The machine learning approach taken will be of supervised learning, using previous orders styles and adjusting to the company updated SMV for a particular garment to generate the specified metrics of operational efficiency. The generic calculations for generating SMV are stated below. The proposed solution is planned to address these issues using machine learning and non-machine learning approaches. The machine learning techniques used will be primarily built using these 3 libraries. The availability of historical data for creating a labelled dataset from the sample room data will vary from organizations depending on their respective company policies. Day-wise total production capacity from 4 lines and cumulative production is shown in the following table. In the last day, lines don't need to work 10 hours to stitch the balance quantity. Shown below are a list of tools to be used to implement the model. Shown below are the respective RMSE (Root mean squared error), MAPE (Mean absolute percentage error), MSE and MAE for the respective approaches. ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue III Mar 2022- Available at www.ijraset.com Responses illustrated in Figure 3-5 indicates that the majority of respondents would prefer further utilizing the data on operational efficiencies of the workforce. Figure 3-6 below shows what type of metrics they would prefer. Illustrated below are the responses from the questionnaire regarding reporting. According to the response analysis, the majority of respondents prefer their report schedule refresh time to be approximately 12 hours. 2) Covid-19 Pandemic affecting supply chain for raw materials The COVID-19 epidemic wreaked havoc on global trade, economic, health, and education systems, as well as enterprises and society, like few other events in the last century. Some sectors were hit particularly hard with the disruptions to the supply chains, such as apparel companies. Figure 3-7 illustrates how 100% of the participating respondents of the questionnaire believe the covid- 19 pandemic had a negative impact on their supply chain. Did the covid-19 pandemic affect your supply chain? With the fluctuating demand and businesses closing their operations due to the pandemic, apparel companies in Sri Lanka and globally found it a struggle in some months during the year 2020-2021 to source some of the necessary raw materials to continue production of certain items. Figure 3-8 and Figure 3-9 elaborate which periods of the year respondents found it most difficult to source raw materials. Figure 3-8 and Figure 3-9 prove that there were difficulties in sourcing raw materials during the year 2021. Analysis from the questionnaire responses show that 19.4% believe August was the most difficult month to source raw materials. When discussed with Kapil Ravindran, he specified, “The industries with the most globalized operations and especially those that rely on Chinese inputs for manufacturing such as some garment companies in Sri Lanka were the most vulnerable to COVID-19's early supply chain disruption. The fashion sector confronts enormous dangers due to its non-essential nature. Indeed, because of COVID-19, consumers all around the world were no longer in need of new items. This industry is characterized by a worldwide supply chain that is highly interconnected.” Responses illustrated in Figure 3-10 justifies what was discussed with K Ravindran during the interview regarding the negative impact faced by the Sri Lankan garment industry supply chain due to the Covid-19 pandemic. This further justifies the requirement for small garment organisations to effectively plan material sourcing for orders. Respondents were asked to rate whether a more accurate system planning and overview for material costing and material planning along with wastage and consumption to better understand the overall production cost of the sample. Figure 3-10 illustrates what type of raw material was difficult to source during the year 2021. 50% of respondents of the questionnaire rated it as highest rating of preferability, and 38.9% of respondents rated it as 4, the second highest. This justifies the requirement for this prediction for better overall material planning prior to approving production. 86.1% of the participants expressed their preference to use a web application over a mobile solution to effectively analyse workforce output, gain a transparent overview of the sample process prior to the approval of bulk production. To further comprehend the willingness of the participants to use a web-based solution, comprehensive questions were added to the survey. The participants were asked to rate from | to 5 on the willingness to utilise the features of the proposed solution. The figures below show that participants rated 3-5 for all the proposed features to be implemented.
