Already deployed in autonomous trucks, chatbots providing customer service, and drone trains, artificial intelligence (AI) is also making huge boosts to manufacturers’ productivity. Creating insights to enable manufacturers to produce higher quality products faster and more efficiently, AI solutions also provide critical information to help managers make more informed business decisions.
There are several ways that AI can optimize each stage of manufacturing from the shop floor to the final product delivery. Here are 5 ways manufacturing functions can raise their level of productivity by using AI.
Every step along the supply chain can be optimized by using smart sensors to track the location of components combined with analytics and machine learning. McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively.
BMW has already implemented an AI system that follows a part from the initial point it was first manufactured, all the way through to when the vehicle is sold for 31 assembly facilities, spread over 15 countries. This system makes sure everything gets to the right place at the right time while utilizing the minimum amount of resources.
AI and machine learning can forecast demand by testing hundreds of models and possibilities while also being more precise by adjusting its calculations for the introduction of new suppliers, product and materials. By having more accurate demand forecasts, companies can avoid overproduction and the costs of keeping excess inventory on shelves. An excess of raw materials or finished goods ties up cash that can be put to better use elsewhere. Insufficient inventory or stock outs can be just as detrimental by causing product delays, which can reduce customer satisfaction and tarnish a company’s reputation for reliability.
There is a huge incentive to invest in predictive maintenance solutions because of their strong ROI and quick payback. By utilizing sensors to monitor operational conditions, technicians can be alerted in advance of potential equipment problems and service machines based on actual wear and tear instead of scheduled service visits based on general manufacturers’ recommendations. When predictive maintenance systems are connected to ERP systems, machines can even evaluate their own performance, order their own replacement parts and schedule a field technician when necessary.
Siemens installed smart boxes containing sensors and a communications interface on older motors, transmissions of wind turbines, and other equipment to assess a machine’s condition and detect irregularities in order to determine when a service call is required. A similar predictive maintenance solution has been implemented at the German railway company Deutsche Bahn to monitor the condition of engines of high speed trains.
Advances in AI and software intelligence are enabling companies to make products and services that are highly personalized. Twenty percent of consumers said they would be willing to pay a 20% premium for products or services personalized for them. And brands who can highly personalize products are also able to build greater loyalty and trust with their customers. Daimler utilizes real-time data of parts and their availability to efficiently respond and adjust for vehicle customizations even providing an app where customers can track the progress of their car’s assembly called Joyful Anticipation.
AI systems can monitor quantities used, cycle times, temperatures, lead times, errors, and down time to continuously optimize production runs. AI will enable us to transform data into intelligence in a vendor agnostic environment where all machines speak the same language, increasing production efficiency from machine to machine across the shop floor. Siemens already equips devices with AI capabilities to improve the reliability of power grids, with the ability to classify and localize disruptions in the grid, and perform the necessary calculations remotely to self-heal electrical systems.
There is resistance to the implementation of AI systems. Many companies are reluctant to share vast production and process data due to its sensitive nature. Others include concerns about data security, lack of standards (although those are rapidly progressing), and concerns about the impact on people who fear they will be replaced by machines or resist working side by side with AI systems.
There is also the challenge of integrating data between different types of equipment and back office systems with a high level of reliability and low latency so systems can benefit from real-time insights. A data management platform can provide scalability with the capabilities needed to collect, integrate, process and share huge volumes of data with a high level of performance and security.
Since AI can introduce so many different types of efficiency boosters throughout the manufacturing organization, it’s certain that in time technology, people and processes will adjust to make machine learning an indispensable part of companies improving product quality and customer service.
