Mitigating Barriers on Artificial Intelligence Pre-adoption in Forecasting : A case Study in a Manufacturing Firm

Introduction: To predict the future, how a coming day, week, or month will look has become even more crucial than ever for a firm, due to recent pandemic crises, and wars. Being able to predict the future will enable firms to reduce costs and increase time efficiency. Processes such as forecasting have been at the forefront to aid managers in these matters by improving decision-making and planning. Greater forecasting capabilities have been achieved by adopting technologies such as Artificial Intelligence (AI). As it has shown to aid practitioners in predicting the future with high accuracy. Thus, leading to improved decision making and planning. Problem discussion: AI is still in its infancy, and technology adoption is a staged-based process. More research is needed to identify the potential barriers a firm faces when looking to adopt AI into their forecasting process. As well as how these barriers are mitigated, and what barriers are relevant depending on the stage of adoption. Purpose and Research question: The purpose of this study is to investigate the barriers of AI pre-adoption in forecasting and how these barriers are mitigated. To answer the following research question: How does a manufacturing firm mitigate AI pre-adoption barriers in the forecasting process? Method: First, a scoping review is conducted to identify barriers in AI adoption with the support of the TOE framework, (Technological, Organizational, and Environmental). Later, the thesis follows a qualitative approach, conducting a single case study. The primary source of empirical data was collected from five in-depth semi-structured interviews. The data is collected from an international manufacturing firm located in Sweden that is looking to adopt AI-ML into its forecasting process. The findings collected from the firm are later discussed with an expert in the field of AI and forecasting to further bring validity and input to the findings. Findings: Organizational readiness, Top management, Poor data, Inappropriate technology infrastructure, and Partnership were identified as key barriers in the AI-ML pre-adoption for the forecasting process. The barrier could be mitigated by building a strong business case, creating managerial awareness and understanding, interactive data platform, comprehensive dataset, and incentives. Conclusion: The study provides theoretical contributions as well as managerial implications. By shedding light on the barriers in the pre-adoption phase and providing insight as to how to mitigate the barriers. Future research is recommended to study the same phenomena at another firm

Mitigating Barriers on Artificial Intelligence Pre-adoption in Forecasting : A case Study in a Manufacturing Firm

Introduction: To predict the future, how a coming day, week, or month will look has become even more crucial than ever for a firm, due to recent pandemic crises, and wars. Being able to predict the future will enable firms to reduce costs and increase time efficiency. Processes such as forecasting have been at the forefront to aid managers in these matters by improving decision-making and planning. Greater forecasting capabilities have been achieved by adopting technologies such as Artificial Intelligence (AI). As it has shown to aid practitioners in predicting the future with high accuracy. Thus, leading to improved decision making and planning. Problem discussion: AI is still in its infancy, and technology adoption is a staged-based process. More research is needed to identify the potential barriers a firm faces when looking to adopt AI into their forecasting process. As well as how these barriers are mitigated, and what barriers are relevant depending on the stage of adoption. Purpose and Research question: The purpose of this study is to investigate the barriers of AI pre-adoption in forecasting and how these barriers are mitigated. To answer the following research question: How does a manufacturing firm mitigate AI pre-adoption barriers in the forecasting process? Method: First, a scoping review is conducted to identify barriers in AI adoption with the support of the TOE framework, (Technological, Organizational, and Environmental). Later, the thesis follows a qualitative approach, conducting a single case study. The primary source of empirical data was collected from five in-depth semi-structured interviews. The data is collected from an international manufacturing firm located in Sweden that is looking to adopt AI-ML into its forecasting process. The findings collected from the firm are later discussed with an expert in the field of AI and forecasting to further bring validity and input to the findings. Findings: Organizational readiness, Top management, Poor data, Inappropriate technology infrastructure, and Partnership were identified as key barriers in the AI-ML pre-adoption for the forecasting process. The barrier could be mitigated by building a strong business case, creating managerial awareness and understanding, interactive data platform, comprehensive dataset, and incentives. Conclusion: The study provides theoretical contributions as well as managerial implications. By shedding light on the barriers in the pre-adoption phase and providing insight as to how to mitigate the barriers. Future research is recommended to study the same phenomena at another firm

Solidmechanical simulation of high pressure hydraulic couplings

Hydrauliska högtryckskopplingar av typen FEM ½” studeras med avsikten att fastställa en effektiv beräkningsmetodik som kan användas till att prediktera kopplingarnas hållfasthet. Metodiken utgörs av finita element analyser (FEA), och valideras av experimentella trycktester utförda på kopplingstypen FEM ½”. Genom FEA kan kopplingarnas hållfasthetsbeteende och maximala belastningskapacitet studeras virtuellt, vilket minskar behovet av experimentella tester och medför potential för optimering av produkterna. Arbetet utförs på Parker Hannifin AB i Skövde. Experimentella tester utförs på 20 stycken kopplingspar av typen FEM ½” för att utöka förståelsen av kopplingarnas beteende under brottsförloppet och för att prediktera trycket som medför haveri. Testernas genomförande och struktur baseras på metodiken Design of Experiments (DOE). Kritiska komponenter identifieras utifrån experimentets resultat, vilka sedan studeras närmare via FEA. Analyserna valideras utifrån standarder som kopplingarna ska efterfölja, och mätdata insamlad under de experimentella testerna. Från de experimentella testerna är det komponenterna: kulhållaren, styrningen och nippelhuset som upptar belastning i störst utsträckning. Vid haveri framgår två brottmoder som vanliga, att kulhållaren slits isär samt att styrningen brister, båda fallen uppkommer vid approximativt samma tryck. FE-analyserna för styrningen och kulhållaren visar god överensstämmelse med experimentella resultat. Deformationerna skiljer sig dock mellan analyserna och de experimentella testerna, var nippelhusets analyser uppvisar störst avvikelser. FE-modellerna uppvisar god potential för att prediktera samt utvärdera kopplingarnas mekaniska beteende under tryckbelastning. Analyserna är dock helt beroende av ingående data, var saknaden av en verklig materialmodell medför avvikelser från experimentella resultat. Förhållandet framgår tydligt av nippelhuset, vars relaterade härdningsegenskaper saknas.Hydraulic FEM ½" high pressure couplings are studied with the purpose of establishing an effective methodology that can be used to predict the strength of the couplings. The methodology consists of finite element analyzes (FEA) and is validated by experimental pressure tests, performed on the FEM ½” couplings pairs.  Using FEA, the couplings solid mechanical behavior and maximum load ability can be viewed virtual, reducing the need for experimental tests and gives the potential for optimized products. The work is performed at Parker Hannifin AB in Skovde. Experimental tests are performed on 20 FEM ½” couplings pairs, to understand the solid mechanical behavior of the couplings until failure occurs, and to predict the maximum pressure that can be applied. The experimental structure and performance is based on the method Design of Experiments (DOE). Critical components are identified based on the results from the experimental tests, which are then studied more closely through FEA. The analysis are validated based on the applied material model, and data collected during the experimental tests. From the experimental tests it is shown that the components: ball cage, guide and plug housing are the components in which failure occur. In case of failure, two failure modes appear as common, that the ball cage is worn apart and that the guide burst, both types of failure modes occur at a similar pressure. The analysis for the guide and ball cage corresponds with the experimental outcomes. Differences occurs however when looked at the deformations, in which the plug housing shows the largest deviation when compared to the experimental results. The usage of FE-models appears to be appropriate for predicting and evaluating the mechanical strengths of the couplings during pressure loads. The analysis are however entirely dependent on the input data, where an incorrect material model generates incorrect results. The relationship is shown for the plug housing, which lack the mechanical properties related to curing processes