Zhiyuan Hu

1.Analyze the company's historical data and compile historical sales patterns.

2.Understand the company's needs for an automatic replenishment strategy, suggest possible solutions to achieve it based on the needs, and analyze and discuss its feasibility.

3.Establish a computer model based on the logic of the automatic replenishment strategy and use the computer to simulate the actual situation of the replenishment strategy

4.The company conducts trial runs in selected shops to obtain actual results.

5.Import the company's trial run data and compare the results from the simulation to verify the correctness of the computer model.

6.Field research to find out how well shop staff accept the computerized automated replenishment.

7.Study the problems with the current replenishment model based on the new data obtained and revise the replenishment strategy to achieve stable results. This resulted in a 15% reduction in logistics costs and a 12% increase in order service rates for the company

1.Act as team leader and coordinate the work of all parties. Arrange reasonable plans under tight deadlines and control progress under pressure.

2.Analyze current warehouse distribution characteristics and complete data classification and analysis based on the placement characteristics of different SKUs using Excel.

3.Design algorithms in Python to calculate SKU correlations such as complementarity and

mutual exclusivity between SKUs.

4.Generate 2D coordinates of the warehouse based on current warehouse shelf placement patterns.

5.Complete the SKU location recommendation algorithm using simulated annealing algorithm to generate a thermodynamic chart.

6.The newly generated warehouse location recommendations can reduce forklift operation time by 23%, thus reducing logistics costs and increasing efficiency.