Your line manager, Ms Lionheart, has collected and scrutinised performance data for the company. She was concerned that the inventories were high for certain products which had resulted in significant price reductions and losses for the company. At the same time, the company had run out of stock for other items early in the season resulting in unsatisfied customers and lost sales. Ms Lionheart has concluded that the problem was not with the specific products carried in the stock, but with the quantities ordered by the procurement department for two popular products; Dog Ball Launcher and Lightweight Dog Lead. Dog Ball Launcher is a product carried by the company for the past four years. Quarterly demand data for the past four years are shown in table 1. Last year the company seemed to always be out of stock for this item. The model used by the procurement department to forecast demand for this product over the last two years has been Multiplicative Seasonal model based on seasonal indices developed using data from 2018‐2019 with predicted annual demand increase of 6 units per year since 2019. Lightweight Dog Lead is a new product that the company has been selling for the past two years. When the product was introduced in 2020, it was expected to have a large increasing demand trend. The procurement team has been using Trend Projection with least‐squares method developed using data from 2020 to forecast the demand for this item. However, they now seem to have too much inventory for this product. Monthly demand data for the past two years for this item is shown in table 2. Ms Lionheart now wonders if right forecasting models were being applied to these two products. She wants you to analyse the available data and produce a short report (1,000 words ± 10%) with all relevant tables and figures, to address the highlighted issues. In your report you need to consider the following for both sets of data: 1. Analyse and explain the data. 2. Is appropriate forecasting model being applied? Explain. 3. What forecasting model would you use? Explain why you selected your approach. 4. Generate the forecast for each period in 2020 and 2021 using the current model and the model you selected. Analyse the results. 5. Determine the forecast error for the current model and the model you selected. Explain your method and findings. 6. Provide three key recommendations for the company.q= quater2018: q1: 10 q2: 29 q3: 26 q4: 152019: q1:14 q2:31 q3: 29 q:182020 q1: 20 q2: 26 q3: 28 q3: 302021 q1: 30 q2: 31 q3: 33 q4: 35 AutoSave OFFHome Insert Draw Design LayoutGPastePage 2 of 3Times New... ✓BI4.5.CEU v ab12XvX606 wordsForecasting ReportReferencesA^ A Aa| ĄA 2✓ Av<<Tell meVParagraphFocusVStylesShareallu 2021 then we call suongly agree that this is not a stasunal product menee the useof seasonal multiplicative method is not necessary. We have been given 4 years ofdata which implies that we need to use a long-range forecast method.DictateFor table 2, the trend projection with least square method is not appropriate because itstates that it has too much inventory. We have only been given 2 years of data whichis not enough to use while using the trend projection with least-squares method.Therefore, we would have to use a medium range forecast method.3. For table 1, a method that would be good to accurately predict the forecast sales forthis would be trend projection with least-square method because it would create amore reliable forecast which would consider the previous data and forecast the trendthe future sales will have. This method will be able to spot out anomalies that we havein the data and ignore them when creating trend projection graphs.!!!!EEditorFor table 2, we could use weighted moving average because we do not have morethan 2 years of day and therefore this method would ensure that the most recent datahas more weightage than previous one. It would also predict the trend direction.However, once we have the actual demand for the third year, the forecast used willhave to be reassessed based on inventory and sales.6. The company can use JIT (Just In Time) method to minimize the chances ofoverstocking or understocking products. This will increase the chances of profits andreduce the chances ofCommentsŐ125%My StuCourseTED Sugata Mitra: The...= Microsoft Word...Extenumdx.mrooms.net/pluginf...AnsweQ3Q4All our free course...2 / 4past two years for this item is shown in table 2.94%AnsweW Gildan 18500 - He...14312918Table 1: Demand for Dog Ball Launcher 2018-2021Quarter 2018 Demand 2019 DemandQ110Q2292615+b My QueK.Ms Lionheart now wonders if right forecasting models were being applied to these twoproducts. She wants you to analyse the available data and produce a short report (1,000words ± 10%) with all relevant tables and figures, to address the highlighted issues.2020 Demand20262830Table 2: Demand for Lightweight Dog Lead 2020-2021Month2020 Demand 2021 Demand3698JanuaryESIn your report you need to consider the following for both sets of data:1. Analyse and explain the data.2. Is appropriate forecasting model being applied? Explain.3. What forecasting model would you use? Explain why you selected your approach.4. Generate the forecast for each period in 2020 and 2021 using the current model andthe model you selected. Analyse the results.5. Determine the forecast error for the current model and the model you selected.Explain your method and findings.6. Provide three key recommendations for the company.Mic XMKeyword Tool (FR...Update :2021 Demand30313335Page 2 of 4MGT2221 & MGT2222 Operations Management; 2022-23…….

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Your line manager, Ms Lionheart, has collected and scrutinised performance data for the company. She was concerned that the inventories were high for certain products which had resulted in significant price reductions and losses for the company. At the same time, the company had run out of stock for other items early in the season resulting in unsatisfied customers and lost sales. Ms Lionheart has concluded that the problem was not with the specific products carried in the stock, but with the quantities ordered by the procurement department for two popular products; Dog Ball Launcher and Lightweight Dog Lead. Dog Ball Launcher is a product carried by the company for the past four years. Quarterly demand data for the past four years are shown in table 1. Last year the company seemed to always be out of stock for this item. The model used by the procurement department to forecast demand for this product over the last two years has been Multiplicative Seasonal model based on seasonal indices developed using data from 2018‐2019 with predicted annual demand increase of 6 units per year since 2019. Lightweight Dog Lead is a new product that the company has been selling for the past two years. When the product was introduced in 2020, it was expected to have a large increasing demand trend. The procurement team has been using Trend Projection with least‐squares method developed using data from 2020 to forecast the demand for this item. However, they now seem to have too much inventory for this product. Monthly demand data for the past two years for this item is shown in table 2. Ms Lionheart now wonders if right forecasting models were being applied to these two products. She wants you to analyse the available data and produce a short report (1,000 words ± 10%) with all relevant tables and figures, to address the highlighted issues. In your report you need to consider the following for both sets of data: 1. Analyse and explain the data. 2. Is appropriate forecasting model being applied? Explain. 3. What forecasting model would you use? Explain why you selected your approach. 4. Generate the forecast for each period in 2020 and 2021 using the current model and the model you selected. Analyse the results. 5. Determine the forecast error for the current model and the model you selected. Explain your method and findings. 6. Provide three key recommendations for the company. q= quater 2018: q1: 10 q2: 29 q3: 26 q4: 15 2019: q1:14 q2:31 q3: 29 q:18 2020 q1: 20 q2: 26 q3: 28 q3: 30 2021 q1: 30 q2: 31 q3: 33 q4: 35