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.
IT pioneer and philosopher Ted Nelson, who coined the term hypertext, once famously said, "The good news about computers is that they do what you tell them to do. The bad news is that they do what you tell them to do." Historically, in the automotive supply chain, this couldn’t be more true. New technological developments like early computerized workflows and simple process automation were hampered by information silos and integration issues not because the technology lacked sophistication, but because they still had to be told what to do in very specific ways.
Let’s think back for a moment to the early days of Facebook. Today, the social media giant boasts more than a billion users worldwide, but there was a time when its base was just an infinitesimal fraction of that number—a handful of early adopters scattered across American college campuses. Pretty quickly, that handful, having influenced others to join up, grew to a critical mass. People across the world felt that they had to be on Facebook because their friends were already using it, and the more users joined, the more attractive the social networking site seemed to potential users. This quickly reached a tipping point and led to the explosion of users that they’ve seen in the past few years.
Imagine you own and operate a pin factory at the dawn of the Industrial Revolution. One day, you come in and announce to your workers that you’ll be implementing steam powered machinery into your production processes, completely reimagining many existing workflows in the process. How do you think your employees, especially those involved in planning out production workflows, are likely to react? Some of them might be excited or intrigued, certainly, but many others are likely to meet the news with apprehension or even distrust. After all, they were doing just fine making pins by hand all this time.
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.
For decades, production planners in non-clocked production environments have been trying to optimize their job shop scheduling processes, and for decades the problem has continued to elude them, owing in large part to the tremendous complexity of uncovering the most efficient route for each product to take through a non-linear production environment. Luckily, new advancements in supply chain technology are constantly presenting planners with new tools and tactics they can use for gaining the maximum possible value from their production workflows. In many ways, the most significant of these advancements come in the form of the new technologies that make up the Industry 4.0 revolution. But what, exactly, is it that makes Industry 4.0 and job shop scheduling a match made in heaven?
Think about some of the biggest supply chain risks for a moment: unexpected weather events or natural disasters; price fluctuation for oil or other transport factors; inaccurate forecasts—all things that require an immediate response in order to prevent complete supply chain shutdowns. Now, think about most sales & operations planning (S&OP) workflows: focused on mid-term, quarterly or yearly cycles; designed to support longer-term goals like new product launches—quite simply, the opposite of immediate. Of course, S&OP is crucial to shaping a business’ mid-term strategy, but when disruptions hit there’s rarely time to wait for the next quarterly planning meeting in order to respond. As a result, without a secondary workflow to cover the weekly or monthly planning timeframe, the inherent risks in longer-term planning processes are significantly amplified.
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?
Imagine a scenario: Your company has contracted a shipper or freight forwarder to complete a delivery of parts to one of your customers. Because of extensive data-collection during your research and development for the parts, you know that high temperatures over a prolonged period of time can increase the part’s failure rate. As a result of a shipping delay, these parts spend too much time in a container that’s not properly temperature controlled.