The software solutions of CASCULATE leverage this progress to optimize the individual calculation for the quotation process within the manufacturing industry. Using historical data, the AI or ML system is recognizing patterns to predict the effort required for a specific production. The algorithms make use of historical data and individual expertise to calculate efforts faster and more efficiently, according to specific requirements.
To account for the specific characteristics of each factory, separate models are trained for each production facility. This is due to the unique setup of each plant, including specific workflows and machinery. For example, certain components may be produced efficiently in one facility but require significant effort in another. Additionally, workflows can vary significantly, which also affects the efforts and their prediction.
Machine Learning (ML). in simple terms, involves algorithms that create models capable of processing and analyzing large amounts of data. These models can then make predictions based on the existing data they have been trained on. This includes tasks such as object identification in an image, text translation, or product recommendations. Training an ML model relies on a sufficiently large dataset containing information about past events, observations, or other occurrences. The ML model learns the relationships between variables from this data. Based on these learned relationships, the model can make specific predictions on new data without explicitly programming all the rules for it.
CASCULATE utilizes ML to predict cost estimations. In general, ML requires large amounts of data, and if sufficient data is not available, additional rules are incorporated to narrow down the calculation field. This combination has proven to be a powerful tool for predicting trends, prices, and costs. Furthermore, using data science techniques, it is possible to artificially generate more data points for training the ML model. This enables achieving higher accuracy and compensating for data scarcity. ML excels at discovering relationships that may go unnoticed by humans. The models' learning capability improves over time, leading to more accurate predictions.
Artificial Intelligence (AI). Artificial intelligence encompasses a broader concept of machines or computer programs emulating human cognition, including reasoning, planning, problem-solving, and learning from experience. AI encompasses various techniques beyond ML, such as rule-based systems and logic programming.
CASCULATE benefits from the use of AI models. One significant advantage of CASCULATE's chosen AI approach is that it doesn't require massive amounts of data. With the ability to train different models for different factories, it becomes possible to capture the entire manufacturing process with a smaller dataset. The use of AI helps achieve high accuracy in predicting effort. Even with limited data, which is often encountered, the accuracy remains high due to the consideration of rules and premises specific to each factory's workflow in the algorithm.
Accuracy. plays a significant role in effort prediction. The AI algorithms are trained using historical data and framework parameters from the production environment to make more precise predictions about future production efforts. The chosen models can also be enriched with actual data and past production efforts, incorporating factory-specific factors as essential elements. By utilizing Casculate, factories can calculate their effort estimates more accurately and allocate resources more efficiently, leading to improved overall efficiency and productivity.
Repeatability. AI is capable of identifying patterns in data and can repeat similar predictions for similar parts. This is of great significance as similar components in a manufacturing process typically require similar effort during repeated production. This repeated prediction can be achieved using historical data, where AI holds a significant advantage over humans. Humans require extensive training and long-lasting experience to recognize such similarities, making the calculation experience-dependent. AI, on the other hand, can uncover these obvious as well as previously hidden patterns and utilize them to make the process of effort prediction repeatable. This enables even inexperienced employees to generate quick calculations.
Domain Knowledge. Casculate's system complements the daily work of experts and less experienced individuals in the factories. With the help of the software solution, it is possible to generate a baseline forecast for expected production efforts within a minimal timeframe, which would typically take longer when done manually. Based on the initial systematic calculation, a user can adjust the parameters and repeat the prediction. This allows for the incorporation of expert knowledge retrospectively to make the most accurate prediction possible.
In summary, ML and AI are powerful tools that enable the utilization of historical data and expert knowledge. These methods can uncover patterns in the data that may be difficult for a human to identify. As a result, the accuracy, repeatability, and speed of effort prediction are significantly enhanced.