For a logistics business, operational efficiency and transparency are key to fast and timely delivery. Yet the visibility of supply chains is overshadowed by the many gaps between the origin and final destination. Roads are unpredictable and often demand on-the-fly decision-making. Many companies are used to relying on previous experience that may not be relevant for today’s reality, manifesting in subpar results.
Companies who run logistics operations or deliver technology solutions for tracking supply chains have to collect data continuously, process it in near real-time, and transform it into actionable steps to profit from it. Without dedicated big data supply chain technology, promptly executing such a task is impossible.
By defining a clear strategy for data collection and analytics, logistics providers can ensure data-driven planning, operational efficiency, and satisfied customers. Let’s dive deeper into big data supply chain strategies, learn how data can help solve burning issues of planning peak demand, and see how companies like Amazon and UPS apply big data to solve their supply chain challenges.
- What is big data supply chain management and how can you find the right data?
- How does big data for supply chains make logistics more predictable?
- Ways to adopt supply chain data science without getting stuck at the beginning
- Where should you begin with big data supply chain analytics?
- How leading companies take advantage of big data supply chain analytics
What is big data supply chain management and how can you find the right data?
Big data analytics in supply chain management is a set of technology-powered analytical processes that help business owners sift through huge volumes of structured and unstructured data to identify common operational patterns, bottlenecks, and business opportunities.
In the case of logistics, big data can help with demand prediction, dynamic pricing, accountability, and performance optimization. The idea of supply chain data science isn’t how you process data but rather which exact data you process. Previously, cloud transportation software systems were limited to processing varied data on logistics maps. It was difficult to map specific data and relate it to a specific event. Also, the issue of data fragmentation ruined supply chain trackability when a lot of companies were just beginning to digitalize their operations.
Fast forward to today: Businesses have access to a wide range of smart devices that can transmit a wide range of information including text, images, video, audio, and geospatial data. IoT devices, video telematics, GPS, radar, and IR sensors help companies get data around the clock, fast, and over great distances. But data aggregation isn’t the only improvement modern technology brings; processing power has increased as well.
Cloud servers have enough computational power to crunch hundreds of thousands of records relatively fast. And to make things automated, artificial intelligence, machine learning, and data science can crunch numbers even faster, more accurately, and at scale. All processed information is then laid out on user-friendly and customizable dashboards for business owners to identify patterns, see bottlenecks, and devise new courses of action.
How big data in a supply chain makes logistics predictable
For companies such as Google and Facebook that built their revenue around big data, the benefits of big data are clear. It allows them to process more information and deliver better service. But how does logistics tie into this? What can you expect from the numbers? The answer is opportunity.
Companies that incorporate data analytics into supply chain management can reduce delivery costs during peak product demand and improve customer satisfaction in the process. Moreover, due to reduced costs, they can add new services such as an expanded delivery zone. Here’s how.
Match internal and external data
For a logistics company to learn where their supply chain is the busiest, they need enough relevant data. Thankfully, your business already possesses vast amounts of information. You just need to process it to make it useful.
Big data in supply chain analytics works effectively when information is gathered from various sources. Typically, these sources are internal (enterprise resource planning systems, supply chain management tools, sensors, track records, etc.) and external (weather data from stations and forecast systems, traffic control information, road construction data). If you’ve processed historical data you once used to find general patterns in customers’ behavior or vehicle performance, matching software will provide you with even deeper insights you never expected.
Once this information is grouped and sorted by a data analytics supply chain system, it becomes easier to identify whether any patterns are matched. For example, logistics companies can rely on internal data about driver availability and the busiest distribution centers to streamline delivery in areas with the highest demand.
But how do we know when and where demand is high? This is where we refer to historical and dynamic data. By matching dynamic data about traffic and weather conditions with historical data on fleet capacity and seasonal demand, we can route drivers more efficiently. The routing process can be further enhanced with dedicated location-based services that allow you to gather even more data to use in the future.
Here’s how big data works with IoT devices
Develop a custom simulation model
Now that we know how many drivers we need and what the traffic and weather will be, how do we know if the new route will be any better than the current route? Big data analytics supply chain allows for modeling real-life situations without risk. You can use your accumulated information to create a simulated event. A simulation system creates a sandbox-like environment where companies can perform an infinite number of tests, akin to how digital twins work.
Logistics businesses can visualize and track how new virtual delivery routes perform, how new distribution models operate, and whether the current batching model is functional. Companies can further enhance the simulation by supplying weather data, traffic conditions, and information on driver availability. Lastly, an analytics system can benefit from proper GIS services for capturing, sorting, and interpreting spatial and geographic data. Learn how Intellias helped create a 3D mapping solution for a data visualization system
Learn how Intellias helped create a 3D mapping solution for a data visualization system
Accelerate planning processes
There’s more to big data. You can use it to make your planning process not only faster but of higher quality. Using big data is one of the best ways to gather information on warehousing, planning, and batching.
You can use data to see how well your sourcing works, how many similar orders can be grouped into one truck, and whether the routes your drivers take are efficient. This technology can also pair a smart shipping container with a GPS module to see how orders move in real time and how they compare with historical data.
Ways to adopt supply chain data science to lower security risks
The benefits are clear, but how can you get into big data? To apply data science supply chain technology, companies need to address key challenges of security and data transmission. How you overcome them can make or break your supply chain, and avoiding these challenges is not an option.
Solving the data transmission challenge
One way to overcome the data transmission challenge is to send data through dedicated channels using microcircuitry. The idea is that the tracking sensor found on a vehicle’s micro board can be paired with a data transfer algorithm that beams information through a secured line directly to a heavily protected server.
To ensure that data isn’t sent through public networks, you can set up data transfer points that you know are secured and send data along that route. Finally, you can add a dedicated obfuscation mechanism that will mask the data traversal process so you can be sure your information lands where it should.
Solving the data security challenge
To solve the data security challenge, you can create a proprietary file extension that can be accessed only on specific machines. Providing a separate access point will limit where your data is viewed, and therefore will allow you to implement more security stratagems in a single entry point. As for vehicles, companies can invest in fleet security solutions to ensure ubiquitous protection.
Another way to protect data is to enforce custom encryption. If data is protected unconventionally, cracking it becomes harder and pricier. Anonymization is another way to make data secure. Even if data is lifted, it’s unusable if anonymized, as anonymization removes pointers that help to identify what data is about and why it’s collected.
Developing a robust internal security policy
Businesses need to train their personnel to protect their data; they also have to employ software security tools to ensure no external attacks are possible. A great deal of data theft doesn’t involve sophisticated network penetration attacks (not that they aren’t carried out) but rather human error.
If your employees don’t know how to follow security protocols, you’re vulnerable. Teaching your staff security hygiene will help you avoid costly data breaches. Simple but effective policies that mandate every employee use different passwords on different devices, always close apps if they aren’t using them, and avoid public networks take time but are well worth it. Transportation and logistics development Power your supply chain by creating a powerful big data analytics system with Intellias
Transportation and logistics development
Power your supply chain by creating a powerful big data analytics system with Intellias
Where should you begin with big data in supply chain analytics?
Big data analytics in supply chain management isn’t just useful in planning. Big data can help you gain a competitive edge by giving you a clear picture of the entire supply chain. However, to take full advantage of your information, you need to ensure the following criteria are met.
Aligning business and IT
For big data to work in supply chain management, companies need to ensure all their operations are restructured. A strong alignment of the IT department and business unit is essential. These entities need a mutual understanding of the challenges, risks, and benefits of big data supply chain usage. The transformation into an information-driven company is impossible without the alignment of IT and business.
Data collection equipment
All big data supply chain solutions rely on external devices to gather information. Your current vehicle fleet and roads must be equipped with a wide range of sensors for big data and AI systems to work.
Hardware is only one side of the coin. The more sophisticated the software you employ, the more data your sensors will be able to detect and gather. You don’t need expensive hardware to get more data; you simply have to show your current equipment where to look for it.
Having sensors and tracking software in your infrastructure and internal file management system will allow you to create more reliable strategies and identify bottlenecks not only on the road but inside your planning headquarters. Learn how you can build a holistic IoT transportation ecosystem to make the most of your big data solution
Learn how you can build a holistic IoT transportation ecosystem to make the most of your big data solution
For supply chain management, data science is critical and to yield results, data transparency is a must. All information assets and their owners have to be clearly defined. Moreover, data has to be sorted and explicitly defined across several databases to avoid improper data mappings.
Another part of data transparency is governance. Before you can use big data, you must eliminate incomplete, obsolete, or duplicate records, as failing to do so will compromise mass query results. A company might want to invest in a GIS system that will convert multiple data points into one proprietary format to avoid data incompatibilities later.
Data science skills
By combining the supply chain data science, companies can expect great results. However, you need to know how to apply the right technique for a specific use case. Often, this requires specialized knowledge of computational mathematics that are necessary to generate meaningful and reliable insights. In other words, you must have the right equipment and skilled data engineers in-house or externally to make the most of your data analytics in supply chain management.
How leading companies take advantage of big data supply chain analytics
While this all sounds great in theory, real-world examples are more compelling. Companies that use data science in supply chains have a better grip not only on their entire operations but demonstrate how expenses can be cut on common challenges such as last mile delivery.
Considered one of the pioneers of supply chain data analytics, Amazon has been leveraging big data for several years. Yet their most impressive use of big data is in shipping. The company uses big data to solve the last mile delivery problem in a unique way.
What Amazon anticipatory shipping looks like
Amazon combines supply chain management and data science with predictive algorithms to identify what customers will buy before they complete a purchase. These algorithms essentially allow Amazon to move products to warehouses or trucks that are as close to customers as possible long before they convert.
This big data use case is not directly tied to logistics but rather to eCommerce. Still, logistics companies can use similar big data techniques to forecast demand for trucks and drivers. Having the right number of trucks at the right time will most certainly manifest in more deliveries. Here’s how you can pair GPS and video telematics to gain a real edge in the fleet and trucking business
Here’s how you can pair GPS and video telematics to gain a real edge in the fleet and trucking business
UPS is another big-name company that’s using the power of big data to solve its logistics problems. Based on data on delivery times, fuel consumption, and GPS coordinates, UPS engineers found that drivers should not turn left. Applying data science in supply chain may look trivial, but financial reports prove otherwise.
UPS claims their drivers use 10 million gallons less fuel, have to travel 33 million fewer kilometers, and produce 20,000 fewer tons of carbon dioxide each year as a result of avoiding left turns. UPS has also reported that the no left turn policy has helped reduce the number of trucks dispatched by 1,110. All thanks to big data in logistics.
Big data isn’t a buzzword but a real and proven technology that’s slowly reshaping the logistics industry. It can be used to handle all kinds of issues, not just peak demand. From routing to matching to modeling, big data helps make supply chains efficient.
Neglecting to adopt this technology means being at a massive disadvantage. Large companies such as UPS and Amazon are already doubling down on it, and so are smaller businesses. With time, big data technology will only get better, and the possibilities will surely expand. To remain competitive, adopting big data is a must.
Looking for ways to implement big data in your logistics operations? Let’s talk about it! The Intellias team is here to consult you about technological solutions.