Management Program Based on Java EE Programming

Since the outbreak of the novel coronavirus at the end of 19, the competition among SMEs has become increasingly fierce. Based on their traditional management model and low level of informatization, SMEs have poor ability to cope with the impact of the epidemic and cannot quickly meet the new needs of target customers and new market opportunities. In the context of the impact of the epidemic situation, small and medium-sized enterprises need to optimize the inventory management system by introducing computer technology, use big data analysis technology to adapt to market demand, reduce the problem of enterprise inventory backlog, improve the efficiency of enterprise resource allocation, and maximize the avoidance of resource mismatch. This paper uses Java EE programming technology and the Spring Boot framework to design an enterprise inventory management system, which can comprehensively and directly display the enterprise inventory management situation, achieve data recording, storage, and modification. At the same time, it uses enterprise inventory management data to achieve the portrait analysis of target users, improve the efficiency of enterprise inventory management, optimize the enterprise management system, and quickly meet the new needs of customers. © 2022 Association for Computing Machinery.

Supply Chain Resilience of Mineral Resources Industry in China

Improving the supply chain resilience of the mineral resources industry is crucial for ensuring national economic security in China. Based on the supply and demand data of China's mineral resources industry from 2002 to 2018, this study adopts system dynamics model to simulate the supply chain resilience of the mineral resources industry, the mining industry, and the smelting and processing industry under the scenario of steady economic development and the scenario of supply chain crisis. From the simulation results, the reserves of the mineral resources industry and the smelting and processing industry under the two scenarios are nearly the same, indicating that they are weakly affected by the foreign market, and both have strong resilience. The mining industry has a high dependence on imports and a lack of supply chain resilience. Under the condition of steady economic development, the output of the mining industry needs to develop at a low speed to reduce production capacity. More attention should be paid to the high level of import dependence and insufficient supply chain resilience of the mining industry. In the stable international trade situation, reserves of important minerals should be increased to alleviate the resource shortage during the supply chain crisis.

EMSRtrc: relaxation of booking limits by total revenue control for expected marginal seat revenue.

The Covid-19 pandemic has negatively affected life worldwide and caused catastrophic loss of life. It has also been harming the economic activities of businesses, and airline companies are among the sectors most affected by this situation. One of the goals for survival in such a situation is to make the best in airline revenue management (ARM). The most helpful model for ARM is Expected Marginal Seat Revenue (EMSR), widely used in the literature and industry. In this study, the simple and effective models that simulated EMSRa, EMSRb, and EMSRc were developed, called EMSR Total Revenue Control (EMSRtrc). The proposed three models aim to keep the simplicity of the original EMSR models while creating a new perspective and methodology. The developed EMSRtrc models were tested with numerical examples and compared with EMSRa, EMSRb, and EMSRc models proposed previously in the literature. Numerical examples show that the developed EMSRtrc models perform better than the EMSRa, b, and c. For the minimum revenue category, the developed EMSRtrc models exhibit outstanding performance. The results show that the proposed models guarantee a higher minimum revenue of 9.900, 11.619, and 2.537%, respectively. The EMSRtrc models have generated higher revenue and achieved a higher load factor rate of up to 98% simultaneously. Considering the third numerical example, the approximate number of empty seats is 1.3 for the EMSRtrc-(a), 2.65 for the EMSRtrc-(b), and 7.85 for the EMSRtrc-(c). The overall results demonstrate that the proposed model is an effective tool for ARM.

Blood Inventory Management During COVID-19 Pandemic Using a Simple Mathematical Tool: A Two-Year Study from a Tertiary Care Hospital in North India.

Multiple recurrent waves of the coronavirus disease 2019 (COVID-19) resulted in major fluctuations in blood supply and demand, which presented a major challenge for the blood centres to maintain adequate blood inventory. Hence, the primary aim of the present study was to determine whether safety stock as a simple mathematical tool can be used to maintain optimum blood inventory to meet all blood demands. The secondary aim of the study was to test whether daily blood stock index (DBSI), which was a novel index developed by the authors and derived from the calculated safety stock, can be used to minimize blood wastage due to the outdating of packed red blood cells (PRBC)/whole blood (WB) units. The present study was a descriptive, cross-sectional study conducted from 1st October 2019 to 31st December 2021 at a blood centre of a tertiary care hospital. For the purpose of data analysis, the time period of study was divided into 7 periods signifying different phases during the COVID-19 outbreak. Data of PRBC/WB (referred to as red cell) collection, red cell issue and the daily red cell stock were collected for these 7 time periods. Safety stock, percentage of out-dated whole blood/packed red blood cell units (OB) and DBSI were calculated based on the data extracted. Red cell collection as well as red cell utilization decreased during the 1st as well as the 2nd wave of the COVID-19 outbreak. The blood centre was able to meet the blood demand of the hospital at all times, as the daily average red cell stock remained above the calculated safety stock during all periods. OB (12.4%) and DBSI (2.3) were highest during the lockdown period of second wave of COVID-19 outbreak (period E). A strong direct relationship was seen between OB (dependent variable) and DBSI (predictor variable) [R = 0.79; p = 0.03]. Firstly, safety stock is a simple, user-friendly mathematic tool which can be used for efficient blood inventory management not only at times of a pandemic/disaster but also during routine times. Secondly, DBSI is a logical and empirical tool to reduce OB units and consequently reduce blood wastage.