Imagine for a moment that you are the supply chain planner for a company that manufactures parts for automotive production with major clients overseas in Asia. To save money on shipping costs, you accept the long lead times associated with ocean shipping over air freight and send a large shipment of parts by boat. Once the parts have already been shipped, your demand planners revise their demand estimates and it becomes necessary to ship a large number of parts by air at great expense in order to meet the new demand estimate. By not assessing demand accurately before shipping, your company has left significant value on the table and incurred significant additional shipping costs.
What happens when you’ve been doing the same job for many years? Even if you began your work with little or no expertise in your field—let’s say supply chain management—no doubt by the time you’ve spent a few years performing the same or a similar set of tasks you gain new skills and improve the ones that you already have. Because you understand the supply chain better than you did when you were more junior, you’re better able to predict how it will react to different disruptions, and you can more quickly and more easily make snap decisions to preserve your supply chain’s agility and maintain optimal performance and on-time deliveries. In short, you learn, and by learning you make relevant production and shipping processes run that much more smoothly, more efficiently, and more profitably.
Blockchain. Big Data. Industry 4.0. Smart Factories. Terms like these get thrown around with some regularity whenever the future of manufacturing is under discussion. Often, it’s difficult to cut through the jargon and get to the matter that’s most relevant to you, i.e. what is this new technology and how will it impact your business? It’s a strange new world in modern manufacturing, and we at the flexis blog are committed to demystifying the terms that you may be hearing more and more frequently as you evaluate the ongoing health of your supply chain. To that end, let’s talk about machine learning, intelligent manufacturing, and the difference between the two.
Topics: Big Data
At a recent event, renowned consulting firm Deloitte revealed the results of a survey showing that only 14% of C-level executives were highly confident in their readiness to utilize Industry 4.0 principles to their maximum advantage. While other surveys have shown similar anxieties to exist throughout many different spheres of global manufacturing, we at the flexis blog believe that the new changes surrounding so-called smart factories, though significant, become less daunting as one learns more about them. After all, this new technology is explicitly meant to make life easier for businesses. In the spirit of demystifying the new global technological landscape, here are a few things you might not know about Industry 4.0:
In the autumn of 1999, Hershey’s was preparing for what they hoped would be a typical Halloween season. By the arrival of the holiday, it would prove to be anything but typical. In fact, the American candy giant would see an almost 10% drop in its stock price over the course of just one day. The reason? A failure to deliver more than $100 million dollars worth of Hershey’s Kisses and Jolly Ranchers candies to stores in time for Halloween. It turns out that Hershey’s had adopted a new order fulfillment system just weeks before their annual Halloween rush, and their IT hadn’t yet been successfully integrated into their value stream. The company would ultimately recover, but the incident still stands as one of history’s biggest supply chain snafus, proving that all supply chains are susceptible to risk and disruptions. Here is a ranking of some of the biggest supply chain disruptions:
In a recent research report, Business Insider found that when it came to machine learning, 53% of the company executives surveyed were interested in the emerging technology, but unclear as to its exact use cases and applications. Similar figures applied to executive attitudes towards other technological advances, such as artificial intelligence and 3D printing. Although machine learning in particular is already driving new Industry 4.0 workflows and fundamentally changing the way that manufacturers do business, it’s no surprise that many have trouble envisioning specific applications for it. The transformative power of new technological advances comes not from generalities, but from specific tools and methods for integration that must be carefully calibrated to specific business functions.
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.
Imagine for a moment that you’re on a flight from London to New York. You probably take It for granted that someone has charted an appropriate route at an appropriate altitude based on weather and air traffic patterns, and that departure, arrival, and flight time have all been carefully calculated based on past flights and current conditions. At the same time, no matter how much planning has gone into a flight, you probably also take it for granted that there is a pilot in the cockpit, measuring real-time information with her instruments and communicating with air traffic control to make necessary adjustments and course corrections as new scenarios emerge.
If you had walked onto a factory floor during the second or third industrial revolution, it would have been immediately obvious what was so modern about what you were witnessing. You would have seen raw parts being turned into complex products on a moving assembly line, or newly automated processes making use of modern industrial machinery and early computer networks. In the world of Industry 4.0, the so-called “fourth industrial revolution,” the differences in appearance might be more subtle. You might still see a mix of manual labor and automated, computerized systems carrying out various production tasks, while many of important innovation brought about by Industry 4.0 might remain invisible to you. You might even be prompted to ask, “what’s so modern about modern manufacturing?”
We discuss in great detail on this blog how integrated processes and optimized models result in enhanced operations, increased productivity, and more effective strategic vision. While these are certainly critical and worthy elements of discussion, they are part and parcel to a much larger concern we devote little conversation to: How these various planning and production techniques actually result in a more innovative way of doing business. Because, at the end of the day, a manufacturing company is a business, and a software solution or platform is only as valuable insofar as it helps a business develop and grow.
For example, take the idea of integrated production planning. Such a planning method is a core driver in helping today’s manufacturing companies (especially those in variant-rich industries such as automotive or packaging) not only reduce costs, but also create inroads for revenue generation and growth across the value stream.