Evaluating Automation Investments : Integrating Cost-Benefit Analysis into theDynamo++ Decision-Support Model

In today’s manufacturing landscape, automation is widely recognized as a key driver of productivity, efficiency, and competitiveness. However, despite its potential companies often face challenges justifying such investments due to limited integration of financial analysis in existing decision-support tools. While the Dynamo++ framework provides structured evaluation of Levels of Automation (LoA) across mechanical and informational dimensions, it lacks a financial component essential for strategic investment decisions. This thesis addresses that gap by integrating Cost-Benefit Analysis (CBA) into the Dynamo++ method. A mixed-method case study was conducted at a Swedish truck manufacturer, focusing on two assembly stations. The study combined Dynamo++ with the Analytic Hierarchy Process (AHP), Benefit Change Scoring (BCS), and CBA. Data was collected using a mixed method approach. AHP was used to prioritize operational criteria, while BCS provided scenario-specific benefit scores. These were compared against investment costs using CBA. The results demonstrate that the integrated framework supports more balanced automation decisions by combining operational priorities with financial reasoning. Some lower-cost automation scenarios delivered strong benefit-to-cost ratios, while others with higher costs showed limited added value. Overall, the study demonstrates that combining Dynamo++ with CBA and supporting tools results in a more holistic and ransparent evaluation framework. This integrated approach bridges the gap between technical analysis and financialjustification, offering practical value for companies planning future automation investments in manufacturing environments.