Discussions around the effects of climate change have been among the dominant topics of conversation in the first two decades of the 21st century. As global leaders in government and business consider what steps must be taken in order to ensure the health of our natural resources and ecosystems, businesses can almost certainly expect changes to the way that supply chains operate. Many within the worlds of manufacturing and shipping are beginning to track their carbon footprints and overall environmental impacts, but still others are unsure of the potential considerations involved. If you’re in the latter camp, we hope that these four facts will help give you a grounding in conversations around green footprint optimization.
Just as the modern factory is adding new, intelligent technologies in order to create connected, interoperable workflows, the modern supply chain is rapidly becoming smarter, more networked, and more technologically advanced. Though the so-called fourth industrial revolution gets most of the attention, there is another revolution occurring simultaneously within the world of logistics, and it’s changing the way that products make their way from production facilities to customers. In the spirit of Industry 4.0, some have taken to referring to this new logistics paradigm as Logistics 4.0—but what exactly does this term mean?
The rise of Industry 4.0 is already impacting the way that supply chain managers do business. As it continues to promote digitization and interoperability across all touchpoints on the global value chain, it will no doubt bring about significant changes across a variety of different supply stream operations. No doubt one of the most significantly impacted processes will be transport logistics, which might lead one to wonder, “what will transport logistics look like in the Industry 4.0 era?”
Advanced analytics continues to be one of the most talked about new advances in supply chain technology. Also known as big data analytics, this increasingly-important tool can increase the power and accuracy of a given company’s predictive forecasts and suggest prescriptive process improvements by analyzing mountains of information that would be impossible to comprehend if they had to be analyzed by hand. While integrating this new technology remains a significant pain point for many manufacturers, it also represents a unique chance (especially in the early days of its widespread adoption) for businesses to gain a competitive advantage and increase revenue. Here are some surprising facts about it:
It’s long been an open question in the world of business: which is a bigger hurdle, planning or execution? As the global supply chain has become more sophisticated, however, we’ve gotten a wealth of evidence that for the majority of companies, execution is the more frequent stumbling block. In an informal poll a few years ago, Dick Ruhe at Blanchard found that 76% of the more than 300 respondents said that the most common experience at their company was "good planning and poor execution" (compared to just 4% who said "good planning and good execution", 8% who said "bad planning and bad execution", and 13% who said "bad planning and good execution"). Though these statistics don’t speak to supply chain management in particular, they do give an accurate sense of how difficult it can be to put even a well-conceived business or production plan into action.
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.
Plenty has been written on the right way to choose the supply chain management technology that best fits your company’s needs, much of it focusing on broad organizational points like defining specific needs and long term business goals. Coming into the IT procurement process with intra-operational buy-in and a well-founded idea of how new technology should integrate into your workflows and key performance indicators (KPIs) is, of course, crucial to finding the right solution, but that’s not the end of the discussion. Once you’ve assessed your specific needs and your short- and long-term goals, how do you evaluate the technology itself?
Although automotive manufacturers have been hearing for years that Big Data is the next big thing, studies often show that executives, not just in automotive but across many different industries, fear that their organizations aren’t ready to take advantage of the new advancements in analytics. Big Data analytics can and will be a huge value-added proposition for companies hoping to stay competitive in the world of Industry 4.0, but it’s true that reaping the benefits of new technological insights often requires significant changes in workflows and IT infrastructure. Luckily, these changes are often not as daunting as they first appear. Here are a few suggestions for getting the most out of your advanced analytics.