In recent years, we've seen how rising costs, disrupted supply chains, and lockdowns can adversely affect businesses of any size. But there are some solutions that, if followed, can reduce your risk and help make turbulent times a little easier. This week on Let's Talk, our experts share their tips that will help you address the risks and prepare your business for any supply chain shocks. "There are several tactics that Australian business leaders can adopt to prepare for and address the aftershocks of shipment delays and stock unavailability. "Rather than relying on the just in time approach, which can be risky when there are supply shortages or shipping delays, the just in case approach is recommended. This approach focuses on forecasting demand to proactively secure sufficient supplies ahead of time. For this to work, a robust business management solution which grants to timely data which provides insight into incoming orders versus available stock is a key requirement. The just in case approach can boost profitability, while preventing wastage. "Having up-to-date industry data like procurement lead times, stock levels and order volumes can allow business owners to manage potential vulnerabilities in the supply chain and optimise efficiencies within. Finance teams can leverage this data allowing them to create more accurate financial forecasting models to save on supply chain costs and inventory management."

Let's all agree: the pandemic has reshaped the global supply chain. Multiple lockdowns paired with temporary trade restrictions and workforce shortages unveiled vulnerabilities in supply chains that were previously unseen. The drastic change of the landscape forced supply chain executives to up their strategic management game. To do so, some of them have turned to innovative technologies, with supply chain AI leading the race. In fact, while it is a standard practice for enterprises to hold up their digital transformation projects in times of economic uncertainty, the COVID-19 crisis did not stop supply chain decision-makers from turning to artificial intelligence solutions providers.

Let's all agree: the pandemic has reshaped the global supply chain. Multiple lockdowns paired with temporary trade restrictions and workforce shortages unveiled vulnerabilities in supply chains that were previously unseen. The drastic change of the landscape forced supply chain executives to up their strategic management game. To do so, some of them have turned to innovative technologies, with supply chain AI leading the race. In fact, while it is a standard practice for enterprises to hold up their digital transformation projects in times of economic uncertainty, the COVID-19 crisis did not stop supply chain decision-makers from turning to artificial intelligence solutions providers.

Some elements of what we now call digital twins reach back into the early days of industrial computing. Data historians, PLC's and instrumented processes have been a part of large industrial systems for some time. Process manufacturers in energy, resources, chemicals, food, and pharmaceutical markets have instrumented the environment for safety, efficiency, and regulatory compliance for as long as we have used computers in industry. This data provided some level of benefit in terms of meeting compliance requirements and improving maintenance and uptime through alerts and analytics, but it was also difficult to work with and expensive to deploy and manage. Contemporary digital twins are an entirely different animal.

Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionising supply chain management in the process. Machine learning algorithms and the models they're based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon's Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyses 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions.

In simple terms, that "most important role" is the cycle of observation followed by critical thinking followed by action. It's important to bear in mind that the proper goal of Machine Learning (ML) is not abdication of human responsibility for decision-making. Rather, it's improving our individual and collective ability to make better decisions by leveraging increased speed, accuracy and absence of bias. Our context here is supply chain planning and execution, but there is no reason to limit the scope of Machine Learning. When it comes to designing and creating technology solutions for supply chain analytics and business intelligence, this is not a throw-away idea buried in a long-forgotten PowerPoint presentation.

It wasn't until this past year that I was introduced to the concept of machine learning. As with most advanced technologies, it took a personal experience to help me best understand the concepts and benefits. Case in point -- a couple of months had passed before I fully connected a Nest thermostat to our Wi-Fi network and the Internet. Before this connectivity, the well-designed thermostat managed our home temperature much like our previous thermostat. Once connected, however, it better understood our preferences and patterns for managing home comfort and reducing energy requirements.