DHL Parcel has an old model for notification to their customers. The purpose of the assignment was therefore to investigate how new or alternative methods can be used in a new model. The new model should be reliable and profitable.

Research and interviews were conducted to get an understanding of the present situation and to find ideas for a new model. Notifications by mail is the largest cost in the current model. Research and meetings with LINK Mobility showed that “Facebook Messenger” is the most developed method to use as a new notification method.

The new model presented in the results suggested that the mail being sent on day 7 should instead be sent on day 3. This by reading the list of deliveries of SMS and only send mail to customers not reached by SMS‐notification. The new model is calculated to reduce the yearly cost by 3 million SEK and high level of reliability.

At the end of the report, reflections on the work are presented. Recommendations to DHL for continued work on implementation plans and other necessary steps before an implementation of the new model are presented.

This is a report that aims to map qualified suppliers to Aqua‐Nova. The company's organization is small and consists of five employees. It is therefore a challenge to sell new projects, monitor production and care for customer relations and at the same time conduct development projects. The work is based on a technical report obtained by Aqua‐Nova. The report investigates a possible optimization of the distillation process using ejector technology.

The purpose of this thesis was to investigate the most suitable subcontractor of ejectors for Aqua‐Nova. The purpose was to also answer the four detailed objectives.

• What advantages and disadvantages are created by introducing an ejector into the system?

• What customer benefits does an ejector provide?

• What requirements are placed on the technology to work together with Aqua‐Nova's current system?

• What requirements are placed on suppliers in terms of competence, delivery ability and price?

Through Google searches, examination of Achema industrial fair and through interviews, eleven suppliers were found who met the requirements specification. With Pugh and Kesselring matrices, a two‐step screening process was performed. The British company Transvac Systems Ltd. got the highest score in both screenings and is thus considered the most suitable supplier. 

The screening process was supplemented by a competition analysis, a risk analysis and an interview to answer the detailed objectives. The competition analysis aims to investigate the pros and cons of the development project and shows that Aqua‐Nova can increase its market share by differentiating the product. The focus should be on the markets where cooling water is a scarce commodity and the companies that suffer from space shortages. The customer benefits become a more environmentally friendly and profitable product. The risk analysis shows whether there is a risk of implementing new technology in Aqua‐Nova's system. Based on the result, the risk of the ejector causing a downtime is minimal. The interview shows that a supplier must be able to deliver tailor‐made ejectors and how the ejector should be designed to work in the Aqua‐Nova system.

The result can be considered credible since the screening process was performed in two steps. The risk and competition analysis give a representation of the challenges the company can face with the development project.

The result ought to be supplemented by a cost analysis and developing of a test facility to ensure profitability and a functional product.

In human-robot collaborative manufacturing, industrial robot is required to dynamically change its pre-programmed tasks and collaborate with human operators at the same workstation. However, traditional industrial robot is controlled by pre-programmed control codes, which cannot support the emerging needs of human-robot collaboration. In response to the request, this research explored a deep learning-based multimodal robot control interface for human-robot collaboration. Three methods were integrated into the multimodal interface, including voice recognition, hand motion recognition, and body posture recognition. Deep learning was adopted as the algorithm for classification and recognition. Human-robot collaboration specific datasets were collected to support the deep learning algorithm. The result presented at the end of the paper shows the potential to adopt deep learning in human-robot collaboration systems.

This study aims to investigate the implications of supply chain (SC) complexity (based on static and dynamic complexity drivers) on constituents of resilience capabilities to deal with disruptions. A systematic qualitative analysis based on critical incident technique has been applied on secondary data collected on disruption incidents. Findings indicate that most of the complexity drivers have positive influence on several resilience constit-uents; however, negative effects were observed as well. Static SC complexity drivers seem to have both positive and negative effects on resilience, while dynamic complexity drivers seem to reinforce SC resilience.