Member Article

Why the future of manufacturing depends on quality machine learning

We’ve all read dozens of articles on the so-called “rise of the machines”; predicting a fully-automated future in which a new generation of smart robots and Artificial Intelligence (AI) enabled systems have taken all of our jobs and rendered us all irrelevant in the workplace.

Let’s be honest, in many respects the future is already here. A recent report by ABI Research found that 13% of businesses were making use of so-called ‘Cobots’, collaborative robots, in their premises, with another 15% suggesting that they would be investing in this technology in the coming year.

We all know that Artificial Intelligence is being embraced by decision makers all over the world by businesses keen to profit from the efficiencies that it can bring, across an endless spectrum of industries – from retail, to hotels, to healthcare and beyond.

When you consider that manufacturing is fundamentally dependent on the accuracy of processes and forecasts, it makes perfect sense that AI going to be a vital element of the industry’s future. However, it’s important that companies understand that the benefits of AI are dependent on the quality of the learning that leads to the overall decision or output.

And AI isn’t just taking place on the factory floor. It’s increasingly being integrated to sales and marketing departments – including the software that helps teams to land the deals in the first place. This means AI systems need to be seen as part of the wider picture for tech investment, as an overall drive towards improving processes and efficiency, whether on the floor or in the office where relationships with customers are built and maintained.

AI can only be intelligent with the right data

Artificial Intelligence, machine learning and predictive technologies all hinge on one vital area: the quality of the data set they are interpreting and learning from. The whole purpose of this technology is to study patterns of behaviour from data, and construct algorithms that can learn from and make predictions, boosting efficiency and cutting down on manual processes. The ultimate aim is to reduce the investment and resource needed to programme machines – it’s called machine learning for a reason.

The quality of the outputs from AI tech, which are largely measured by how accurate and appropriate they are to addressing the initial challenge, is directly correlated to the quality of their learning which led them to the decision. This is a consequence of the data that the learning is based on and systems that provide that data.

This is why the data set needs to be rich and robust. In the sales and marketing department, this can be achieved through technology such as customer relationship management (CRM) systems, which aggregate data from multiple touchpoints, to create accurate data and records that AI can leverage. This, in turn, will enable the tech to provide more accurate business outcomes such as demand forecasts, inventory shortages, or defect prediction.

Invest in smart data collection systems

But the truth is manufacturers’ customer relationship management (CRM) systems are often outdated, meaning that they’re missing out on the efficiencies created by an ability to plan and forecast sales revenues. This is where tech and humans need to work in unison. It’s true that customers often want an automated or self-service model for simple requests like checking the status of an order, but when they have a real or complex problem, they want a human to listen to them.

In a multi-channel world, manufacturers need to understand the buyer’s journey from start to finish, with 360 degree insights on sales, service, fulfilment, to improve the customer experience at each touchpoint. This is where humans are still vital to manufacturing workforces – and will continue to be so. Employees are crucial for the delivery of the customer experience, and building relationships.

A lack of supply chain visibility can increase the risk of disruption and inefficient business practices, and result in missed deliveries or incorrect outputs. This is why manufacturers must have the ability to evaluate supply chain performance and identify potential risks on a 24/7 basis; which can be achieved through a combination of smart AI tech and customer relationship systems which work in unison.

It’s crucial that customer-facing employees have access to technology that supports their relationships; enabling them to see the exact status of every order, expected delivery date and quickly have the data available for troubleshooting any issues that arise.

Senior management also need to monitor and measure the performance of sales teams and individuals, to address any issues. Our Sugar Hint technology, for example, makes this process a whole lot easier by providing AI to sales teams, to give them a complete picture of their customers.

Looking to the future

There is no doubt that AI tech will continue to evolve and transform the manufacturing industry, but it’s crucial for businesses to remember that the quality of the output is dependent on how accurate and robust the data is in the first place. The combination of modern, smart systems and a human workforce means manufacturers can have the best of both worlds: efficiency, accuracy, productivity – all underpinned by great customer relationships.

This was posted in Bdaily's Members' News section by SugarCRM .