Introduction of Our Client: Junyi Academy In this project, our client is Junyi Academy, a platform offering online learning resources for all ages. It provides practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. With high utilization ratio of the practice exercises, Junyi receives problems reported by users, which are called “user problem reports”. All the reports will be checked and then be distributed to the responsible team by operation team.
One of the largest challenges facing small and medium restaurants is that of competing against big chains with better information infrastructure – an advantage that allows the big chains to plan resource allocation more precisely based on demand and other factors. iChef provides this platform for small restaurants “making enterprise level technologies affordable and understandable for small restaurants”.
a. Problem Description
Our client is a German drug manufacturer who owns and operates six stores in different parts of
Germany. Each store has a unique layout and some stores are open for fewer days than others. Moreover, customer footfall varies by location. Our client contracts staffing personnel on a daily basis from a staffing agency for all the stores, and each contracted staff is paid on per hour basis every day. The
management in our client’s organization has been tasked with an objective to bring down the staffing
Problem Description: FMCG companies like Nestle face trouble in forecasting demand for smaller
regions which comprises nearly 50% of their business and is highly critical. This is due to high volatility in
demand. Due to this problem more often than not the sales force in these regions face a situation
wherein they are either short of inventory and unable to meet demand or have piled up inventory at
warehouses. A model that effectively forecasts sales can be tested on a small region (in this case
Improve capacity utilization of Maruti’s Manesar and Gurgaon plants by forecasting
future demand of Maruti cars and hence scheduling production.
Maruti has 2 manufacturing plants at Manesar and Gurgaon. Manesar plant has a capacity of
550k and Gurgaon plant has a capacity of 900k as of 2016. The production numbers for 2016
shows that Manesar plant produced 630k cars suggesting overtime at the plant, whereas
Gurgaon plant produced 678k cars only suggesting underutilization.
Ford Motor Company deals with a product portfolio that consists of three subcategories namely
cars, light trucks and heavy trucks. The lead time for manufacturing planning of any subcategory
is 12 months. Hence they need a forecast of the total auto sales in the US market for the next 12
months on a monthly basis. Some examples of vehicles in each subcategory are shown in the
appendix. Both domestic sales and exports are to be forecasted.
Our primary stakeholder is theater managers, an important role in theater who have to arrange released weeks and halls for each new movie. Therefore they have a potential need of knowing how new movies will perform on box office revenues. However, there’s a gap among the box office revenues in Taipei and in US and other movie features, and cause the prediction difficult. Hence, our business goal of this project is to allow managers knowing how new movies will perform on box office revenues in Taipei in advanced.
In the library, it has been a hard time for librarians to decide whether to purchase the replacement for a book that has been reported missing since some books may be found not long after the replacements have been bought. On the other hand, it would be irritating for us as students if books we’re looking for have been report missing for a long time without replacements.