The Future of Supply Chains Is AI: How to Start Small to Win Big Over Time


Skim the latest supply chain news and you’ll find headlines spanning disruptions, backlogs, shipping delays, soaring prices, and changing consumer habits. “Supply chain complexity” is a popular search term as businesses explore technologies that will enable them to better manage risk and uncertainty. We’ve discussed how supply chain visibility is the foundation to building a successful technology strategy – but what is the ultimate aim?

Certainly, the “perfect order” – on time and in full (OTIF) delivery at the lowest possible cost – is the gold standard from a business perspective. From a technological perspective, the systems that will best help us reach those aims are intelligent cloud platforms with artificial intelligence (AI), machine learning (ML), and deep learning (DL) capabilities.


Start by Embracing Supply Chain Complexity

From planning to execution, there are many factors to consider for maintaining consistently high – but also cost-effective – service levels. Brands must keep up with customer buying habits to prevent excess inventory and stock outs, carrier and transport constraints which can cause delays, and unexpected events and anomalies that require a timely and effective response.

Given all the variables and complexities, leveraging intelligence is the only way to ensure you always make the best use of your own resources and partner network under any condition. Multi-party orchestration platforms that have embedded intelligence use business rules to factor diverse conditions and constraints to determine optimal inventory allocation, as well as optimal routing through the supply chain. Moreover, when exceptions arise, the supply chain software is able to re-plan and re-optimize, so you stay on track – and on budget. The technology is easy to implement and requires no rip-and-replace; rather, it connects to and complements existing systems. As such, you can gain full supply chain visibility and begin optimizing each and every order within weeks.


Build on Your Experience & Tap into New Potential

 Once you’ve adopted a cloud platform that allows you to optimize each and every order, you can begin accruing data and learning from your experience to mitigate risk and become even more reliable to your customers. Machine learning technology is uniquely able to tap into deeper supply chain complexity by recognizing hidden patterns, relationships, and dependencies. Processing the results of past efforts, ML provides insight into how to proceed more strategically and cost-effectively, so you can learn from past decisions and refine your approach over time.

Machine learning draws from wide-ranging data in the platform (e.g., data related to inventory, orders, transportation, network partners, grids), as well as external factors, such as the weather forecast, traffic, and currency rates, to help businesses better forecast supply and demand relationships, maintain minimum and maximum inventory levels, estimate an order’s time of arrival, and assess the risk of surcharges, delays, and damage. Having a sense of what to expect from the market and the risk levels involved in fulfilling any given order, empowers supply chain managers to reliably facilitate on time and in full delivery, control costs, and flag high-risk orders for close monitoring, giving sensitive orders the VIP treatment when necessary.

With machine learning, it doesn’t take that much data to begin! The accuracy will improve over time, but you can start small and reap the benefits of incremental operation and service improvements.


Get One Step Ahead & Change the Game

 Once you’re optimizing every order, and learning from past experiences to continuously improve, you’re ready to extrapolate your findings to anticipate future trends and generate a predictive environment. Depending on the individual business, objectives can vary widely. For instance, retailers may want to factor seasonal buying patterns into their inventory strategy, or the probability of an order to be returned; logistics service providers and shippers may want to develop a dynamic pricing model based upon market conditions; while those in the barging industry may leverage predictive data from changing oil prices to offer more competitive rates.

The idea is to move from a reactionary to a predictive model. From ensuring consistent perfect orders in the present (by learning from relationships and dependencies) to gaining competitive advantage (by anticipating where those patterns are likely to develop) and taking intelligent, preemptive action.

Investing in advanced technology, such as AI, ML and DL is not an all or nothing proposition. You can start small – but you have to start somewhere, and fast. Learning takes time, so it’s critical to begin building a wealth of data and experience now to have it augment over time. Finally, don’t be afraid to let your imagination run wild! Your software provider should be eager to hear about the challenges you face and be willing to explore new possibilities and innovations together.

Are you interested in learning more about solution opportunities in AI and Machine Learning? The MPO Multi-Party Orchestration Platform provides an embedded layer of intelligence for continuous order optimization, as well as capabilities for Machine Learning that you can apply to enhance your supply chain’s agility, resilience, and risk management. Contact us today to discuss your business challenges and how our solution offering can help.