1019. Digital twins engine for SMEs sustainability through data driven decision making in South Africa
Invited abstract in session Digital twins and Industry 5.0: Building Resilience, stream OR for Development.
Authors (first author is the speaker)
| 1. | Amos C. Mpofu
|
| Accounting Sciences, National University of Science and Technology | |
| 2. | Helper Zhou
|
| School of Accounting, Economics and Finance, University of KwaZulu Natal | |
| 3. | Gordon Dash
|
| Finance and Decision Sciences, University of Rhode Island | |
| 4. | Nina Kajiji
|
| Computer Science and Statistics, University of Rhode Island, and The NKD Group, Inc. |
Abstract
Informal SME businesses face inconsistent transaction recording, hindering data driven decision making. Leveraging WhatsApp's ubiquity to track owner/manager behaviour enables a digital twin for voice activated transaction processing and analysis. This study applied NN, RF, and SVM models to forecast spending behaviour comparing actual, budgeted and projected using ML to identify spending archetypes. While diverse factors influence forecasting acuracy for business spending, results showed improved budget alignment prediction, thus enhances data driven decision making for SME sustainability.
Keywords
- Decision Support Systems
- Accounting
- Artificial Intelligence
Status: accepted
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