- Title
- Assessing factors affecting forecast accuracy in automotive and surface coatings industry
- Creator
- Mhletywa, Monde Irvin
- Subject
- Business forecasting
- Subject
- Business planning
- Subject
- Automobile industry and trade
- Date Issued
- 2025-04
- Date
- 2025-04
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/73037
- Identifier
- vital:79326
- Description
- Accurate Demand forecasting for many industries including automotive and coatings industry, is a cornerstone of ensuring accurate supply forecasting for supply chain management. This research primary objective is to assess the elements that can be used to improve forecast accuracy in the surface coatings industry. An operational plan that informs resource allocation and product delivery strategy can be delivered accurately when it is informed by accurate demand forecasting. Further, the strategic decision-making of the organisation is directly impacted by the level of accuracy of forecasting as the key performance indicators are linked to forecasting for finance, operations in a formal supply chain that includes purchasing, planning, production and logistics. These elements directly impact profitability and customer satisfaction. The research was conducted using a qualitative design with data collected using a semi-structured interview model within the automotive and coatings industry. Onion metaphor was used to carry research as methodology, while thematic analysis for analysing data to provide conclusive results was practised. The review of literature and research results confirmed that there are methods still to be exploited by the industry to improve accuracy of forecasting. The impact of time horizon, data quality, seasonal fluctuations, experience and expertise of forecasters cannot be ignored as directly impacted the accuracy of forecasting as confirmed by the research and literature with non-use of AI-driven forecasting methods through advanced technologies in data analysis and strategic decision making for forecast improvement contributing to poor forecast accuracy. To address these challenges, the study recommends the use of forecasters that are trained to gain expertise in order to use advanced technologies of AI driven forecasting methods, and with time, gain experience to be able to improve and maintain forecast accuracy.
- Description
- Thesis (MBA) -- Faculty of Business and Economic Sciences, Business School, 2025
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (156 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Business and Economic Sciences
- 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 | MHLETYWA, I.pdf | 3 MB | Adobe Acrobat PDF | View Details Download |