# Artificial Intelligence Software Adoption in Manufacturing Companies
## 3 Methodology
### Variables  
This section describes the operationalization of the variables used in this study. The dependent variable represents the use of AI and the independent variables represent the level of readiness for Industry 4.0, company size, role in the supply chain and technology sector. It was hypothesized that these independent variables influence the adoption of AI in manufacturing companies.  
Let us start with the explanation of the dependent variable: AI usage. This is derived from the responses to the EMS questions \"Do you use specific software solutions for the following six production areas?\" and \"Does the software offer self-learning functionality/capability?\" The six production areas indicated were as follows:  
1. Management of production processes (e.g., process monitoring).
2. Quality control (e.g., defect detection).
3. Maintenance of machinery and equipment (e.g., condition monitoring).
4. Management of internal logistics (e.g., warehouse, transport, etc.).
5. Energy management.
6. Improvement or innovation of production processes.  
Participants answered \"yes\" or \"no\" for each area, indicating whether the software they use has self-learning capabilities that are considered indicative of AI. An initial descriptive statistics analysis revealed that some companies use AI software in more than one production. Therefore, for the purpose of this study, the total number of AI software solutions was calculated (sum of all \"yes\" answers for the different production areas) and a company was classified as an AI user if it reported using at least one AI-enabled software solution in any of the listed production areas.  
Once the dependent value had been correctly coded, we continued with the independent variables. For the variable \"company size\", the categories small (1), medium (2) and large (3) were used and the companies were categorized according to the number of employees in each category.  
When coding the variable for the company's role in the supply chain, the question and corresponding answers were used, as follows: \"Regarding your main line of products, is your factory predominantly a producer of finished products or a supplier of systems or components?\". The possible answers were:  
* Producer of finished goods--for consumers.
* Producer of finished goods for companies--capital goods.
* Producer of finished goods for companies--operating resources, other products.
* Supplier--parts/components supplier.
* Supplier--system supplier.  
Participants were categorized as an \"OEM producer\" if they ticked any of the answers that related to producing finished goods and as a \"Supplier\" if they ticked only answers that related to suppliers. Participants who ticked answers that included both the OEM production and supplier roles were categorized as \"Both\". This categorization was then used in our analysis.  
For the high-tech classification, a glossary was used to determine the technology intensity of the companies. In the questionnaire, companies reported their two-digit NACE classification, which was then used to categorize companies into four levels of technology intensity: low, medium-low, medium-high and high.  
Finally, the companies were categorized into different levels of I4.0 readiness according to the Fraunhofer Industry 4.0 Readiness Index.  
Table 1 summarizes the variables that were used in our study.  
We used descriptive statistics and logistic regression to analyze the data. The descriptive statistics gave us an overview of the data in our sample. The frequency analysis helped us to understand the distribution of the data within the different categories of the variables. Logistic regression was used to model the relationship between the likelihood of AI adoption (dependent variable) and various predictors such as company size, role in the supply chain, technology intensity and level of Industry 4.0 readiness (independent variables).