Nadif, Naurania (2020) Analysis of Demand Disruption in Retail using Bayesian Network. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
The COVID-19 pandemic outbreak has greatly impacted the daily lifestyle of consumers, so that they showcase a change in their shopping behavior that leads to demand disruption. Retail industries are one of the many business sectors that are directly impacted from this phenomenon. This phenomenon could create several issues in the retail industry, such as product availability and capacity issues. Therefore, an analysis of the impact of customer demand fluctuation in several item products, product families, and in the retail store during the COVID-19 crisis is required. The study will use Bayesian network as its tools, due to its ability to hierarchically sort risks and interpret causal probability into a graphical chart. The study will start with literature study and field condition study. Next is to gather inventory data from 15 retail locations from East Java region, particularly convenience stores. Then, proceed to developing Bayesian network layout based on the product categories. Then from the inventory data, the stock out probability is developed. Then developing the conditional probability table to help making the joint probability table. The joint probability table will reveal the percentage of impact of the pandemic driven disruption to the retail’s product availability, as well as families that are directly impacted by the demand disruption. This research will also use the Genie software to validate the results from the calculation and visually display the Bayesian network. In the last step of the research, suggestions based on the result of the research will be given.
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | T Technology > T Technology (General) > T57.5 Data Processing |
| Divisions: | Faculty of Industrial Technology > Industrial Engineering > 26201-(S1) Undergraduate Thesis |
| Depositing User: | Naurania Nadif |
| Date Deposited: | 23 Aug 2020 08:30 |
| Last Modified: | 30 Oct 2023 08:30 |
| URI: | http://repository.its.ac.id/id/eprint/80552 |
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