The past three years have seen unprecedented shocks to the global economy at all scales. Manufacturers across the world have already devoted considerable amounts of time and resources to anticipating and proactively responding to the pressures brought on by the coronavirus pandemic. And just this past year, with the war in Ukraine once more sowing the geopolitical landscape with instability, the existing global supply chain disruptions have been compounded by an energy crisis and by rising rates of inflation worldwide.
But necessity is the mother of invention, and amid these challenges manufacturers are finding ways not only to adapt, but to make themselves more efficient, intelligent, and resilient in the face of the winds of change. In the manufacturing industry, 64% of enterprises (and counting) acknowledge the importance and necessity of developing AI-driven systems and processes. And a growing number of these companies have already begun to integrate AI across key business units and to derive value from them, paving the way to Everyday AI.
In 2023, the companies that get out ahead will be those that take concrete steps to develop their AI maturity, especially in the face of global challenges and demands that will otherwise produce a strain upon legacy data systems and sclerotic processes. Whether the next step is upskilling data analysts into data scientists, or introducing MLOps into model deployment and integration, manufacturers of all sizes are seizing the benefits of AI-driven operations.
As I’ll now discuss, four key challenges and opportunities will be top of mind for companies looking to adapt and thrive in 2023.
Industry 4.0: The Coming Horizon
Some trends keep trending, year after year, for a reason: they represent a compelling vision of the future. In manufacturing, Industry 4.0 is still the term on the tip of everyone’s tongue. It is the central piece of the “smart manufacturing” puzzle. By leveraging sensors, machine logs, quality information, digital twins, and much more, AI-enabled manufacturing enterprises can operate with greater efficiency and agility at all stages of the production life cycle.
With predictive maintenance, one of the core tenets of the Industry 4.0 landscape, manufacturers can use AI to analyze data from hundreds (if not thousands) of sensors and machine logs to develop models that accurately predict when machines and equipment will require maintenance before breakage occurs. Naturally, this allows companies to drastically reduce unplanned downtime, which by some estimates can cost plants upwards of $100 million (depending on size and sector).
But as many industry leaders know, the challenges of predictive maintenance can sometimes be obscured by the hype surrounding it. Though it can drive immense value, it is also difficult to implement. And what’s more, processes only produce value if the person designing and benefiting from them understands what’s being processed. When it comes to predictive maintenance in particular, there will be nothing to predict if the people running the process don’t understand failure modes or operational theory.
Kinks in the Supply Chain
Even as the global supply chain has begun to recover from the shock it suffered during the pandemic, it has been plunged back into uncertainty over the course of the war in Ukraine and rising rates of inflation. Everything from freight costs to global demand has been thrown into question, and the key objective for many manufacturers is to get ahead of the doubt with predictive analytics and other AI-enabled tools. With the ability to foresee and plan for upcoming obstacles, these companies will be able to stay the course without any major diversions.
One of the main elements of this strategy for manufacturers and the shippers they know and trust is blockage prediction. Because many manufacturing plants rely on the estimated delivery dates of both incoming and outgoing shipments, it is crucial that those estimates be as accurate as possible. In a volatile supply-chain environment where disruptions are the norm, using AI and machine learning to predict them before they happen is essential. Companies that have deployed predictive analytics for this purpose have benefited from prediction accuracy rate of 90% and a consequent 50% reduction in blockages.
Major geopolitical disruptions also reliably create fluctuations in demand. Whether billions of N-95 masks need to be produced on a dime or new forms of energy are suddenly required in the face of oil and gas shortages, being able to predict demand in the coming weeks and months is a massive advantage. That’s why manufacturers worldwide are turning to demand forecasting models and seeing rapid gains in value.
When it comes to the supply chain in 2023, then, the key terms are visibility and optimization. Whether companies are looking to maintain a commanding and precise view of shipping ETAs or to optimize dock use at their plants and warehouses and reduce truck wait times, their processes can be improved with an AI-driven approach.
Related to supply chain analytics but forming its own unique set of challenges and opportunities, energy management is on the minds of most industry professionals in manufacturing for the coming year. Whether we’re talking about matching energy production to forecasted demand, reducing the costs of that energy production, or developing productive capacities for a diverse range of renewable forms of energy at the right cadence, AI is an essential part of the picture.
Energy producers tend to be both large and widespread, given their role in supplying energy to communities across the globe and in all seasons. This means, of course, that manufacturing companies need to keep an eye on a vast array of data sources, from equipment status data to the varying market prices for different forms of energy in different countries and regions. They also need to take into account weather and temperature forecasts, as these variables can have significant effects on productive output.
While finding innovative ways of staying the course, industrial companies are also concerned with adapting for the future of renewable energies: wind, solar, nuclear, etc. But they can only lead the charge in these areas if they have the ability to develop them in accordance with the differing needs of communities across the world. For this reason, major industrial companies are deploying dual production and demand forecasting models for renewables to ensure that needs are always met.
Sustainability for the Future
All of this leads us naturally to sustainability, the North Star of the manufacturing industry. The vast improvements being made in supply chain and energy management, and the plans being laid and executed toward the coming Industry 4.0 are driving us toward a more sustainable future.
As demand for industrial products continues to rise, so does the emission of CO2 into the atmosphere. Consequently, the industry sector accounted for a quarter of total global final energy use in 2021. Companies across the industry are, for this reason, striving to attain Net Zero in the coming decades according to Scopes 1, 2, and 3 of the Green House Gas corporate standard scope classification.
The true Net Zero future involves cutting or offsetting all Scope 1, 2, and 3 emissions, not only at a single firm but across the entire industry. Achieving this monumental goal requires not only cross-industrial collaboration and responsible policymaking, but also the deployment of AI and machine-learning enabled processes to improve visibility into the scale of the problem at any given company and to drive quick and concerted solutions.
Given all the data they have access to, manufacturers are benefitting from the deployment of such models, like electricity and CO2 emissions forecasting, to move quickly and efficiently. When it comes to sustainability, the entire industry is benefiting from the explosion in AI, machine learning, and data science capabilities.