<p dir="ltr">Small and medium enterprises generally employ the use of custom developed quoting programs to bid on goods and services. Custom bid programs (e.g., Microsoft Excel) are used to capture the company-specific costs of production. The inputs of variable costs, such as machine rate and scrap rate, are critical to get correct (Brassington & Pettitt, 2013); however, companies often rely on educated guesses and industry expertise to quote packaging products to end-users. Due to the guesswork involved there can be a financial difference between the quoted costs and actual costs. This variance is often the cause of significant lost dollars. Price, if not determined correctly, could negatively impact both the company’s and the product’s profitability (Helna, 2020). Predictive analytics can be used to support quoting activities by providing a future value based on previous job performance. The purpose of the present study is to identify whether predictive analytics can be used to predict machine rate and scrap rate to give more accuracy to quoting estimation.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/24372574 |
| Date | 19 October 2023 |
| Creators | Diana H Solt (17222431) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/_b_Using_Predictive_Analytics_to_Reduce_Small_Business_Cost_Estimation_Error_b_/24372574 |
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