Even manufacturers themselves may sometimes forget how tremendous the global manufacturing sector really is. Manufacturing in the U.S. on its own, for instance, would be in the world’s top 10 economies. Because this sector encompasses so many different businesses with so many different missions and products, it’s easy to prove or disprove almost any prediction. Sure, someone among the incredibly diverse array of global electronics producers is probably using voice activated AI in their plants—just as someone else is probably bucking every emerging trend by continuing to eschew digitization and connectivity. Still, as general trends emerge, it can be helpful to identify and understand them. To that end, here are some predictions for the world of global manufacturing in 2020.

Let’s picture a hypothetical. You’re a sales and operations planner at a global manufacturer, specializing in a high-end variety of widget that other global companies tend to order in large quantities. Your sales cycle is fairly long, so every time a member of your sales team closes a deal it feels like a major victory. Recently, you’ve closed one of your largest deals yet, meaning that a large quantity of deliverables need to be produced in the immediate future. This will mean leveraging your production facilities at their maximum capacity for some time (potentially resulting in some wear and tear on your machines that will cause slowdowns later), but, like they say, “make hay while the sun shines.”

In the past five to 10 years, real-time information has become a key value-added proposition for bolstering efficiency and decreasing waste in modern, digital supply chains. Businesses have used it to power more agile, responsive processes within their own value streams, creating environments that are primed for improved data-quality and easier analytics integration. The question remains, however, is this technology being utilized to its maximum effect, or are there still use-cases for real-time information that most businesses are failing to fully leverage? The answer is resoundingly the latter, as evidenced by these four surprising uses for real-time supply chain data. 

Any production planner who has spent time working on non-clocked production processes can tell you that it presents challenges and hurdles that simply don’t exist on assembly lines or in other linear production processes. And yet, for some manufacturing outfits, non-timed production is the best way to maximize their machine and personnel resources while maintaining a relatively flexible and adaptable production environment. How do we reconcile the difficulty of scheduling production in a job shop with the obvious value that it presents for many businesses, and what can that tell us about the future of job shop scheduling? 

Imagine you’re working in tech support, and you receive a call from someone who’s having trouble getting his phone to send and receive text messages. You try all of the usual tactics, asking the caller to turn the phone off and on again, etc., before checking to make sure that the phone is running the latest version of its operating system. The caller concedes that it probably isn’t, but as you walk him through the process of updating he continues to run into problems. “How,” he asks, “do I see what operating systems I am running?” “How do I access my settings?” “How do I get to the home screen?” It is only as you dive deeper into the rabbit hole that you realize that your interlocutor doesn’t have a smart phone at all, but an old rotary phone without the slimmest chance of ever accessing the internet.

Seasonality, which refers to regular, predictable fluctuations that recur year over year, has traditionally been a major factor in automotive manufacturing. Since car sales often spike in spring and autumn (when new models are traditionally released) and drop off in winter and summer, manufacturers can and do factor seasonal slow-downs and increases in demand (potentially including demand for new parts) into their production processes. With the rise of Industry 4.0 and the emergence of an increasingly global supply chain, however, the nature of seasonality is rapidly changing. Let’s take a look at how seasonalities operates in modern manufacturing. 

It’s safe to say Big Data is here to stay. Since its introduction in the manufacturing landscape in the early 1990’s, Big Data has demonstrated its value proposition in the capacity for grouping, sorting, and analyzing large and complex data sets into executable actions, provides planners and managers the capability to apply predictive analytics and other forward-looking logistic strategies to increase the efficacy, efficiency, and cost-effectiveness of planning and production programs.

Big Data has since found a home working in tandem with other supply and manufacturing movements such as Industry 4.0, Advanced Analytics, and The Internet of Things (IoT). Alongside these technological developments and platforms, Big Data has helped companies gain increased insight and visibility into a number of critical planning and production functions such as forecasting, modeling, data analysis, and the implementation of integrated sales and manufacturing principles for a more streamlined production cycle.

Ask anyone in the automotive industry about the future of artificial intelligence (AI) and you’re likely to hear one thing: Driverless cars. Yes, the development and proliferation of driverless cars or assisted driving is perhaps one of the greatest innovations on the horizon in today’s automotive manufacturing industry. Yet even so, AI has the potential to impact the automotive manufacturing supply chain in equally profound and interesting ways beyond the idea of the driverless car. In fact, AI has the potential to be a truly disruptive force in the way automotive manufacturing companies produce vehicles and how the consumer interacts with the end product.

With AI as an increasingly common technology platform, the automotive industry is set to experience significant changes in the coming years in terms of production and supply chain management. As vehicles become more integrated, individualized, and complex, manufacturing companies will have to leverage more lean methods of production and supply chain logistics to keep pace with the demands of such a variant-rich industry.

Fact or fiction. Trend or mindset. Fad or fixture. While Big Data has certainly permeated nearly every aspect of today’s manufacturing and supply pipeline, some industry analysts still question the validity, value proposition, and staying power of Big Data for companies as they strive to streamline their operational platforms and leverage lean manufacturing principles for optimal productivity and profitability.

First introduced to the manufacturing and supply chain landscape in the early 1990’s as a method of grouping, sorting, and analyzing large and complex data sets into executable actions. The sorting of these large, unstructured datasets gives manufacturing companies the capability to apply predictive analytics and other forward-looking logistic strategies to increase the efficacy, efficiency, and cost-effectiveness of planning and production programs.  

So much in the discussion of modern manufacturing surrounds the subject of Industry 4.0 and its influence on the daily, weekly, monthly, and even annual processes associated with the global manufacturing industry. In fact, one could argue the term Industry 4.0 is tossed around by industry insiders without truly understanding its place in the context of manufacturing and supply chain processes on a global stage.

Whether it’s misinformation about how Industry 4.0 came to be or the ways in which manufacturing companies can deploy the concept of Industry 4.0 as a core driver of moderneized, intelligent production, there’s still much supply chain planners and managers can learn about Industry 4.0 and its applications in today’s global manufacturing and supply streams.