21 August 2018 by Spencer Symmons
Thanks to groundbreaking leaps in technology, artificial intelligence has quietly crept into the mainstream within the short space of a decade. Today, we can find machine learning at the heart of many everyday processes that we take for granted: a recommendation of a film from a streaming service, an estimation of delivery time on a takeaway and even the filter that diverts spam away from our email inbox.
Beyond simplifying our personal lives, however, artificial intelligence is already proving invaluable to businesses in its ability to draw critical insight from swathes of unstructured, qualitative data. If integrated effectively, AI and machine learning could be the key to improving the efficiency of the ERP and optimising an organisation’s business processes on a whole.
As more leaders within the manufacturing and distribution industries recognise the opportunity of AI in supercharging the ERP, we’re exploring the tangible benefits of this combination.
Imagine it was possible to predict when machinery parts would need to be repaired or replaced before any sign of damage: ultimately, the outcome would be less breakdowns through proactive action. Through the use of sensors equipped with their own IP addresses, machine learning software can be deployed to detect patterns in the data set of an entire production floor - the data that is collected by an advanced ERP. For companies who suffer lost sales thanks to machinery breakdowns, AI & Cloud ERP are a match made in heaven.
For most business owners, choosing the right supplier can be a minefield. However, by integrating machine learning algorithms into a track-and-trace application, organisations can easily identify which supplier has a consistent record for delivering high-quality products and which will pose a risk. Once again, the in-depth analysis and ability to find patterns in diverse data sets makes machine learning a key component in driving efficiency.
In harnessing the capabilities of machine learning programs, Cloud ERP providers could create a self-learning platform that has eyes and ears in every area of the business. With a cloud-based infrastructure that combines core Web Services, apps and real-time data monitoring, information can quickly be digested and assessed by machine learning algorithms.
In turn, the speed at which the entire system learns is significantly accelerated. Naturally, effective integration with the many suppliers and buyer systems outside the business would require APIs and Web Services in order to deliver advanced insight from the mountains of data they have generated.
When a Cloud ERP is effectively transformed into continually-learning knowledge system, manufacturers are able to gain previously inaccessible insight into the OEE performance across their shop floors. Monitoring of real-time health data from machinery and other production assets will enable manufacturers to pinpoint key areas for improvement in advance, thus extending the life of equipment and reducing manufacturing costs.
If Cloud ERP platforms could use production incident reports to predict production problems on assembly lines, manufacturers could benefit from reduced risk of production slowdown or the discontinuing of a line altogether. Thanks to quantum leaps in machine learning, this is fast becoming a possibility. According to Forbes, an aircraft manufacturer is already using predictive modelling in conjunction with machine learning to compare past incident reports.
Cloud ERP systems are uniquely placed to scale across the entire product lifecycle, collecting data from production to point of sale. By introducing machine learning algorithms, this wealth of real-time information can be capitalised by manufacturers seeking quick design wins. The more an algorithm learns from inspection, quality control, Return Material Authorisation (RMA) and product failure data, the easier it becomes for manufacturers to improve the quality of their products and reduce time to market.
As well as improving product quality, an AI-fuelled Cloud ERP system could soon allow manufacturers to fine-tune their demand forecasting accuracy and drive enhanced collaboration with suppliers - all thanks to insights generated from machine learning-based predictive models.
Though commonly used to order food, check the weather forecast and send text messages, virtual assistants such as Apple’s Siri and Amazon’s Alexa have shown potential in redefining a number of key areas within manufacturing operations. If modified, voice agents can be used to provide contextual guidance to those carrying out complex or repetitive tasks to reduce the risk of errors and bring down time to market. Considering the success of voice pick-by-voice systems, the introduction of virtual assistants to manufacturing could prove revolutionary in improving the efficiency of production workers.
Cloud ERP providers must consider the role they will play in closing the configuration gap that currently exists between the various systems used throughout the organisation: from the ERP itself to PLM, CAD and CRM solutions. Fortunately, the combination of Cloud ERP and AI capabilities could be the key to creating a single view of product configurations through their entire lifecycle. By enabling configuration lifecycle management, these advanced systems will help to relieve the current conflicts between how products are designed with CAD and PLM, how they are built with an ERP and how they are promoted and sold with a CRM.
If they are to capitalise on the vast streams of data created by devices within the IoT ecosystem, Cloud ERP solutions must design in support at the data structure level. By taking advantage of IoT based data and using machine learning algorithms to analyse, aggregate and learn from it, the intelligence gap that many companies suffer today can be quickly bridged as they pursue new business models.
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