The gaining momentum of AI implementation in manufacturing calls for a reflection on how to effectively integrate this technology into processes and embrace business value coming from optimizing production. Steps such as understanding AI’s power, building a strategy aligned with business goals, and developing AI algorithms are crucial to successfully adopting AI and overcoming emerging challenges.
SwissCognitive Guest Blogger: Agnieszka Maciąg – “Leveraging the Potential of AI in Manufacturing: How to Implement and Scale Up?”
Since quite a while, AI stopped being just a buzzword. Propelled by introduction of ChatGPT, more and more organizations are looking for opportunities how to leverage this promising technology in order to drive business value. In this case manufQacturing industry is also not an exception. According to Statista, the adoption rate of artificial intelligence is expected to grow in manufacturing and supply chain industries from 2022 to 2025. In 2022, only 11% of senior executives reported that AI is critical and 34% expected a widescale adoption of this technology, respectively in 2025 these numbers increased to 38% of respondents claiming AI criticality in successful manufacturing operations and lowered down to 30% on widescale adoption.
While these numbers are still relatively low, there is a hope for an increase in technology adoption in the upcoming years. However, there is a debate on how to achieve that. Below, there is a comprehensive guide on how to effectively integrate AI into manufacturing.
1. Understand AI Power
Among AI capabilities, in 2022, it was mostly leveraged in technologies such as Robotic Process Automation (39%) and computer vision (34%). Rethinking how these can be used in the manufacturing spectrum, one can point out automating manual repetitive processes within production planning – handling production orders, booking transportation, or extracting data from purchase orders and invoices with the use of OCR. While automating production processes is the baseline for eliminating human non-value-added work, AI can bring business to the forefront of innovation and allow for production risk elimination and building successful forecasts for demand to fully optimize manufacturing levels.
2. Build a strategy
With limitless growth opportunities, the second step after grasping AI capabilities should be to fully understand business needs and connect them. A good AI integration strategy should ensure improving employee satisfaction, in addition to, minimizing product waste, and optimizing overall production. It should also go in line with the main business strategy and serve as a support to achieve goals and initiatives.
The first step in devising an artificial intelligence strategy should be to set objectives and determine AI algorithms that are relevant to manufacturing needs. Whether it should be machine learning, computer vision, or natural language processing (NLP), each aspect should reflect factors such as data availability, computational resources, and know-how in the company in order to implement and provide maintenance of AI systems.
3. Investigate data sources and prepare
Effective use of AI starts with having profound sources of data which can be leveraged for algorithms’ training and decision-making. In the manufacturing reality, data collection can include technologies such as IoT devices and sensors within the production line or just historical records and manual inputs. The important step should be to clean and preprocess data to eliminate any outliers and inconsistencies which can disrupt the final result of AI models. Due to security issues, it is crucial also to implement safety measures to protect sensitive information and ensure data privacy.
4. Develop AI algorithms
After devising an AI strategy and selecting data sources, appropriate methodologies and algorithms should be selected to develop AI models tailored to the identified manufacturing needs. Some of the most commonly used algorithms in manufacturing include supervised learning (used for predictive tasks and classification), unsupervised learning (spotting patterns and data structures on unlabeled datasets), or reinforcement learning (usually used in optimization tasks or resource allocation). The selection of the algorithm should rely on the data structure available and set objectives and goals. To achieve the best accuracy, it is important to iterate and refine the parameters of the AI models using both historical and current data with the help of data scientists, AI experts, or external partners in case of a lack of AI expertise within the organization.
5. Integrate and monitor
Once AI models are selected, it is time to integrate them with the existing IT systems and processes in place. This step should be achieved by a close collaboration of IT business partners and operations to ensure streamlined data flow between the production environment and devised AI recommendations. Before any move to production, thorough testing and validation are required to sustain the reliability of the systems and don’t cause any disruptions in production. After passing User Acceptance Testing (UATs), the baseline is to introduce standard protocols, establish thorough documentation to facilitate transparent communication in the future, and allow implementation improvements. All this should be also supported by data analytics tools such as BI reporting to gain insights into system accuracy, and performance, and track any errors or anomalies.
6. Look out for scale-up opportunities
Ensuring successful implementation of AI in such a complex industry as manufacturing is relying on continuous improvement of AI models by updating and retraining them to improve their accuracy, in addition to, regular upskilling of the employees to promote digital skills and ensure trust in technology eliminating the fear factor of being replaced by “robots”.
Scale up and experiment
As with most business activities, organizations should investigate how they can scale up utilized algorithms across the company. This can include identifying similar processes or needs in other markets, departments, or business areas. However, there shouldn’t be fear of the adoption of AI – in some cases, it can be implemented as a small pilot project which will validate whether the use of such technology is profitable and brings value.
In conclusion, the manufacturing industry has great potential to leverage artificial intelligence capabilities to aid human workers in various manufacturing activities. However, the implementation of such technology also brings challenges concerning upskilling technical and nontechnical employees, in addition to, deriving the most effective results from the models. Nevertheless, above-described guide should point a direction on how to start off with integrating AI into daily manufacturing activities.
About the Author:
Agnieszka Maciąg is a professional specializing in artificial intelligence and automation in the realms of manufacturing and supply chain. With a strong foundation in consulting, where she gained perspective on business needs and optimization opportunities, she is right now a manager of Automation and Digitization in one of the leading FMCG companies.