inventory management
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This research examined the effect of inventory management activities on retailer satisfaction in manufacturing industries in Rwanda from the year of 2016 to 2021. The mixed approach of both qualitative and quantitative data were used as research design to collect results from 121 respondents from 174 who were expected as sample size of the study by the use of simple random and stratified sampling techniques. Before, the actual process of data collection the researcher pre-tested the questionnaire survey and the key informant interview, which were used later for collecting data from the field. Thus, the quantitative data were analyzed using both descriptive as percentage distribution and inferential statistics represented by multiple linear regressions. Thus, the regression coefficients demonstrated that ?1 =.241, with p=0.002 < 0.05 at sig. level of 5% which proves that IOP had a statistically positive and significant effect on the satisfaction of retailers; ?2 = .311 with p=0.001 < 0.05 at a sig. level of 5% implying that ISM had a positive and statistical significant effect on satisfaction of retailers; and ?1 = .402 with p= 0.000 < 0.05 at a sig. level of 5% implying that IDM had demonstrated a positive and statistical significant effect on retailers’ satisfaction in IIL between 2016 and 2021. The respective coefficients further indicate that 24.1 %, 31.1% and 40.2% of the variability in retailers’ satisfaction can be attributed to inventory order processing, inventory storage management and inventory distribution management respectively. The research recommends IIL to adopt JIT inventory practices all the time to avoid inventory costs while retailers need to accurately forecast demand and make orders before they experience stock-outs which affects the supply chain. It is hoped that this study will encourage IIL to sustainably adopt inventory management activities that will continue to sustain their retailer satisfaction. The study may also motivate other researchers to conduct research covering the whole country in order to improve its reliability. Keywords: Inventory Management Activities, Retailer Satisfaction, Manufacturing Industries, Rwanda

This research examined the effect of inventory management activities on retailer satisfaction in manufacturing industries in Rwanda from the year of 2016 to 2021. The mixed approach of both qualitative and quantitative data were used as research design to collect results from 121 respondents from 174 who were expected as sample size of the study by the use of simple random and stratified sampling techniques. Before, the actual process of data collection the researcher pre-tested the questionnaire survey and the key informant interview, which were used later for collecting data from the field. Thus, the quantitative data were analyzed using both descriptive as percentage distribution and inferential statistics represented by multiple linear regressions. Thus, the regression coefficients demonstrated that ?1 =.241, with p=0.002 < 0.05 at sig. level of 5% which proves that IOP had a statistically positive and significant effect on the satisfaction of retailers; ?2 = .311 with p=0.001 < 0.05 at a sig. level of 5% implying that ISM had a positive and statistical significant effect on satisfaction of retailers; and ?1 = .402 with p= 0.000 < 0.05 at a sig. level of 5% implying that IDM had demonstrated a positive and statistical significant effect on retailers’ satisfaction in IIL between 2016 and 2021. The respective coefficients further indicate that 24.1 %, 31.1% and 40.2% of the variability in retailers’ satisfaction can be attributed to inventory order processing, inventory storage management and inventory distribution management respectively. The research recommends IIL to adopt JIT inventory practices all the time to avoid inventory costs while retailers need to accurately forecast demand and make orders before they experience stock-outs which affects the supply chain. It is hoped that this study will encourage IIL to sustainably adopt inventory management activities that will continue to sustain their retailer satisfaction. The study may also motivate other researchers to conduct research covering the whole country in order to improve its reliability. Keywords: Inventory Management Activities, Retailer Satisfaction, Manufacturing Industries, Rwanda

PurposeNumber of disastrous events are rising globally, and it is important to manage the humanitarian supply chain management process to assist the disaster affected individuals in terms of relief operations. Effective relief operations can help to recover the materialistic loss due to any disaster. But there is a paucity of studies regarding this issue for developing countries. This study, hence, inspected the factors that affect the disaster supply chain management (DSCM) processes for relief operations in Bangladesh.Design/methodology/approachThis study examined the factors affecting relief operations through a qualitative analysis. This study used thematic analysis. Interviews were conducted with related supply chain individuals who were triangulated by data from related publications and blogs.FindingsThe study showed that sustainable DSCM for relief operations in Bangladesh require addressing few factors as organizational capabilities, warehousing locations and inventory management, infrastructure facility, coordination among partners, government and local authority support to create a transparent, efficient, effective and sustainable DSCM process for relief operations in Bangladesh. The system loopholes can be identified and rectified on the base of these factors.Research limitations/implicationsThe number of interview respondents was limited to twenty who were selected randomly from four organizations. To create a sustainable disaster supply chain management (SCM) for relief operations few factors were considered as predominant factors in Bangladesh context to generalize the developing country contexts and other factors were not considered. Therefore, for farther humanitarian research, the model of this study can be used for quantitative research and the hypotheses can be tested empirically to get more acute findings.Practical implicationsAs this study identifies the factors which can help to create a sustainable DSCM system for relief operations, hence practically, Bangladesh humanitarian SCM agencies will be beneficial from this study. They can easily recognize the factors need to be considered to create a sustainable DSCM process for relief operations.Originality/valueThis is a unique study carried out to examine the factors affecting DSCM process for relief operations in Bangladesh.

Abstract Background Medicines and vaccines supply chains represent critical systems for realising one of the major targets of the United Nations’ third Sustainable Development Goals (SDGs)—access to safe, effective, quality, and affordable essential medicines and vaccines, for all. However, evidence suggests the system is confronted with several challenges in many low-medium income countries, including Nigeria. This scoping review aims to summarize the available evidence on the challenges of medicines and vaccines supply chain system in Nigeria. Results We searched relevant databases including Scopus and Web of Science for studies published between January 2005 and August 2020 on the challenges associated with medicines and vaccines supply chain systems in Nigeria. Our findings implicate several factors including difficulty with medicines or vaccines selection, procurement, distribution, and inventory management. Others included poor storage infrastructure, financial constraints, insecurity, transportation challenges, inadequate human resources, weak, or poorly implemented policies. These challenges mostly resulted in stock-outs of essential medicines which notably got worsened during the current COVID-19 pandemic. Conclusion Our study is a wake-up call on the need to prioritise the critical sector of the supply chain systems for medicines and vaccines in Nigeria. Effective implementation of existing policies, improved security, strengthening of the health system through adequate budgetary allocations, and provision of infrastructure including regular availability of electricity are keys to surmounting the challenges and improving access to medicines or vaccines in Nigeria.

Abstract Inventory managers in the blood supply chain always seek timely and proper response to their customers, which is essential because of the perishability and uncertainty of blood demand and the direct relationship of its presence or non-presence with human life. On the other hand, timely and regular delivery of blood to consumers is vital, as the weakness in delivery and transportation policies results in increased shortages, returns, blood loss and significant decrease in the quality of blood required by patients. Given the significance of this for the blood transfusion network, the paper tried to design a comprehensive and integrated optimal model of blood transfusion network logistics management by blood group to reduce the cost of losses, returns and blood shortages. This model is divided into two parts: Inventory management and routing. A combination of simulation techniques and neural network with several recurrent layers was used to evaluate the optimal inventory management and a multi-objective planning model was designed to determine the delivery and distribution of blood to consumers. The model designed was implemented in Khorasan Razavi Blood Transfusion Network with a main base, six central bases and 54 hospitals. Solving the model led to estimating the f consumer demand, the optimal value of target inventory and re-ordering point of central bases and hospitals, and blood distribution from the supplier to its consumers that decreased the units of blood returned to bases, increased inventory availability, and reduced costs significantly.

Nowadays, more and more consumers consider environmentally friendly products in their purchasing decisions. Companies need to adapt to these changes while paying attention to standard business systems such as payment terms. The purpose of this study is to optimize the entire profit function of a retailer and to find the optimal selling price and replenishment cycle when the demand rate depends on the price and carbon emission reduction level. This study investigates an economic order quantity model that has a demand function with a positive impact of carbon emission reduction besides the selling price. In this model, the supplier requests payment in advance on the purchased cost while offering a discount according to the payment in the advanced decision. Three different types of payment-in-advance cases are applied: (1) payment in advance with equal numbers of instalments, (2) payment in advance with a single instalment, and (3) the absence of payment in advance. Numerical examples and sensitivity analysis illustrate the proposed model. Here, the total profit increases for all three cases with higher values of carbon emission reduction level. Further, the study finds that the profit becomes maximum for case 2, whereas the selling price and cycle length become minimum. This study considers the sustainable inventory model with payment-in-advance settings when the demand rate depends on the price and carbon emission reduction level. From the literature review, no researcher has undergone this kind of study in the authors’ knowledge.

Supply chain network in the automotive industry has complex, interconnected, multiple-depth relationships. Recently, the volume of supply chain data increases significantly with Industry 4.0. The complex relationships and massive volume of supply chain data can cause visibility and scalability issues in big data analysis and result in less responsive and fragile inventory management. The authors develop a graph data modeling framework to address the computational problem of big supply chain data analysis. In addition, this paper introduces Time-to-Stockout analysis for supply chain resilience and shows how to compute it through a labeled property graph model. The computational result shows that the proposed graph data model is efficient for recursive and variable-length data in supply chain, and relationship-centric graph query language has capable of handling a wide range of business questions with impressive query time.