Growingly, artificial intelligence and machine learning are enabling manufacturers to manage their supply chain, lessen the risk, elevate the supply chain resilience, and also compete in a fast-evolving global environment.
By way of facing the supply chain issues of today, the manufacturers must stay agile and, at the same time, optimize for availability as well as expense, as per Philip Lord, who happens to be the business strategy manager at Autodesk in data and process. Yet more challenge, he says, is getting together data from numerous sources to create a holistic model as well as validate supply chain processes and pinpoint the probable areas when it comes to enhancement.
Lord adds that supply chains are truly worldwide economies, and any sort of local geopolitical transition, such as tariffs, war, regulatory changes, or even taxation, leads to ripples throughout the chain. Manufacturers must listen to the effect of these ripples with certain insightful thoughts as early in the design as possible and make the process as seamless as it can be.
Data Integration
The global vice president at o9 Solutions Inc., Brent Hasenkamp, says that AI and machine learning both help in optimizing the supply chain planning by way of integrating internal data like historical sales data along with external data sets such as risk assessments, economic elements, and even weather patterns. This goes on to produce a data-driven demand forecast, which enhances decision-making and planning.
AI and ML can help teams to track demand signals, insights on supplies, and also other critical elements, which can help planning teams to respond in a proactive way to potential supply chain risks and even opportunities, he adds.
Lord concurs, stating that AI and ML platforms now go on to ingest data from a broad variety of private data sources like ERP and product life-cycle management (PLM). Post this, they join them with public and commercial data sources. This helps to uncover insights on supply quality, compliance requirements, as well as the availability.
Lord says that data points as well as details can be distilled into one supply chain model, helping the manufacturer to make deeply informed decisions that can very well enhance their supply chain flexibility and even optimize costs, logistics, compliance, taxation, and other business outcomes. The overall result happens to be the foresight to construct a far better, agile supply chain and make sure that manufacturers can keep coming up with products to feed their customer demand and help the revenue streams.
ML can especially help in terms of looking at historical data so as to make decisions, as per the senior product manager for data and IoT at SAS Institute Inc., Camille Walker.
She says that machine learning algorithms perform along with data sets, offering an absolute look at trends within a business. The outcome is constant analysis of the waves that a product takes in a given point in time.
Walker goes on to note that AI happens to have similar advantages to machine learning, but it also has an interface that can analyze the data. Simultaneously, AI within operations and also planning can discover large data sets so as to get the right answer without any kind of subjectivity. Artificial intelligence systems go on to make advance analytics more accessible to a broader group of people having varied job rules as well as experience. With AI as well as generative AI, these often-siloed groups can get access to the information that is required to make the best and right decisions for their area of ownership.
The Human Aspect
Experts want to caution that AI cannot, and as a matter of fact should not, completely replace human workers.
It is indeed important to note that machine learning and AI support and hasten rather than replace human experts, says Walker. He also adds that the nuances of supply chains need professional interventions when it comes to proper decision-making.
Machine learning and AI technologies can throttle efficiency and the total accuracy, thereby offering real-time savings for these professionals. And such technologies help supply chain professionals to go ahead and apply their expertise in high-level decisions rather than just gather data and prep it for evaluation.
Looking Back and Moving Ahead
Five years post the pandemic, memories are still fresh about its effect on the supply chain. As per Walker, supply chain operations went on to remain strained, and many companies still struggle to find a potential vendor for their products as well as raw materials that were hard to source during those Covid times.
Walker asserts that the pandemic’s sudden stop in production and resources is still being felt today. The supply chain, which used to be running easily before the pandemic, now finds that one devastating storm can put the overall chain at risk, and then there are certain major materials that are still hard to come by.
As per Hasenkamp, COVID-19 as well as other post-pandemic issues, such as inflation, continued geopolitical challenges, and extreme weather conditions, have all continued to prominently affect the operations within the supply chain. This has gone on to result in many manufacturers creating buffer inventory so as to handle supply disruptions. Today, however, we are now beginning to get back to a more balanced and a pre-pandemic approach so as to right-size the inventory while at the same time keeping an eye on probable disruptions.
Post the pandemic challenges about holding too much of an inventory Became a hot issue, says Walker. With manufacturers wanting to be at peace and at least witness fewer disruptions while also eliminating the cost of huge inventory, they think that technology is what can help.
Walker adds that machine learning technology can enable a manufacturer to make sure that they are not overproducing goods that are not required. It helps the companies to impactfully distribute goods so as to shorten lead times and make sure that the time-to-market goes on to meet the demand.
While machine learning can enable organizations to make more effective use when it comes to present inventory, Walker goes on to suggest that companies still do not gauge the demand volume and in fact may as well overbuy the supplies for use to avoid certain extended periods of items getting out of stock.
To that end, Hasenkamp notes the numerous valuable takeaways from the last couple of years.
For example, organizations now go on to witness this necessity, creating flexible supply chain planning and making utmost use of emerging AI tech so as to gain better visibility into the n-tier supply chain and also create more precise risk management and scenario planning capacities.
For manufacturers to go ahead and create a competitive advantage when it comes to the post-pandemic business environment, they have to focus on a flexible supply chain, technology adoption, as well as a more data-informed decision-making capacity, says Hasenkamp.
Forecasting When It Comes To Best Results
As per Lord, precise supply chain forecasting enables the protection of competing demand for components as well as parts that happen to be in a product design. For instance, by making use of these predictions to critically analyze supplies, you can massively lessen the work that’s needed to address supply chain disruptions throughout a long as well as expensive design-to-make process.
When designers happen to have precise forecasting and that too at their fingertips, they are better positioned to opt for components or materials that are best suited when it comes to their supply chain optimization as well as estimated production volumes, says Lord.
It is well to be noted that the cost of design changes happens to be high and the manufacturing delays because of inventory shortages happen to be even higher. The point is that one misstep can go on to directly affect product deliveries as well as revenue flow. By way of minimizing alterations needed to get a product to the market, manufacturers are, in a way, able to get to the market much more faster with the new products and ensure deliveries with demand at the same time to unleash a new set of revenue growth.
Software also enables organizations to proactively address the potential supply chain disruption as per Hasenkamp, who goes on to explain that platforms leveraging AI as well as ML capabilities happen to be able to elevate demand forecasting precision through collecting as well as evaluating a range of data inputs.
These outcomes and trends, according to him, pinpoint outliers when it comes to forecasted versus actual demand and enable planners to think of various scenarios to account for pro demand, transitions, or supply chain disruptions.
Optimization of Process
Digital twins have also gone to play a crucial role in supporting supplying organizations by way of giving impactful scenarios so as to conquer challenges along with the chain, notes Walker.
A digital twin, or that matter, a virtual replica of the supply chain, can go on to help stimulate transitions of all sizes. For instance, a digital twin enables supply chain professionals to witness how production and facility can get affected down to the manufacturer line in case a material shortage is forecasted. Simulation of potential shortages is crucial when it comes to focusing on how an organization can rise to this issue.
Walker says that when organizations get to know where the bottlenecks might come into the picture, they can as well shift gears and pinpoint secondary as well as tertiary suppliers as needed.
She also adds that digital twins offer a seamless way so that communication of disruptions and their impact on internal business planning, sales, and operations never goes unnoticed.
Walker adds that organizations can go on to make certain meaningful decisions that are based on digital twin-stimulated scenarios and anticipate growing issues with more flexibility as well as better outcomes for the business as well as its customers.
In a similar way, J.-M. Erlendson Who happens to be the transformation engineering lead at Software AG goes on to note that optimizing the supply chain happens to be a process problem. Simultaneously, his organization makes use of MLNAI so as to predict process failures, design remediations, let the stakeholders know, and also manage all the implementations. He adds that in process optimization, AI can evaluate historical process implementation, pinpoint the root causes of why the processes happen to be slow or costly, and even enable the underlining of the areas when it comes to enhancement that can have the most valid impact when it comes to supply chain performance.
In a predictive capacity, as per Erlendson, AI can evaluate the challenges when it comes to instructions and how it can influence the performance of a supply chain. This enables the identification of major areas to speed up with control and automation.
When it comes to designing remediation, he knows that supply chain resilience takes the pole position. For example, AI can generate as well as evaluate scenarios so as to enable the creation of processes that can get flexed wherever they are stressed.
Refining the Product Launch Strategy
Precise forecasting happens to be important for effective product launches, as per Walker, who goes on to describe a recent example that focused on weather prediction data from a machine learning platform.
A cosmetic company went on to delay the launch of a hydration product in the Southeastern US as per Walker because of high humidity and rain forecasted for the month of September. The company shifted the regional launch to November when it anticipated certain drier conditions were about to be witnessed.
A much better analytical forecast makes the work when it comes to deciding on pricing, advertising, and positioning more seamless, says Walker. By way of accurately representing causals to production as well as manufacturing, a company can enable its products to rise higher. And at the same time, it can continue to keep new products right in front of the right customers and that too at the right time.
Taking Care of the Labor Shortages
It is well to be noted that the supply chain optimizing software also enables manufacturers to take care of challenges that are related to the skilled workforce as well as a tight supply of trained and even talented labor.
Hasenkamp notes that organizations that happen to be focused when it comes to investing in new tech require amping up the skilling of the incoming talent and that too along with their existing workforce, which may be, by the way, more familiar as far as the legacy systems and workflows of setup are concerned.
He adds that there are a few prominent challenges that organizations will have to address in the years to come, like the skill gap between planners and even the war of talent as the demand when it comes to skilled professionals outpaces its current availability. Next General platforms that go on to incorporate generative AI capacities, such as large language models, can also enable the digitization of the organization’s tribal knowledge and also automate the routine tasks and even create a playbook that is data-driven, which will in a way enable less experienced planners to go ahead and make certain informed decisions.
