10 Ways AI Might Impact Manufacturers

The goal with integrating artificial intelligence (AI) and enterprise resource planning (ERP) software is essentially the same as ERP when it was first developed — simplifying manufacturing to improve operational efficiency and increase profitability while growing the company. The difference is that with AI, manufacturers can track and analyze predictive data as well as current and historical data.

With its self-learning capabilities, AI can also assist manufacturers in their decision making when the relevant data, parameters and variables exceed human understanding. Here are various ways AI could positively impact your operation:

Inventory management. AI-integrated ERP software helps manufacturers optimize inventory management by predicting demand, identifying slow-moving products and automating order fulfillment. Inventory planning can be made more proactive using AI as it can increase visibility of inventory key performance indicators (KPIs); improve product, channel and location forecasting; automate classifying of SKUs to identify what’s needed to meet demand; and replenish SKUs faster with predictive ordering based on anticipated changes in supply and demand.

Quality control. AI-based inspection systems can identify defects and anomalies in manufacturing processes in real time, thereby reducing the risk of product recalls and improving overall quality. For example, image recognition algorithms are capable of analyzing images of products on the assembly line to identify defects that may not be visible to the human eye. AI is also changing the way quality gets inspected. Machine vision is an integral part of many quality applications. With its deep learning capabilities, AI-powered quality control software can self-learn which aspects are vital and create rules that determine the features needed to define quality products.

Pricing optimization. AI-powered ERP software can optimize pricing by analyzing market trends, competitor pricing and customer behavior. With this data, manufacturers can make better-informed decisions to optimize prices for their products, resulting in higher profits and better customer satisfaction. AI’s deep data dives allow you to model how customers will respond to price changes based on historical sales data. It also lets you factor customer behavior into pricing strategies, predict how different prices will impact sales and combine experience and data to increase prices without damaging sales. AI predictions aren’t 100% accurate, but they inform gut feelings about effective pricing strategies.

AI can combine supply, sales, finance and marketing projections into a holistic view of demand across your entire enterprise.

Demand forecasting. AI can be used to predict demand for products based on historical data, market trends and customer behavior, helping to optimize production schedules, reduce lead times and avoid stockouts. With AI, you can predict consumer demand for every SKU by taking into account seasonality, pricing, promotions and product lifecycles. AI offers the unique ability to engage in demand forecasting across different time horizons. This includes near-term demand sensing, a forecasting method that combines AI and real-time data to create a forecast based on current supply chain conditions. Other AI-enabled forecasting includes direct-to-consumer and e-commerce. AI can also combine supply, sales, finance and marketing projections into a holistic view of demand across your entire enterprise.

Supply chain management. As we all experienced during and after COVID-19, disruptions to supply chains can create serious problems. AI-powered ERP software helps optimize supply chain management by predicting supplier lead times, identifying bottlenecks and optimizing logistics routes to reduce lead times, lower costs and increase customer satisfaction. AI algorithms analyze data to predict which products will be in demand and in what quantities, reducing strains on specific links of your supply chain.

Predictive maintenance scheduling. Proper maintenance is essential to minimizing downtime, reducing repair costs and extending the life of your machines and equipment. AI helps achieve these goals by predicting equipment failure and scheduling preventative maintenance before a breakdown occurs. AI collects and processes data from sensors, cameras, logs and other sources. Engineers then analyze the data to make predictions and recommend maintenance actions.

Labor management. Labor costs are often the biggest line item in the manufacturing budget. AI-powered ERP software can help reduce labor costs and increase productivity by predicting employee productivity, identifying training needs and optimizing scheduling. AI can also alleviate another costly labor problem — workplace injuries — by limiting shopfloor personnel’s exposure to powerful, unwieldy machinery and dangerous tasks. AI does this by streamlining or automating risky processes that can lead to serious injuries.

Labor shortages. AI can help with labor shortages through robotic automation, additive manufacturing and machine vision. AI applications enable robot arms to safely handle objects on the production line regardless of their orientation, speed or placement. With these abilities, robots can be trained to perform various types of assembly line work done by humans. AI-driven autonomous machine vision can count and track items, identify defects and properly sort products using cameras and specific lighting conditions.

Autonomous manufacturing. AI robots can tap into machine learning algorithms to automate repetitive tasks and decision making. Robotic process automation can perform repetitive tasks like data entry and order processing, but it can also handle more complex tasks such as spotting anomalies on the production line. Autonomous mobile robots can transport packages within a facility while cobots assemble products alongside humans.

Real-time analytics. AI-powered ERP software provides real-time analytics on KPIs such as production rates, inventory levels and quality metrics to help you make data-driven decisions and identify areas for improvement. While conventional data analysis methods do a good job of organizing and distributing IoT data, AI does it faster and with greater precision by identifying patterns and inconsistencies in real-time. AI speeds up real-time analytics by preparing, analyzing and assessing data as soon as it is available.