Imagine for a second that you’re entering a friendly betting pool for the 2018 World Cup. Germany won the contest in 2014 (the most recent tournament), so you decide that it stands to reason that Germany will win again this time around. Hindsight being 20-20, we now know that you would have lost your bet, as France won the tournament and Germany didn’t advance out of the first round. Your betting strategy of assuming that past results would continue to hold ultimately wouldn’t prove to be the best approach.
Let's say you've got big event coming up—maybe an awards ceremony, or an important anniversary. You and some of your friends are going to the event together, and to make the whole affair a little more special you decide to rent a limousine take you there and back. Though the venue is only an hour’s drive away, your friends’ homes are spread throughout your town in ways that make planning the optimal order in which to pick them up (and drop them off after the party’s over) a challenge. On top of that, not everyone will be ready at exactly the same time, and those who would be picked up later in the process would like to know in advance so that they can spend more time preparing. Where do you begin when it comes to planning out a tour that works for you?
As we enter an increasingly digitized era in supply chain management, owing to new technologies from IoT sensors to real-time freight tracking, the hurdles that face manufacturers and logistics providers alike are becoming ever more complex: software integration are becoming increasingly difficult, longstanding information silos are suddenly becoming huge operational hurdles, and increased globalization is adding complexity to virtually ever corner of the supply stream. What waits on the other side of those challenges? A world of increased connectivity and the promise of the Industry 4.0 revolution. Anyone who’s been following the global automotive supply chain the past several years know that, now more than ever, success is often a matter of turning mission critical data into concrete business insights.In the spirit of turning data into insights, here are a few statistics that might shed some light on the current state of supply chain management.
In theoretical computer science, the traveling salesman problem asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?" Anyone who has worked in transport logistics or transportation management knows that in most cases there is no easy answer to this question, and that finding the optimal route between different cities or even different stops along the same tour can be a serious logistical challenge—one that requires planners to manage customer delivery windows, anticipate traffic patterns, and optimize time and distance.
Let’s say you’re moving into a new home. After a long day of loading boxes and furniture into your car and driving them to your new place of residence you’ve finally transported everything into your new house and you’re almost ready to start unpacking and get the place settled. Before you get down to the work of opening up all of your packed boxes, you realize that you haven’t eaten all day, and should probably whip something up before you do any more manual labor. It should be pretty easy to find your kitchen supplies and snack foods, because you made a list of what was in each box before you moved them. One problem: you don’t know which box the list is in.
These days, when most people think of automation, one of their first thoughts is of self-driving cars. What many people don’t realize, as they picture themselves magically napping away their morning commutes, is that when it comes to autonomous vehicles there are actually six levels of autonomy. At level zero, you have a standard automobile, which requires the driver to make every decision and maneuver. At level five, the car itself makes and carries out all of the decisions without any human intervention. In between, we find cars that can maintain speed and avoid other cars on the highway, cars that can change lanes and make turns unassisted, and cars that can perform automated interventions in crisis situations like potential spin-outs.
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.
New technologies—from steam power to modern computers—have been the driving forces in supply chain management since the beginnings of industrialized society. Today, supply chain technology is changing at an exponential rate, providing supply chain planners with possibilities that would have seemed like science fiction even a few decades ago. If you follow the news and trends in SCM, you’ve no doubt noticed that machine learning (ML) is often touted as the next major innovation in this long line of technological evolutions—but what, exactly, is it, and how can supply chain managers put it to use?
Let’s say you and a coworker are attempting to find areas of waste in your supply chain. You have a large conference table on which you’ve laid a file that contains all of the transport plans utilized by the company for the past few years. When your coworker hypothesizes that a different grouping of goods would improve fuel efficiency, you need new documents with additional information, meaning that you have to leave the conference room and descend to the basement level where the files are kept. By the time you’ve returned, a new idea has occurred to your coworker, and you have to make a new trip to wherever your files are stored in order to retrieve the necessary information. The result of all this walking to and from the files? Some good cardio, but no plan to speak of.
Each year, topics like big data, advanced analytics, machine learning, and artificial intelligence dominate conversations about supply chain technology, becoming the focus of increasing amounts of speculation as the real power of these technologies becomes clearer and more apparent. As a result, it can be difficult to parse the jargon from the meaningful discourse and the pragmatic analyses from the wishful thinking when deciding how to set expectations for the evolving global supply stream. In the spirit of helping to put all of the hype into context and give supply chain managers the tools they need to look towards an ever-uncertain future, here are a few predictions for the future of advanced analytics: