Building a Resilient Supply Chain

Operational issues apart, organizations face a risk of Supply Chain Disruption from a variety of factors: the emergence of unexpected competitors, pandemics, rapid shifts in consumer behaviors and wars can put a drag on production and supply.

Efficient Supply Chain Management helps reduce supply chain disturbance while Supply Chain resilience helps anticipate disruptions to minimize their impact or avoid them altogether.

Artificial intelligence (AI) and Machine Learning (ML) capabilities in enterprise resource planning (ERP) or used to extend the traditional transactional systems, are increasingly becoming necessary to strengthen Supply Chain Resilience.

AI/ML in ERP use cases to resist disruptive Supply Chain events

AI-powered ERP systems enable more accurate and efficient Supply Chain Management and planning, with minimal human intervention.

These use cases show how the use of AI and Machine Learning can be particularly beneficial during major events that can shake up supply chains.

Predictive analytics

When will specific items in your inventory run out of stock? Who, among your suppliers, can you rely on the most? What are the chances of customers buying a product variant you plan to launch in the future? Predictive analytics is able to analyze patterns in historical data to provide accurate forecasting. It uses mathematical and statistical methods, including artificial intelligence and machine learning, to predict future outcomes to a large degree of accuracy. The use of predictive analytics allows you to move quickly during a disruptive event and mitigate its effects.

Planning optimization

Supply chain planning optimization determines how to procure, manufacture and distribute products to balance demand and supply. It matters even more in a disruptive environment, where profits are under threat. One of the optimization tactics is prioritizing the procurement and production of those items that can help the organization most during a potential crisis. An ERP system with this planning optimization feature will then be useful. Microsoft 365 Dynamics, for example, offers priority-based planning that allows you to generate replenishment orders based on planning priorities rather than requirement dates.

Tracking external pressures

One of the lessons we all have learned is that keeping track of external pressures, such as changes in consumer behaviors, raw material availability, geopolitical changes or any other disruption is crucial. It is yet another reason to extend the traditional transactional systems and integrate artificial intelligence and machine learning into them that will help making sense of the data collected from various sources to provide more insights and anticipate potential shifts and take proactive steps to ensure resilience, and even spot opportunities before competitors can.

The focus on supply chain resilience has intensified in the aftermath of the various disruptions we have seen in recent years.

Real-time access to information and intelligent, data-driven forecasting can enhance organizations' ability to act decisively to survive tough times and boost competitive advantage.