Graduation: 3 Data Science assignments

Graduation: 3 Data Science assignments

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Job area Apprentice (JFG) Location Veghel, Netherlands. Contract type Full time
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Assignment type: Data Scientist

Start date: February 2022

Assignment duration: 6-9 months

Location: Veghel

Educational level: WO Master

Desired study: Data Science, Computer Science, Stochastic Operations Research or similar

Language: English

Job Description

Three assignments are described below from which two graduates can choose for the Process Optimization in DSF.

Description of the assignment

Assignment 1: EXTEND KNOWLEDGE GRAPHS TO PREDICT SYSTEM AND ITEM BEHAVIOUR

As a part of the ongoing research,  a knowledge-graph is implemented that can describe the process related to the airport baggage handling systems, relate it to the physical system layout, show the interactions between the different process steps, the durations between different process steps and some of the characteristics of the bags. The knowledge-graph can provide answers on where there were delays in system for a bag and relate to the system and bag characteristics around this delay. The goal of this graduation project is to extend the knowledge-graph to include more information, aggregate information.

Your task and responsibilities will ben to extend existing knowledge to include how unexpected and undesired behaviour like delays propagate through the system. Also to aggregate information to provide overview at a higher level of processes. And finally your job is to build a proof-of-concept and evaluate the feasibility to include this in existing products.

Assignment 2: PREDICTIVE MODEL(S) FOR OPERATIONAL PLANNING

Vanderlande customers using the automated Material Handling Solutions in Warehouses and Airports have two main needs namely to reduce the number of late bags or late orders and optimize the number of employees like operators that are required to operate the system. The optimization of these KPIs is key in determining the Total Cost of Ownership for the customer. The goal of this project is to build a solution that can predict the number of resources required to ensure the number of late bags or late orders are reduced given the historic data of plans and evaluation measures for the plans. This solution can be built in multiple ways like updating existing simulation models to replay data from actual system and extending it to forecast the future, building reinforcement learning algorithms or building machine learning models that could predict the required parameters.

The objectives of this graduation topic is to determine the resources that has an impact on the late bags or orders and impact the Total Cost of Ownership. And to evaluate the different technologies that can make better plans than existing plans and suggest the best technology to use for this optimization problem. At last to build a prediction model based on the best technology to build the solution to make a better plan

Assignment 3: DATA ANALYSIS AND VISUALIZATION TECHNIQUES FOR MULTI-ITEM PROCESSES

Vanderlande offers warehouse solution projects like STOREPICK. In these solutions the items move through the system as different item types. Items arrive to the Warehouse as a part of pallets, in the warehouse the items are made into smaller units and placed in trays that transport the items in the system, and finally the items are put together to fulfil an order onto a roll cage or a pallet. The aim of this graduation project is to find the best representation of this information that can used to do a root cause analysis when an order is not fulfilled on time. The items of the same category for example all Coca Cola have the same item identifier which is the SKU. The pallets, roll gauges and trays have their own identifiers. Any tray with a given SKU can be used to fulfil an order that required that particular SKU.

The objectives of this graduation topic is to determine the resources that has an impact on the late bags or orders and impact the Total Cost of Ownership. And to evaluate the different technologies that can make better plans than existing plans and suggest the best technology to use for this optimization problem. Your last task will be to build a prediction model based on the best technology to build the solution to make a better plan

Department

The Digital Service Factory (DSF) is a new department being established within the Technology department (former R&D). The DSF develops data-driven digital services – such as a customer portal, connected systems, performance reporting and predictive maintenance – in close cooperation with the Vanderlande Business Units (BU’s) and its customers. The department is organized around dedicated multidisciplinary product teams who work closely together with other teams from Technology, ICT, BU’s and external partners. Key in this are service design thinking, agile way of working and a relentless focus on data and insights.

Your profile

  • Knowledge of Graph Databases is a plus

Contact

Do you recognize yourself in this challenging profile? And are you looking for a graduation assignment in an organization that has been elected as “Best Employer” for years in a row? Please fill out the application form and upload your resume and cover letter. For more information, contact us by e-mail: internship@vanderlande.com or Stef Alferink (Campus Recruiter) by phone: +31 (0)413 – 75 50 87.

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