The Unstable Supply Chain and Technology
The supply chain is never stable. There is risk at every turn that can cause disruptions up and down the supply chain. One of the biggest issues with risk management has been in how risk is assessed. Many events and interruptions cannot be accurately predicted. Or can they?
Supply Chain Risk Management software is giving companies along the supply chain a better sense of their risk, both now and into the future. Using sophisticated technologies, such as artificial intelligence and predictive analytics, risk assessment is not only more comprehensive and accurate, but it is also completely changing how risk is managed. Risk management has become a proactive and actionable tool for competitive advantage.
Artificial Intelligence and Advanced Analytics
Gartner predicts the use of artificial intelligence and advanced analytics will continue to increase, identifying these specific technologies as one of the eight supply chain technology trends in 2019. Artificial intelligence, it says, “supports the shift to broader supply chain automation that many organizations are seeking. For example, AI can enhance risk mitigation by analyzing large sets of data, continuously identifying evolving patterns, predicting disruptive events along with potential resolutions.”
When humans attempt to assess risk, they are limited by their own capacity. They can only review so much data at one time and that data is constantly changing, creating an unending cycle of assessment that is quickly outdated even before results have been solidified. Artificial intelligence, on the other hand, can be embedded into supply chain risk management software to gather virtually unlimited data points. The big data surrounding the supply chain is suddenly consumable and can be operationalized to drive more confident decisions. It’s so effective, Gartner says 37 percent of organizations have already implemented it in some form, a 270 percent increase in the past four years.
But artificial intelligence isn’t the only technology inherent in supply chain risk management software. As Gartner referenced, advanced analytics include predictive analytics and prescriptive analytics that are also playing major roles in how companies are analyzing the big data to drive those decisions. Predictive analytics “identify data patterns and anticipate future scenarios – as well as prescriptive analytics – a set of capabilities that finds a course of action to meet a predefined objective.”
Together, these technologies are providing companies a broader picture of their risks across time and space, then offering a path forward to mitigate those risks through preventative planning.
Supply chain risk management software is giving those involved in the logistics portion of the supply chain some much-needed help. Instead of manually tracking weather patterns, infrastructure and traffic issues, natural disasters, social unrest and temperature fluctuations along every lane of every mode of transportation, technology is doing the heavy lifting at scale.
Risk Management in Logistics
There is a significant amount of risk in logistics. Some companies are facing it head-on with the use of software, but others are slower to jump onto the bandwagon. Forrester recommends executives invest in new technologies to stay ahead of the competition and avoid public failures, yet it says adoption of a formal risk management program is still relatively slow.
Perhaps shippers and carriers need some convincing. Logistics has always been managed the same way and there’s no need to change course now, right? Not so fast. Amazon and other major retailers have transformed the supply chain as customers expect all of their products within a day or two of ordering. This puts pressure on companies to produce more product faster, requiring parts and supplies faster from their vendors. Everyone in the supply chain is squeezed in order to keep up with demand.
Manual processes no longer suffice. There’s simply too much data, too many stakeholders and too much at risk. Customers, no matter if they are the end consumer or the widget manufacturer, demand more. A single, seemingly small interruption anywhere along the supply chain poses a significant risk that a product or part will be delivered on time and in the proper condition.
Bringing AI and Analytics to Logistics
Supply chain risk management software aims to streamline and enhance data collection, first and foremost. That’s where AI comes in. But even with all of the data collected, it can only be useful if it is presented in a way that makes sense. Rather than massive spreadsheets that require the user to perform their own analysis, supply chain risk management software analyzes all of that data continually, perpetually, to constantly deliver the picture that companies need to make better decisions based on that data.
When it comes to logistics, companies need to know when delays are likely based on a shipment’s mode and schedule. They need to be able to identify areas of risk to navigate the impact of natural disasters. They need to reduce overspend on refrigerated freight without compromising quality. And they need to be able to perform root cause analysis on lost loads.
Unfortunately for skeptics, manual processes, ad hoc applications, and unintegrated data cannot detect potential risks accurately, let alone days in advance so something can be done to mitigate those risks. It’s not as simple as watching weather reports and reading radar maps. Those change by the minute. Add up all of the miles of lanes a shipment may travel and those weather resources begin to generate quite a bit of data that is nearly impossible to manually track for every shipment.
What happens when a shipment is scheduled to travel in an unrefrigerated truck along a typical route on a typical summer day but then there is an unexpected temperature shift somewhere along the lane, spiking temperatures for several hours? A static logistics plan for that shipment has no chance to ensure the freight maintains its quality under those conditions.
What’s needed is a dynamic plan that automatically consumes large amounts of data about the origin, destination, pickup time and arrival time and simulates the entire lifecycle of the shipment with forecasted conditions included. Only AI- and predictive analytics-enabled software can do that. It can also provide a scoring mechanism to identify at-risk shipments and the likelihood of those risks having an impact.
This is an important feature in a supply chain risk management solution as it prevents companies from rushing to action if the risk is only slight. They can weigh their options before changing a lane, a mode of transportation or a date. They are armed with the data they need to make an educated business decision as much as 10 days before the shipment is scheduled.
Predictive analytics kicks in to graphically represent those threats on a map, connecting the dots between the threats, the supplier and even the customers. Reports, alerts and notifications ensure nothing is missed. Every planned shipment is being monitored, evaluated and predicted for on-time delivery, with recommendations on how to avoid any potential interruptions.
By automating the data collection and the task of finding lower-risk alternatives, companies save time, costs and reputational damage. They can adjust schedules, equipment, mode and lane to be more nimble to changing conditions that could impact delivery and quality.
No manual process can provide this level of detail, responsiveness, agility or accuracy. Even with a team of professionals, the sheer amount of data that must be gathered and analyzed makes it impossible to manage. Risks are missed, putting the entire supply chain in a precarious situation.
For any company in the supply chain, continually improving logistics is critical to remaining competitive. By leveraging the technologies in supply chain risk management software, organizations are investing in the future. As this market continues to grow, companies who lag behind will be left behind.