Intelligent Replenishment Order Adjustment
|Company Name / Department||
Ching Hong Ng (Max)
Budapest Hungary / The Netherlands
|Start Date||April 2022|
|Housing arranged by company|
Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices
Fulfillment of replenishment orders are always delayed or cancelled due to unexpected factors, such as supply or logistics delay, pandemic, produciton delay etc. And all these leads to constantly adjusting replenishment order by planner manually (eg. Changing goods arrival date, shipping method etc.). On the other hand, additional constrains such as supply gap, supply cost, inventory target and transportation capacity etc. have to be taken into account during the adjustment.
Problem to solve:
- Unexpected factors happen in every critical point along our work flow such as delayed delivery, accident in production, raw material shortage, customer order request date change etc. All these make our replenishment orders suboptimal, and frequent adjustment is needed manually by planner which is very labor intensive.
2. Simple recommend can be given by current system, however, the factors took into account for recommendation is very limited (consider weekly supply and demand only). In reality, more constrains have to be considered. Inventory cost, logistics cost, supply/logistics limitation, the priority of customer orders etc. have to be taken into account and this means that the solutions recommended by our system are not optimal, or even infeasible in certain situations (eg. the system recommends shipping instead of by air despite the port has been shut down due to pandemic).
Goals of the Project
Developing an algorithm and model to find the appropriate method to recommend best adjustment solutions which consider key KPIs (inventory cost/logistic cost) and other constrains such as supply capabilities, manufacturing/logistic capacities etc.
Also, algorithm/model can intelligently determine whether the recommended solutions should be applied automatically or not
- To be discussed
Essential Student Knowledge
Operation Research background, knowledge in operation optimization, knowledge in linear and nonlinear optimization
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