None of the stark realizations of the COVID-19 era is the fragile nature of the supply chains that permeate
the globe. Supply chains are a complex web of supply arteries that feed each other in such a way that a small
disruption can generate ripple effects down the line. COVID-19 became the ultimate monkey wrench that tossed
itself into the supply chain machinery, demonstrating the critical importance of a resilient supply chain - and
the steep costs of supply chain disruption.
Supply chain disruptions are more than just a frustration for consumers. A 2021 report estimated the annual average cost to organizations was $184 million globally, and $228 million in the U.S. , According to McKinsey, supply chain disruptions can cost the average organization 45% of one year’s profits over the course of a decade. In a hyper competitive world, those companies that can attain greater control, resilience and predictability with their supply logistics will realize a significant competitive advantage over their competitors.
Supply chains are vulnerable to an increasing number of stress points and the accelerated speed of business.
Throw in the increasing complexity of global supply chains and you’re left with a rapidly diminishing margin for
error. In response, companies are now focusing on creating a more robust logistical system through the
implementation of supply chain resilience strategies. Gartner defines supply
chain resilience as:
“The ability to adapt to structural changes by modifying supply chain strategies, products, and technologies as the ability to sense and respond to unanticipated changes in demand or supply quickly and reliably, without sacrificing cost or quality.”
Supply chain resilience is about increasing a company’s proficiency to prepare for unexpected events and promptly adjust to abrupt changes that can disrupt an/or negatively impact supply chain performance. Supply chain resilience allows for a company to quickly remediate impactful disruptions to return to normal operations.
Visibility is essential for nearly any operation. Having complete visibility of your supply chains involves
the means to identify every link within your supply chain network. This means having full transparency across
the full supply spectrum, including the tracking of raw materials and components, to subassemblies and customer
It’s not just about identifying risks, but also recognizing those unrecognized opportunities, which translate into greater efficiencies. Unfortunately, more than half of all companies lack end-to-end visibility of their supply chains. That’s because the complexity of elongated and densely intertwined supply chains with many stakeholders involved at any one time has exceeded the capabilities of conventional logistics management tools. Visibility means having access to real time tracking of every component that goes into a product and having highly accurate inventory counts. It requires peering into the near future in the form of demand sensing in which decision makers utilize detailed short term demand data to make demand forecasts with greater accuracy. Visibility means being able to identify alternative delivery routes and handling processes to shrink delivery windows. Visibility also translates into facets such as employee safety by identifying risk patterns that can lead to accidents that can both disrupt operations and impact human life. So how do companies attain such highly scalable visibility? The answer is the use of artificial intelligence in the supply chain. This need is substantiated by an IDC report that shows 42% of businesses are driving digital transformation within their supply chains. Companies are recognizing that an AI powered supply chain is the only way to produce the necessary supply chain resilience that is required today.
Mike Tyson once said that everyone has a plan until they get punched in the mouth. COVID was that punch in the mouth that clearly demonstrated the shortcomings of having a fixed supply chain plan. The primary reason for companies to turn to digital transformation is to attain greater agility. To inject greater agility into their supply chain networks, logistical managers need to use scenario planning to be able to adapt an optimized base plan for inevitable uncertainties. Sudden events such as a labor strike, surging fuel prices or the temporary closure of a shipping port are all circumstances that require advanced planning. This type of adaptive supply chain can only be achieved by a cognitive AI-driven platform that can analytically identify the matching cause and effects of a given scenario along with its impact, and provide remedial measures to counter it.
So how do logistics managers go about playing out these scenarios? To do so requires a digital recreation of
your supply chain environment to simulate it. Think of this digitized ecosphere as a dedicated metaverse you
can use to understand the behavior of your supply chain network. It is often referred to as a digital twin.
These digital chains are virtual replicas that include all of a supply chain’s assets, such as warehouses and inventories, allowing for the creation of situation models that incorporate live information feeds and dynamic snapshots of their real-time environment, all of which can be supplied by IoT systems. Not only can these digitally simulated models help in eliminating bottlenecks and improving risk mitigation, their use of AI-driven, forward-looking intelligence and machine learning can also improve supply inventory control, reduce costs, eliminate operational redundancies and decrease the response time to unforeseen demand.
The insightful intelligence provided by digital twins is proving so transformative that according to Gartner, 75% of organizations that have implemented IoT are either already using digital twins or plan to use them within a year.
Don’t confuse AI with the conventional practice of using predictive intelligence to manage supply chains.
Predictive intelligence involves the use of statistics and relies on human interaction to query data.
It makes use of “what if” assumptions that are based on the human understanding of past events.
Unfortunately, human understanding has its limitations. AI is completely autonomous as it can obtain its information directly from sensory data that is automatically supplied. While predictive analysis methods can use algorithmic analysis, modern-day, AI-driven systems use machine learning, which takes cognitive data in order to learn from it. This learning process allows it to modify the involved algorithms over time.
The benefit of this learning is that an algorithm can constantly test and re-evaluate data to uncover underlying patterns that may otherwise be undetectable. AI-powered systems can also make predictions at a speed and scale that otherwise would not be possible.
The use of AI in supply chain planning is a trend that will soon permeate the industry. The supply chain industry has been forced to adapt a lot over the past few years. Part of that adaptation involves embracing new technologies, including the unlimited learning potential of AI and how it can be harnessed to predict and prepare for the unpredictable. If you haven’t yet injected AI into your supply chain management, the time is now.
AltaML started by building and training an ML model using historical trip data found from the truck’s onboard computers. The first task was to work with the logistics managers to identify where data may be present to tell us more about good and bad driver trip behaviour.