Smart Operations: Embracing AI for Efficiency and Growth

Author: Antti Pohjolainen, Codento

As mentioned in the previous blog post, AI is not just a technological leap; it’s a strategic asset, revolutionizing how businesses function, make decisions, and serve their customers. This also holds true for the domain of operations, where AI is poised to revolutionize traditional processes, driving efficiency, enhancing productivity, and paving the way for sustainable growth.

Unlocking the Potential of AI for Operations

AI’s impact on operations extends across various facets of business, including:

Predictive Maintenance: AI algorithms can analyze vast amounts of data, including sensor readings and historical performance records, to predict equipment failures before they occur. This proactive approach minimizes downtime, reduces maintenance costs, and enhances overall asset utilization.
Smart Scheduling: AI-powered scheduling solutions can optimize resource allocation and task assignment, ensuring that employees are matched with the right tasks at the right time. This leads to improved productivity, reduced overtime costs, and improved employee satisfaction.
Supply Chain Optimization: AI can analyze demand patterns, identify disruptions, and optimize inventory levels, resulting in a more efficient and responsive supply chain. This translates into reduced costs, improved delivery times, and enhanced customer satisfaction.
Risk Mitigation: AI can monitor operational data and identify anomalies or patterns that could indicate potential risks. This allows businesses to take preemptive action, avert costly incidents, and protect their assets and reputation.

Codento has been working together with some of the Finnish forefront companies in manufacturing to implement AI in their operations. Take Fastems for example where Codento implemented AI-powered Smart Scheduling and predictive maintenance capabilities. For more information, please see our reference case stories here and here.

The Journey Towards Smart Operations

Implementing AI in operations requires a strategic approach that considers the specific needs and challenges of each organization. Key steps include:

Identifying Pain Points: The first step is to identify areas where AI can bring the most significant benefits, such as reducing costs, improving efficiency, or enhancing decision-making.
Data Preparation: High-quality data is essential for AI to function effectively. This involves cleaning, organizing, and standardizing data to ensure its accuracy and reliability.
Model Development and Deployment: AI models are developed using machine learning algorithms that train on the prepared data. These models are then deployed to production environments to automate tasks and provide insights.
Continuous Monitoring and Improvement: AI models are not static; they need to be continuously monitored and updated as data and business conditions evolve. This ensures that they remain accurate, relevant, and effective.

About the author: Antti “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.