It’s crucial to maintain accurate visibility in today’s global network of goods production and transport. For example, if items arrive damaged, what caused the problem, and when did it happen? Was there a particular supply chain partner to blame, and are they a repeat offender?
Real-time data can provide the kind of supply chain transparency that answers those questions and others. Here are some of the compelling ways it helps.
Many logistics professionals see supply chain data at key points but not every stage. For example, a packaged salad brand may get insights into what happens to products once they leave a factory. However, it’s not as likely for the company’s logistics leaders to receive details about what happens at the farms that source the salad ingredients.
That’s changing, thanks to Sourcemap, an MIT spinoff specializing in end-to-end supply chain visibility for multinational companies. Businesses regularly use auditors to look for issues, but they only capture the problems present during a particular moment in time. Sourcemap is different because it collects continuous real-time data from every part of the supply chain.
Leonardo Bonanni, the company’s founder and CEO, explained, “We’ve taken our customers from a situation where they had very little control to a world where they have direct visibility over their entire global operations, even allowing them to see ahead of time — before a container reaches the port — whether there is any indication that there might be something wrong with it.”
The technology can help companies verify to their stakeholders that the products they sell were not produced illegally, such as through forced labor or illegal deforestation. Then, it’s easier for companies to safeguard their reputations.
Bonanni believes the time is right for his product and said, “We’ve seen the amount of data collected grow by a factor of 1 million, which tells us the world is finally ready for full visibility of supply chains. The fact is that we’ve seen supply chain transparency go from a fringe concern to a broad-based requirement as a license to operate in most of Europe and North America.”
Real-time data also gives company representatives the power to intervene before a mishap means that products arrive ruined. If perishable products like ice cream show up melted because a refrigerated truck failure went unnoticed, that cuts into the recipient’s profits, which is certainly an unwanted consequence.
However, the ramifications of such problems can be even more far-reaching for life-saving pharmaceuticals that require refrigeration. Also, when items go outside the required temperature range, the problem is not always broken equipment.
For example, leaders at Allergan Pharmaceuticals found that real-time monitoring of goods in transit removed potentially problematic blind spots. One case involved a truck’s data logger failing to pick up a temperature change in the vehicle’s cargo hold. However, a real-time data solution did detect it.
An investigation into the matter showed that the problem originated at a customs check stop. More specifically, someone did not fully shut the truck’s window after the goods examination stop. Cold air getting into the cargo compartment caused the temperature fluctuation.
Real-time data may not identify the cause of a problem. But, as the above example showed, it can alert people to changed conditions, triggering them to take a closer look and see what’s going wrong.
The COVID-19 crisis caused severe issues for supply chains worldwide. For example, as many leaders imposed travel restrictions and set new entry requirements, airlines canceled many passenger flights. Many of the planes used for those journeys typically carry cargo in their holds.
Statistics show there were 27,500 planes in the air at the beginning of 2020. However, by May, only one-quarter were still operating. The slowdown in air travel brought about by the pandemic also led to long backups at the world’s ports, particularly as supply chain professionals scrambled and relied more on the oceans to ship their goods.
Real-time data does not always alert people to all applicable supply chain backups, but it can certainly help. In one case, an advanced manufacturing company relied on real-time satellite imagery and other critical data to overcome transit delays. Taking that approach provided vital supply chain transparency that showed the client the world’s port activity. Then, leaders could make crucial decisions under pressure and feel more confident in the eventual outcomes.
One tool showed that 90,000 ocean vessels cross paths with each other as they move goods between continents. Some real-time data solutions provide location information for some of those ships, plus give in-the-moment statistics about ocean conditions, including currents and wave heights.
If the information shows weather conditions could cause delays or negatively impact fuel efficiency, the responsible parties could act in time to prevent such complications. Then, there’s a higher chance of goods arriving on time.
The availability of real-time data has opened new opportunities for using digital twins. They show a computerized model of actual assets, whether facilities, components, machinery, or something else impacting the supply chain.
Supply chain professionals often use digital twins to catch problems before products arrive on the market. Suppose a digital twin showed that a company’s new packaging was unlikely to sufficiently protect products shipped to other countries. Leaders could then figure out the associated deficiencies and fix them before rolling out its new materials. Addressing issues before production happens is only one way digital twins can minimize waste.
In an example that combines real-time data and digital twins, beverage brand Diageo sought to target waste stemming from the over- and under-filling of casks. The situation gets complicated because the barrels do not have a standardized size as many people expect.
Instead, there can be an approximately 20% size difference between casks in the same category. However, a digital twin to track the real-time fill volume prevented the wastage from over- or under-filling these non-standard containers.
It also facilitated a 33% reduction in the filling time for a 200-liter cask, meaning it was 99% full in less than one minute. The people who worked on this project said it had a positive impact on warehouse storage space for the casks, too. If more of the containers had just the right amount of liquid in them, logistics leaders could ensure they were not unnecessarily taking up warehouse space with extra casks.
These are some fascinating examples of how real-time data can be a game-changer in giving logistics professionals more visibility about their supply chains. However, decision-makers who are thinking about implementing it should do so thoughtfully.
For example, what challenges could real-time data overcome? What kind of infrastructure improvements need to be made so that people can utilize the potential of collecting real-time information? Answering questions like these raises the chances of real-time data achieving maximum payoffs in a relatively short time.