Managing Inventory on Blood Supply Chain

Lestari, Fitra; Ulfah,; Nugraha, Ngestu; Azwar, Budi

doi:10.1007/978-981-13-0746-1_27

Cited by 7 publications

(8 citation statements)

References 5 publications

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“…Fortsch and Khapalova [21] apply various approaches to predict blood demand such as Naïve, MA, ES, and multiplicative Time Series Decomposition (TSD), amongst which a Box-Jenkins (ARMA) approach results in the highest prediction accuracy. Lestari et al [22] apply four time series models, MA, WMA, ES and ES with trend, to forecast demand for blood components. Twumasi and Twumasi [23] apply K-Nearest Neighbour regression (KNN), Generalised Regression Neural Network (GRNN), Neural Network Auto-regressive (NNAR), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM), and an LSTM network for forecasting and backcasting blood demand to predict future and lost past demand data respectively, by using a rolling-origin procedure.…”

Section: Forecasting Methods In Blood Supply Chainmentioning

confidence: 99%

Demand forecasting for platelet usage: From univariate time series to multivariable models

Motamedi,

Dawson,

et al. 2024

PLoS ONE

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Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, five different demand forecasting methods, ARIMA (Auto Regressive Integrated Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator), random forest, and LSTM (Long Short-Term Memory) networks are utilized and evaluated via a rolling window method. We use a large clinical dataset for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily platelet transfusions along with information such as the product specifications, the recipients’ characteristics, and the recipients’ laboratory test results. This study is the first to utilize different methods from statistical time series models to data-driven regression and machine learning techniques for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariable approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach appears to be sufficient. We also comment on the approach to choose predictors for the multivariable models.

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Section: Forecasting Methods In Blood Supply Chainmentioning

confidence: 99%

Demand forecasting for platelet usage: From univariate time series to multivariable models

Motamedi,

Dawson,

et al. 2024

PLoS ONE

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“…Safety stock was defined as the bloodstock, sufficient to meet PRBC/WB (referred to as red cell) requirements for a period of 7 days (1 week) if the blood collection stops completely and blood usage remains to the maximum. Safety stock was calculated using the following formula [ 10 ]: …”

Section: Methodsmentioning

confidence: 99%

“…Several algorithms have been proposed in the past for the management of blood centre inventory [ 6 – 8 ]. Safety stock is one such model [ 9 ] and is defined as the reserve stock of an inventory required to meet demand for a specified period in case of no fresh stock is available to meet the demand during that specified period [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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

Bansal¹,

Bansal²,

Raturi³

et al. 2023

Indian J Hematol Blood Transfus

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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.

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“…Perishability and limited shelf life add complexity and cost to blood stock management (Pirabán et al, 2019). Mathematical demand forecast models have been empirically approved to address blood demand problems (Dharmaraja et al, 2020;Lestari et al, 2017). Thus, researchers claim that improving MPS actually enhances coordination, and therefore have a positive effect on firm performance sustainability.…”

Section: Master Production Scheduling and Blood Transfusion Sustainab...mentioning

confidence: 99%

“…Blood transfusion is necessary in addressing any blood shortages, illnesses and disorders in the human body (Lestari et al, 2017;Seed et al, 2018). As such, sustainable blood transfusion remains crucial in extending and improving life for many patients (Kruk et al, 2018;Mulcahy et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The moderating effect of farming contracts on the relationship between responsive supply chain elements and supply chain resilience

James¹,

Betty²,

Thaddeo³

et al. 2024

10.5267/j.jfs

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Food supply chain disruptions have of recent increased in terms of severity and frequency leading to viability problems for most businesses in related supply chain networks and hunger among millions of human beings. In the extant supply chain literature elements of responsive supply chain such as velocity, versatility, and visibility have been suggested as some of the possible strategies to build supply chain resilience. It has therefore been suggested in this study that these elements’ influence on supply chain resilience is moderated by contract farming as a special form of supplier development. Farming contracts are either production or marketing contracts. Data was collected from a conveniently selected sample of 200 restaurants that use supplier development in the form of contract farming for acquiring critical resources for their operations. A structural equation modelling was used to analyse data related to the direct effects and multi group structural equation modelling was used to assess the moderating effects. The results revealed that there is a statistically significant relationship between the three selected elements of responsive supply chain management and supply chain resilience, and all the hypothesised relationships were moderated by type of farming contracts, with the relationships being stronger under marketing contracts for supply chain velocity, and under production contracts than under marketing contracts for relationships involving versatility and visibility. Therefore, the study recommended that production contracts be used in the fast-food restaurant industry in order to reduce supply chain vulnerabilities, and marketing contracts to build supply chain capabilities. This is the first study that has sought to assess the differential utility of different farming contracts through assessing their moderating effects as a build-up on previous research that has already established that supplier development in the form of contract farming leads to supplier development.

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Managing Inventory on Blood Supply Chain

Cited by 7 publications

References 5 publications

Demand forecasting for platelet usage: From univariate time series to multivariable models

Demand forecasting for platelet usage: From univariate time series to multivariable models

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

The moderating effect of farming contracts on the relationship between responsive supply chain elements and supply chain resilience

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