代写QBUS 6310 Business Operations Analysis Assignment 2代写数据结构语言程序

日期：2024-10-14 05:34

QBUS 6310

Business Operations Analysis

Assignment 2

Group Case Study

20% of total mark

Due: Fri Oct 18, 2024 23:59

Part 1. Assignment information

– Group assignment

– Group

– Self-enroll in the groups at "People"--"Groups". You can post on Ed to find members to join. Each group has no more than 5 students.

– Task

– Write a PPT to report your recommended solutions.

– Clearly explain the network design and assortment choices.

– Submission

– Appoint a representative to submit the PPT and Excel file.

– Page limit is 30, including title page and references.

– The PPT needs to be well-written and suitable for corporate presentation (i.e., addressing to the case company not the academic staffs).

– Logical flows (such as title page, group member page, overview, transition, conclusion pages) are expected.

– Your PPT should contain a group photo.

– Feel free to use generative AI to assist you in drafting the PPT. Relevant pictures can increase the realism.

– Assortment and facility location joint optimization Information gave in lecture week 5

– We break the model into two stages.

– First, solve the universal assortment model (apply greedy algorithm)

– Week 5 lecture

– Second, use the expected demand and assortment as inputs for a facility location model (4 candidate sites for warehouses).

– Week 3 lecture

– Third, write a PPT to explain your solution in such details that the case company can act upon your suggestions.

– 带有 Pitch 性质

– The case is roughly based on a retail chain store in Shanghai.

– The GPS data is shown but some of the distances are already computed in the matrix. (You compute the missing data using a link recommended.)

– The cost data is modified due to business confidentiality.

– The assortment data is artificially created for the testing purpose.

Part 2.

Background of the Case Company

Founded in 1989, G company started with only one product—baby stroller. Over the past 30 plus years, G company expanded its product portfolio to include: cots, highchairs, children’s clothing, and nursing supplies. In 2024, G company emerges as a well-known brand in Shanghai, China.

At present, 84 stores are being supplied from a regional warehouse, from which the transportation time is 24 hours. In addition to the transportation time, each order requires picking, packing, and moving activities such that the total lead time for each order is roughly 72 hours (from ordering to receiving the goods). The transportation cost from the regional warehouse to stores is estimated to be CNY 6 per kilometer per product.

G company plans to carry out a “520” pilot project: 5 hours’ maximum replenishment time, 2 replenishments a day, and 0 out-of-stock situations. The pilot project requires upfront warehouses serving as buffers for product flow from the regional warehouse to stores. The entire system will install an automatic ordering system that monitors the in-store inventory levels in real time.

The pilot project will deploy to 10 out of 84 stores in Shanghai. The focus is on children’s clothes, of which the assortment contains 5 variants shown in the following Table 1.

Table 1: Five Variants

Variant	Attractiveness	Profit Margin ($)
A	4.8	35
B	3.6	30
C	2.4	30
D	2.4	35
E	1.2	40

G company currently is offering all five variants in their assortment. Ms Yan is leading the project and is thinking about whether to adjust the assortment. As the expectation of zero out-of-stock to be met, the mean demand at each store will be used for facility location planning. However, the impact of adjusting the assortment needs to be considered. The attractiveness of no purchase is scaled to be 1.

The transportation cost from the regional warehouse to the chosen upfront warehouse(s) will be excluded. For Tasks 2 and 3, the facility location model must minimize the total costs based on the mean demands at each store.

The transportation cost from any upfront warehouse to store is estimated to be CNY1 per kilometer per unit. There are 4 candidate locations to open an upfront warehouse.

Table 2: Candidate Locations for Upfront Warehouse

Candidate	Hong Kou (North)	Chang Ning (West)	Min Hang (South)	Pu Dong (East)
Equipment	317,718	244,845	316,641	221,649
Overheads	1,543,065	905,190	1,542,957	1,049,409
Fixed Cost (Total Annual)	1,860,783	1,150,035	1,859,598	1,271,058
Latitude	31.267	31.115	31.222	31.223
Longitude	121.504	121.382	121.424	121.544
Daily capacity if open	500	300	500	350

The second to fourth rows are measured in CNY while the fifth and sixth rows are GPS data.

The 10 stores in the pilot project are the following.

Table 3: GPS Data of Stores and Their (Daily) Average Store Traffic

Store ID#	Average Traffic	Latitude	Longitude	Hong Kou	Chang Ning	Min Hang	Pu Dong
4	131	31.229	121.517	4	18	9	3
72	91	31.224	121.446	7		2	9
5	77	31.237	121.474	4	16	5	7
65	61	31.133	121.402	18	3	10	17
56	60	31.237	121.500	3	18	7	4
79	58	30.916	121.484	39	24		35
33	58	31.217	121.409	11	12	2
14	57	31.233	121.412	10		2	13
57	52	31.302	121.515		24	12	9
37	52	31.235	121.477		16	5	7

Task 1) Use the calculator on www.nhc.noaa.gov/gccalc.shtml to compute the distance in kilometre. Some numbers are already filled. (Hint: As the GPS data is rounded in three decimal places, the calculator will produce a distance in integer. Feel free to double check the pre-filled numbers.)

Task 2) Develop a facility location model by assuming that all variants are included in the assortment. The mean demand for each location equals the average traffic multiplied by the choice probability of purchasing from the offered assortment. Use these mean demands as the inputs for the facility location problem. Solve the facility location problem using Excel.

Task 3) Develop a two-stage model. Stage 1: Apply the greedy Algorithm 2 (which has been discussed in the lecture) to choose a universal assortment for all stores. Stage 2: Compute the new probability of purchasing (the total probability of purchasing from the offered assortment). The mean demands are then updated as the following: the new probability multiplied by the average traffic of the store. Solve the facility location problem again. The second-stage problem is a network design problem (facility-location model). The objective is to minimize the total cost of the supply chain.

Task 4) Contrast the solutions for Tasks 2 and 3 to determine the impact of adjusting the assortment.

Task 5) If we conduct a full search of all the possible assortments (rather than using the greedy algorithm 2), will the optimal solution change?

Write a PPT to explain your recommendations to Ms Yan. Submit your Excel file as the accompanying evidence. (Page limit is 30, including title page and references.)

A supply network means which upfront warehouses are chosen and which warehouse will supply to which store. You can present this network using a table (or map with arcs) with expected delivery quantities.

Hints:

- To increase the realism of your PPT, you can add photos of products or map of Shanghai city. Video is too large for uploading and downloading and hence is banned.

- The traffic in Table 3 is daily and hence, the annual income is the daily income multiplied by 365.

- Daily income of each store equals the average daily traffic multiplied by the “single-customer” income from the assortment model.

- Transportation cost also incurs daily while the fixed cost is annual.

- About the multiplication models: if an assortment attracts 90% of customers at store 4, the expected demand at store 4 equals 131*0.9, which will be used in the facility-location model; if an assortment generates 40CNY per customer on average, the daily net income of store 4 equals 131*40.