Digital transformation: the key to future supply chain resilience

Digital transformation: the key to future supply chain resilience

iStock/Phawat Topaisan 

Manufacturers are using AI tools, IoT devices and cloud solutions to make data-informed decisions and improve their product lifecycle management

Guest contributor |

Manufacturers are facing a wide range of disruptions across the world. Supply chain shortages are caused by geopolitical issues, cyberattacks, consumer-demand swings and natural disasters. To meet this challenge, they are accelerating their digital transformations to improve their visibility throughout the supply chain and using artificial intelligence-powered analytics to streamline and innovate their daily operations and processes.

By combining generative AI with advanced data analytics, businesses can make data-informed decisions, access valuable insights and optimise their operations to create robust distribution networks. 

Rochelle Fleming

“Digitally transforming the manufacturing process unlocks automation in the quality control process, eliminating the potential for human error and improving efficiency,” says Microsoft’s Rochelle Fleming

Streamlining operations with AI

There are four use cases in which the integration of AI will positively impact supply chain resilience: enterprise resource planning (ERP), manufacturing execution systems (MES), predictive maintenance and logistics management. 

ERP solutions are the backbone of industrial operations, a central hub for all information and processes. By incorporating business intelligence and AI with Microsoft Dynamics 365 Supply Chain Management, users gain cross-departmental insights faster. The solution also creates more efficient processes, improves cost savings, optimises operations and improves forecasting accuracy using insights gleaned from real-time data. For example, food company Nestlé implemented Dynamics 365 Supply Chain Management, Finance and Commerce with the help of Microsoft partner KPMG to improve its accounting and supply chain reporting across its 2,000 brands in 188 countries.


Nestlé uses Dynamics 365 for product lifecycle management

Another applicable use case is for MES, which are systems designed to help manufacturers track and manage operations in ways that ERP and process control systems may not fully cover. An MES grants visibility into data to make decisions that will result in efficient and optimised production. For those with regulations to consider – such as pharmaceuticals, food and beverages, and medical devices – MES helps drive industrial automation and lowers the cost of production while ensuring regulatory compliance and operational visibility. Combined with generative AI, employees can surface information and raise alarms faster, as well as track and manage ‘lean’ inventories while still meeting customer demands and expectations.

When AI is implemented within a firm’s maintenance cycles, it enables predictive maintenance. This allows them to proactively avoid unplanned downtime by predicting when maintenance work needs to be carried out on assets and then optimising those repairs by scheduling them at the least disruptive time for production. AI can also use captured data to provide a real-time view of operations on the plant floor and equipment. This additional visibility improves efficiency and productivity. 

In response to the global delivery disruptions experienced during recent crises, manufacturers integrating AI to help with logistics management. Through automation, firms are able to order, track and deliver materials needed for production on time and on schedule without tying up valuable resources. They can also link identified supplier risk to real-time supply chain data for deeper insights and to improve response times to outages and disruptions.

Product lifecycle and quality management

As manufacturers move to integrate new digital cloud technologies into their processes, generative AI-driven product lifecycle management (PLM) improvements are helping them to reduce costs, improve quality and increase their speed to market. With generative AI, deeper insights can be gleaned from extant data, allowing employees to automate tedious manual processes, freeing up valuable resources. 

With many companies consolidating their data in the cloud as part of their digital transformation journeys, the logical next step is harnessing the storage and computing power of the cloud to drive more innovative products. They can do this by pulling insights at scale from a wide range of sources, both from traditional methods of customer feedback and incorporating edge and internet of things (IoT) data to create a holistic view of the entire business.

AI-powered quality management solutions from Microsoft partners like, Predisys and Quest Global analyse large amounts of data in real time, enabling a proactive approach to quality management. By using data collected during production, manufacturers can quickly identify trends and patterns and, if necessary, implement a resolution. Digitally transforming the industrial process unlocks automation in the quality control process, eliminating the potential for human error and improving efficiency.

This added depth allows businesses to not only make better, more informed decisions, but it also empowers them to create better quality products and experiences, which in turn increases customer satisfaction and loyalty. In addition, these insights also allow firms to find new ways to improve profitability and performance while also lowering environmental impacts and costs.

As manufacturers move further along in their digital transformation journeys, new evolving technologies like edge computing and IoT can, and will, transform the way they create resilient supply chains in the future.

Learn more about how digital manufacturing enables companies to increase revenue, improve their bottom line and gain a competitive advantage by registering for Microsoft’s manufacturing webinar series.

Rochelle Fleming is director of partner marketing at Microsoft

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