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Continuing with the Cupcake Creations inventory management DSS, the firm is concerned about their replenishment policy.

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Project Overview

Continuing with the Cupcake Creations inventory management DSS, the firm is concerned about their replenishment policy. They have noticed that weekly demand for cupcakes has become very volatile in recent months. Unfortunately, ordering large amounts of ingredients to account for demand variation could result in waste. On the other hand, ordering too little could result in rejecting many orders for lack of raw materials, so that the firm would lose revenues. Therefore, the company would like to find a better replenishment policy to improve ordering amounts. They currently use a fixed default weekly inventory replenishment level policy (see Figure 1 from the description of the first part of the project) to account for cupcake demand fluctuations. However, they think it would be better to develop a variable inventory replenishment policy based on demand forecast. For instance, they have noticed that demand for cupcakes negatively correlates with the average weekly price of eggs and flour, two of the main ingredients used in making cupcakes. That is, as prices of eggs and flour increase, the cost of making cupcakes also increases, so that the firm needs to charge a higher price for the cupcakes, which might result in decreased demand for cupcakes. The main objective of the second part of the project is to use linear regression to develop forecasting models to predict weekly demand of each type of product (cupcake boxes) that the company makes. The program will compute seven regression models (one for each type of box), and then use the models to predict demand for cupcakes and so, predict required ingredients. To do so, the firm has prepared a sample of about 1000 orders placed between April 26, 2021 and April 8, 2022 to develop the regression models. The sample data only include orders that were placed and delivered in the same week.

 

Project Specifications/Requirements I. Weekly Summary Frame: Write an R function entitled “Create.Weekly.Summary” to generate a data frame entitled “Weekly.Summary”. This frame will summarize the total number of boxes per week and per box type for each of the 50 weeks of data contained in the “Orders.xlsx” Excel file (from 4/26/2001 to 4/9/2022). The data for the average weekly egg price and the average weekly flour price will be provided in the Excel file entitled “EggFlourPrices.xlsx”. Do not assume that the given dataset is fixed, that is, when grading your assignment, it is likely that a different dataset will be used. Copy-Paste the code of your function on your Word file.

 

 

 

II. Regression Models Frame: Write an R function entitled “Create.Regression.Models” to create a data frame entitled “Regression.Models” that will contain regression models to predict weekly demand for each type of box (see Figure 2.) To do so, your program should automatically generate seven regression models, one for each type of box. In each regression model, the dependent variable is the weekly demand for the product and the independent variables are egg and flour prices. The data to compute the regression models should be taken from the “Weekly.Summary” data frame from the previous item. Copy-Paste the code of your function on your Word file.

 

 

 

III. Ingredients Forecast: Design a Shiny app to recommended replenishment inventory for next week (with respect to the date provided by the Sys.Date function from R). The recommended amounts for each of the ingredients should be based on the regression models and the pending orders for next week. The app will have a slider to input the next-week price of eggs (cents per dozen), ranging from 0 to 300, default is 150. The app will also have a numeric input to enter next-week price of flour, ranging from $0 to $1, in increments of $0.01, default is $0.50. Finally, add a button entitled “Predicted Ingredient Levels”. When this button is pressed, the program will use the seven regression models to predict weekly demands for each of the products (in number of boxes). Based on the predicted demands, the program will determine the default amounts of required ingredients to satisfy such demand. Then, the program will add the amounts of the ingredients needed to satisfy next week’s already scheduled orders. The results will be displayed using a table output showing a data frame like in Figure 3. Copy-Paste the code of your Shiny app on your Word file.

 

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