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Stock level optimisation model and algorithm development using data

MTO Electric

Company description

MTO Electric A/S is a privately owned trading company founded in January 2002 and owned by Michael Johansen and Mads A. Velbæk. Pr. We stock a large range of enclosures and accessories as well as electrical components and niche products widely used by customers within the electrical industry. In addition to our large standard program consisting of more than 40,000 item numbers, we offer customized products made in-house or ordered directly from our flexible suppliers.

We operate exclusively in the B2B market. Our main customers are the Original Equipment Manufacturer (OEM) industry, panel builders, wholesalers, electrical workshops and service companies. Our core competencies are enclosure solutions as well as electrical components for power controls. Company’s products can be ordered from MTO Electric directly via a telephone call, our e-shop and sold through a nationwide network of dealers and wholesalers.

On January 1, 2014, MTO Electric took over the activities in EL: MATIK electrical equipment and hired former owner Klaus Hansen. The staff consists of 16 highly qualified people, who together with more than 50 international suppliers can offer a wide range of solutions and know-how. 

Project description

Currently, MTO Electric has a product catalog consisting of 40,000 Stock Keeping Units (SKUs) out of which 10,000 are stored in our warehouse and the remaining 30,000 can be ordered through us directly from our suppliers’ warehouses.  Having such a variety of  SKUs with different demand levels and different reorder lead times, we have a hard time keeping optimal stock levels. In particular safety stock and reorder levels might be unreasonably high which in general business sense locks in our cash and increase operational costs. Therefore, we want to develop an inventory management strategy based on just-in-time philosophy.

There are also additional considerations to be taken before manipulating the data. We have agreements with some of the customers to keep a certain level of stock for some particular parts. Other customers have regular bulk purchase patterns without any agreements. Yet other bulk purchases represent just a single random event. All of these scenarios need to be identified, considered and right stock levels found accordingly.

We would like you to:
  • Understand and consider different arrangements we have with our customers and prepare data for optimisation by removing “untypical orders”e.g. high volume/frequency as well as classifying regular customers (by contract or by historical data);
  • For each SKU computationally determine demand level, safety stock level, reorder point and quantity;
  • Analyse which SKUs should be kept in stock, which should be kept at the suppliers’ warehouse and finalise overall inventory strategy and model.
Our logistics team together with external IT company providing system support will give you access and required information from our ERP system with 5-years of historical orders data including an order frequency, volume, delivery time, etc. Data is well structured and prepared for data manipulation and analysis.

Expected outcome:
The outcome of this project is an algorithm (model) which allows us to optimise inventory levels. The algorithm (model) should ensure the correct level of safety stock and optimal purchasing volumes to reduce inventory binding, ensure delivery performance to customers and at the same time reduce order and shipping costs. In the final presentation, we would also like to see an example of your models’ processed inventory data and insights as well as considerations you made while making a model. The findings of the project will be presented to the key company’s stakeholders and your model is expected to be implemented in our daily operations afterwards. Implementation is not a part of this projects’ scope.

Student description

We expect thesis student(s) having a background in logistics, supply chain, data analytics and interest in business optimisation challenges. Prior experience working with Inventory management, warehousing, and stock level optimisation would be highly advantageous. Since the project has a significant emphasis on Data, we would expect you to be competent and have strong data manipulation and analysis skills.  Students would have a chance to solve a real-business optimisation challenge and a possibility to contribute to the way inventory will be managed afterwards.

To support you through this project, we will provide access to data from our ERP system. You will also communicate with our general manager and logistics manager who would be able to answer your questions if needed.

The applications will be reviewed on an ongoing basis and we will close once the right candidate or a group is found. For any questions related to the project, you are welcome to contact Kris at zibutis@matchmythesis.comelow you should describe students’ profile and practicalities. 

Stock level optimisation model and algorithm development using data | Match My Thesis
Feb 06, 2020


Stock Optimisation

Inventory Management

Safety Stock

Reorder Point

Data Analysis

Purchasing Volume