Manufacturing companies are in a great position to automate up to 80% of their processes (European Coatings). AI is the key driver of such fundamental change – the use of AI can reduce producers’ conversion costs by up to 20%, with up to 70% of the cost reduction resulting from higher workforce productivity (BCG). 

Manufacturing is estimated to generate 1,812 petabytes of data per year, which is more than finance, communication and other industries. (Deloitte). If those data are utilized, companies can get valuable information and spot important performance drivers more efficiently, improve production processes and achieve positive and measurable spillover effects on financial performance.

Therefore, global market leaders from different manufacturing sectors already started to utilizing AI-driven benefits: 

• By utilizing AI, BMW Group analyze component images from its production line and thus identifies deviations from the standard product quality in real time (Capgemini Research institute)
• Carlsberg processes the data by using AI and leverage the information to develop new beers and improve the quality of existing ones (Capgemini Research institute)
• Nokia is using machine learning in video application to monitor the factory’s assembly line process (Capgemini Research institute)
• Bombardier has improved its resource planning capacities by using AI enabled tools. Supported by AI, Bombardier is able to schedule its airplane assembly operations, and handle production rate changes, more effectively (Capgemini Research institute) 

Still, not all companies are successful in implementing AI projects that meet set goals. If we sneak peek into Deloitte’s recent report, we can see that 91% of AI projects manufacturing companies failed to meet expectations. Additionally, Gartner made this year’s prediction that 85% of AI projects will fail to meet expectations due to data biases, algorithms, or the team managing those projects.  

However, we know what it takes to deliver AI implementation in more efficient and productive way for your company. 

Let us first explain the key areas of AI application in manufacturing: 

Smart production

AI is used for factory automation, order management and automated scheduling (Deloitte).

AI gives an opportunity to machines to become self-optimized and to adopt their parameters in real time by analyzing and learning from current and historical data (BCG). For example, while tracking the overall equipment effectiveness, manufacturers can increase productivity, and pre-emptively identify breakdowns in production. 

Implementation of IoT together with AI gives a manufacturing organization an opportunity to spot and monitor in real-time multiple areas in the production process and adjust their plans and decisions based on data insights. 

Additionally, the implementation of AI can improve the production planning and consequently increase the production efficiency. 

Supply chain management

AI can significantly improve demand forecasting, and facilitate more efficient inventory management. Applying AI in inventory management enables companies to have faster entry and exit of products and services from warehouses and savings in inventory costs. 

But not only this. Early warning alerts based on AI timely predict the future trends and demand which enables companies to be more agile in creating and executing production and sales plans. 

Sales optimization 

By leveraging AI-based demand elasticity models, demand forecasting models and optimization algorithms, manufacturing companies can drive sales, adopt flexible pricing and achieve higher marketing return on investment. 

For successful AI implementation process, PwC defined the six important steps: 

• Business application: meaning that your primary goal is to make valuable business decisions based on data and analytics, you don’t need theoretical models with no business in mind 
• Data: To be able to start data-driven business journey, you need to think about data sources you have internally and externally
• Talent and organization: understanding the talent gap and the need for external support for a successful data-driven journey 
• Processes: digital and data company transformation require business processes to be optimal and adjusted to data democratization and governance principles 
• Technology: mapping the current systems and tools that you use in tech-driven business development 
• Culture: data-driven development needs to be put in the heart of a company's culture that enables data-decision making processes and trust in data algorithms and results, at all levels in the organization. 

We, at EM Analytic Solutions, are leveraging smart data insights from our tailor-made explainable AI models to optimize price and/or promotion policies, improve inventory management, increase sales and/or production efficiency, and timely identify disruptions in production and/or packaging lines. 

Are you now more likely to start your AI-driven manufacturing business? 

If you are, write to us at: office@emanalyticsolutions.com

And check our useful cases from different industries: https://emanalyticsolutions.com/Industries/