- Title
- Inventory management decisions for effective inventory management in the South African automotive component manufacturing industry: pre-and since COVID-19
- Creator
- Delport, Jason
- Subject
- Inventory management
- Subject
- Automobile industry and trade
- Date Issued
- 2022-12
- Date
- 2022-12
- Type
- Master's theses
- Type
- Theses
- Identifier
- http://hdl.handle.net/10948/59511
- Identifier
- vital:62145
- Description
- Globalisation has enabled the automotive industry to source various automotive products worldwide. It assisted in increasing the economic growth of countries as it allowed the flow of goods and capital between countries and created many employment opportunities locally. Emerging markets, especially Africa, forms a pivotal part of the global automotive industry. The South African automotive industry as the largest manufacturing and third largest economic sector in South Africa, has been acknowledged by government as a prime source of economic growth. The South African manufacturing businesses, in particular the automotive component manufactures (ACMs) are reliant on inventory for automotive manufacturing. In 2019, the world was hit by the Coronavirus virus outbreak known as COVID-19, which became a global health pandemic that significantly affected the global economy. The pandemic and lockdown measures implemented, seriously affected the automotive industry, in particular inventory management as it led to raw materials inventory shortages due to delivery delays. Therefore, the primary objective of this study was to investigate the inventory management decisions influencing effective inventory management in the South African automotive component manufacturing (SAACM) industry prior to Covid-19 and whether and how it changed since the Covid-19 pandemic. The comprehensive literature review identified four inventory management decisions as independent variables (inventory forecasting, inventory storage, inventory control and inventory staff capabilities management) and effective inventory management as the dependent variable in the proposed hypothesised model. The model was tested to establish the influence of the identified four inventory management decisions on effective inventory management in ACMs prior to Covid-19 and then again since Covid-19. A quantitative research approach was followed to collect data required for the hypothesis testing. Nonprobability sampling in particular judgemental sampling was utilised for this study by selecting respondents employed by ACMs in South Africa as logistics managers, supply chain managers, production supervisors, master production schedulers, cycle count operators and warehouse staff. A selfadministered internet-based questionnaire was used to obtain the data from the target sample comprising 200 respondents, of which 162 were usable for further statistical analysis. Data was analysed first for prior to and then for since Covid-19 using Statistica Version 14 computer software. Exploratory factor analysis (EFA) was used to extract the variables and validate the measuring instrument. The Cronbach's alpha values for reliability were confirmed for each of the variables identified in the two sets of EFAs. All four independent variables (inventory v management decisions) and the dependent variable (effective inventory management) for prior to as well as since Covid-19 were found to be valid and reliable and retained for further analyses. The results of the Pearson product moment correlation coefficients reported mostly weak and moderate associations between variables for both prior to and since Covid-19. The results of the multiple regression analysis (MRA) for prior to Covid-19 found four statistically significant relationships between the four independent variables - inventory forecasting management, inventory storage management, inventory control management and inventory staff capabilities management and the dependent variable effective inventory management. The results of the MRA for since Covid-19 found two statistically significant relationships between two independent variables inventory forecasting management and inventory resource management and the dependent variable effective inventory management. The tested hypothesised model provides a framework for further testing in future ACM inventory management studies in other countries. Business managers and inventory management staff of global ACMs can use it as a guide for effective inventory management; on which specific inventory management decisions to always pay attention to and, which inventory management decisions to pay attention to when a long-lasting pandemic occurs such as Covid-19. It is recommended that regardless of the Covid-19 pandemic, inventory managers in ACMs in South Africa should consider inventory forecasting management methods such as demand forecasting, determining the economic order quantity (EOQ) for all inventory item orders and materials requirement planning (MRP). They should also use an inventory information sharing system and inventory replenishment procedure to ensure inventory is managed effectively. During a prolonged pandemic such as Covid-19, inventory managers in ACMs in South Africa should pay particular attention to inventory resource management specifically regarding re-order inventory levels and classifying all inventory items according to the importance of using ABC analysis. They should further offer employees inventory training to remain abreast of new inventory developments in the industry and for career advancement.
- Description
- Thesis (MCom) -- Faculty of Business and Economics Science, School of Applied Accounting, 2022
- Format
- computer
- Format
- online resource
- Format
- Format
- 1 online resource (xv, 204 pages)
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Business and Economics Science
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Delport, J Dec 2022.pdf | 874 KB | Adobe Acrobat PDF | View Details Download |