Pls follow the rules to answer the group discussion of “Molly“‘s part
Purpose: To make sure the team is on track to solving the key case issues.
1. The team leader should review all available information (including feedback from your professor regarding the S-SWOTs), review the teams learnings to date, and proposed solutions to the case problem(s) if any have been developed.
2. In this course you have learned three time-series forecasting models simple moving average, weighted moving average, and exponential smoothing. Develop forecasting models for Beat XX SKU XX. Compare the results of the different models and determine the most appropriate model using the mean absolute percentage error (MAPE). Briefly present your findings.
3. Besides the forecasting problem there were other issues you identified that iD faces in this case. Based on what you have learned from your research of this case, as well as from previous courses and what you learned in this course, consider what other recommendations you will make to iD and include them in your final report and presentation.
4. If necessary, request from the professor, either in-class time or a conference meeting, to discuss any roadblocks or to get clarification.
5. Prepare an outline of the final report. NOTE: As this is the initial draft of the report outline, it does not need to be complete. The idea is to get the team members thinking about the analytical process and how it would best be presented. – Alice
6. Assign next steps to team members
· An initial draft of your report outline, a summary recommendation (from 2 and 3 above), and what next steps, if any, are proposed.
Table of Contents
Company Background of iD Fresh Food
Nature and History of the Firm
Existing Business Models
FMCG Industry in India (Market Environment)
Key Issues and Problems
Defining the Companys Objectives
Methodology (brief description of the methodologies used in this paper)
Issues with Existing Data
Missing data (EE #2)
Outliers (EE #3)
Simple Moving Average – Alice
Weighted Moving Average – Jingjing
Exponential smoothing – Gary
Comparison of Models – Cuicui and Molly