Six tips to help manufacturers leverage AI and Machine Learning

Six tips to help manufacturers leverage AI and Machine Learning

Khaled AlShami, Vice President, Solution Consulting, Middle East and Africa, Infor, discusses the benefits of Machine learning and AI and how they can help businesses navigate these difficulties.

With manufacturers facing challenges including rampant inflation, supply chains issue, and geopolitical tensions, many are turning to Machine Learning (ML) solutions to help navigate the headwinds and boost their operations.

Manufacturers can gain serious advantages with ML, which can deliver predictive quality control and product maintenance to ensure they are producing good products, monitoring system maintenance to avoid production disruptions, and capturing their internal knowledge.

A recent example that highlights the value of ML/AI is the COVID-19 pandemic that threw a huge curveball at manufacturers who had to quickly figure out how to adapt. Demand forecasting with the benefit of ML allowed manufacturers with those tools to quickly, and more accurately, determine what needed adjusting.

Here are six primary value drivers that manufacturers can tap into by integrating AI/ML:

1.           Process Intelligence – Improves efficiency and decision making for both business and manufacturing processes. This can be as simple as, ‘how do I detect anomalies in our accounting system to prevent fraud?’ Information like this is simple for AI/ML to detect.

2.           Asset Intelligence – Very commonly used for predictive and preventative maintenance. It is helpful to be able to predict when a piece of equipment might not work as it is supposed to or have unexpected downtime which is one of the leading causes of lost opportunity and lost value for a company during production.

3.           Forecasting Intelligence – A newer tool that is necessary for companies to help with supply chain issues. Helps with knowing what/when to order materials and can even predict when some materials may be short.

4.           Sales Intelligence – Helps with improving efficiency and effectiveness of customer relationships, answers questions like: Who are my best potential target customers? As well as helps with forecasting sales goals for the coming year.

5.           Pricing Intelligence – Supports market-centric pricing and detects pricing anomalies or opportunities in the market where prices could be raised to increase margins as well as where margins may be shrinking and how to deal with that.

6.           Human Capital Intelligence – Provides labour efficiency with insights about how to improve employee satisfaction and retention. Can identify employees who might be at risk before they quit. This is important with today’s tight labour market.

It is important for manufacturers to start thinking about what kind of information they need to deliver a certain result and then build in the AI/ML tools with semantic data models to generate accurate and helpful results.

In terms of implementation, it is advisable to start with simple processes and analysis, and then branch out based on those learnings with more advanced AI/ML. Most enterprises already have significant amounts of data within their ERP, CRM and HR maintenance management systems and manufacturing execution systems which they can leverage with ML.

It is also important to remember that successful ML implementations demand some specialist skills, so don’t try to go it alone when you are determining how to utilize and integrate Machine Learning or Artificial Intelligence for your organization. It requires focus and skill sets from those with experience in these areas.

In short, AI/ML can be valuable assets to expand a manufacturer’s knowledge about its processes, products and production. Integrating AI/ML strengthens strategic decision making and can deliver bottom line value, both of which are becoming ever more important in today’s challenging climate.

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