Demand planning optimization of combined forecasting at Den Hartogh
|Company Name / Department||Den Hartogh Logistics|
|Contact Person||Niels Lobbes|
|Location||Rotterdam, The Netherlands|
|Optional remote work||Max. 2 days|
|Travel expenses (own account or reimbursed by the company)||No|
|Housing arranged by company||No|
|Housing expenses (how much per month, own account or subsidized by the company)||No|
|Internship compensation||€500,- per month|
|Study program||Operations / Supply Chain Management|
September 1, 2023
Den Hartogh Logistics is one of the leading Logistics Service Providers. The family-owned organization was established in The Netherlands in 1920. As a bulk logistics service provider for the chemical, gas, polymer and food industry, we combine the best elements to create the optimal solution for each situation. Safety and operational excellence are embedded in our culture.
Den Hartogh has a presence in every region of the world, with premises/offices in 50 locations within 27 countries. Our workforce consists of more than 2,100 people and our modern equipment includes more than 24,000 tank containers, 5,750 dry bulk containers and specialized dry bulk trailers, 300 tank trailers and 650 trucks.
From our four business units, this project will take place in business unit Liquid Logistics Europe. This business unit is responsible for all the intra-European liquid chemical activities.
The business unit Liquid Logistics Europe faces a challenging business environment with volatile market conditions and complex global operations. To manage volatility and mitigate supply chain risks, we integrated a Sales & Operations Planning (S&OP) process.
As part of the planning process, we developed a combined forecasting method to obtain insights on the expected customer demand in the short-term forecast horizon. This forecast is part of our operational workflow and crucial to balance our customer demand with the available capacity on hand.
Unfortunately, we observe unexpected fluctuations in the forecast predictions with a low accuracy as a result. Also, the weighting of the forecast methods is static throughout the week and does not consider the probability of order cancellations and alterations.
In this project, we would like to investigate if we can improve the short-term demand forecasting and planning by optimizing the weighting of the forecasting methods and introducing cancellations and alterations.
Goals of the project:
Investigate how to improve the short-term demand forecasting and -planning by optimizing the weighting of the forecasting methods and introducing cancellations and alterations.
MSc thesis report
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