- 1 Why Use Machine Learning in Logistics?
- 1.1 1. Accommodate More Volume with Near-Perfect Accuracy and Reliability
- 1.2 2. Shippers that Save Money on Labor through Automated Auditing can Reinvest in Logistics
- 1.3 3. Integrated Systems Will Conduct Real-Time Audits Before Completing Payments, Diminishing Expenses in the Processes
- 1.4 4. Smooth Auditing of Expense Claims
- 1.5 5. Streamline the Complex Process of Freight Auditing
- 2 Why Machine Learning is a Perfect Match for Freight?
- 2.1 1. The Freight sector creates massive data sets that can only be handled by Machine Learning.
- 2.2 2. For ML, Repetitive Logistics Activities are Perfect.
- 2.3 3. When People Multitask, they are Prone to Make Mistakes. ML, on the other hand, is not.
- 2.4 4. Freight does not take a break; Neither does ML.
- 2.5 5. Models of Machine Learning are Constantly Improving.
- 3 Conclusion
Why Use Machine Learning in Logistics?
Machine learning and AI are used in the logistics industry, from Storage, warehousing and materials handling, Packaging and utilization, Inventory Control, Transportation, information and control.
If you’re looking for the benefits and advantages of machine learning and Artificial Intelligence in the Logistics Industry, then you’ll love this guide.
Benefit of Machine Learning and Artificial Intelligence in Logistics and Supply Chain
Machine Learning offers a wealth of benefits, particularly to Freight Payment and Audit Companies. Specifically, we are going to see in brief how machine learning (will) helps the Logistics Industry
- Accommodate More Volume with Near-Perfect Accuracy and Reliability
- Shippers that Save Money on Labor through Automated Auditing can Reinvest in Logistics
- Integrated Systems Will Conduct Real-Time Audits Before Completing Payments, Diminishing Expenses in the Processes
- Smooth Auditing of Expense Claims
- Streamline the Complex Process of Freight Auditing
1. Accommodate More Volume with Near-Perfect Accuracy and Reliability
A data capture solution powered by machine learning not only processes documents faster but can also manage larger amounts of documents. Furthermore, it can do it with the same or even higher accuracy than a human, up to 99%.
When performing monotonous, repetitive activities, an automated system, unlike a human, does not become exhausted, bored, or careless. It does not need to rest and complete hundreds of data collecting activities in minutes rather than hours. High levels of accuracy are a consistent result, thanks to powerful machine learning.
2. Shippers that Save Money on Labor through Automated Auditing can Reinvest in Logistics
Doubtlessly, there is a lot at stake when freight charges account for 10% of a company’s overall spend, and 30% of all freight invoices are erroneous. Separating the correct tools from the hype can be a time-consuming chore for accounts departments. Moreover, if a company is new to the logistics industry, it may not even know where to begin.
Also, the correct technologies can help you build a solid logistics “data warehouse” that can help you make tactical and strategic decisions. Having data to manage your logistics operations daily or long-term is necessary, and having reliable data is even more crucial.
Thus, savings from freight invoice auditing and trends alignment can be re-invested in the company to take advantage of other forms of automation, such as robotic picking, packaging, labeling, and even delivery. In some ways, the path to autonomous freight auditing of bills will be a necessary stepping stone toward complete supply chain automation.
3. Integrated Systems Will Conduct Real-Time Audits Before Completing Payments, Diminishing Expenses in the Processes
Invoice auditing has always gotten relegated to the less-than-truckload and full-truckload modes. After all, these modes are associated with the smallest number of bills. However, in the realm of e-commerce, this is impractical.
Notably, shippers send more parcels than any other method; so in 2021, it will be like starting a larger project with freight invoice auditing software and realizing that invoice auditing must transcend mode, location, and time.
Moreover, it is worth emphasizing that connected systems via an API and EDI will enable real-time audits before shipments ever leave. Indeed, auditing has typically been a retrospective procedure, but imagine being able to audit a freight quote in real-time. This timing ensures that the freight gets paid accurately as soon as the carrier portal changes, completes payment upon verification, and even performs a second check later to confirm accuracy. This system is the efficiency model that the supply chain sorely needs to stay afloat and keep costs in check.
4. Smooth Auditing of Expense Claims
Expense claim auditing is another transactional finance duty that could benefit from AI-assisted automation. Freight businesses must check that receipts are legitimate, that claimed amounts match receipts, and that they follow company policy. While the ultramodern travel-and-expense solutions might streamline the claim process, auditing still gets done by hand.
Rather than depending solely on representative sampling methodologies, machine learning algorithms can allow businesses to examine an entire population for irregularities. When audit teams have access to the whole data population, they may conduct tests more focused and deliberate.
Additionally, Machine Learning algorithms can also “learn” from auditors’ conclusions on specific objects and apply the same logic to items with similar characteristics.
What if artificial intelligence and machine learning could help with this process, auditing 100% of all claims and only sending the ones that are irregular and suspicious to a manager for approval? The system could read receipts in any language, verify their authenticity, and compare them to the policy.
5. Streamline the Complex Process of Freight Auditing
Machine Learning can also help auditors do the following:
- Automate auditors’ manual tasks, such as documentation.
- By parsing data, analyze the entire volume of organized and unstructured data from financial records.
- Detect anomalies such as odd payments or activities that manual audits would miss.
- Review and analyze historical transaction data to make forecasts about future risks and events.
Auditors must evaluate thousands of documents and contracts, monitor the implementation of regulatory changes, and conduct inquiries into unclear transactions.
Moreover, auditors rely on sampling to attain this goal because of the enormous volume of data and the high cost of manual labor. In this regard, you can use Machine Learning to go beyond sampling, automatically analyzing all available data and bringing high-risk documents to human attention.
While auditors spend a significant amount of time going through pages and accessing individual digital files to review them, they almost always use a checklist or follow the same procedures. Some of these checks can be automated, allowing auditors to concentrate on activities that need human thought and understanding.
Why Machine Learning is a Perfect Match for Freight?
Machine Learning has several advantages over how things typically get accomplished in the freight industry. The following are five advantages of machine learning in Freight Industry:
- Analyzing massive amounts of data
- Performing repetitive duties without making a mistake
- Multitasking while moving quickly
- Working on a 24-hour basis
- Constantly improving
Each of these advantages applies to various aspects of truckload transportation. Read on to have a closer look at each of them:
1. The Freight sector creates massive data sets that can only be handled by Machine Learning.
The freight business creates vast volumes of data, with approximately 400 million class 8 shipments every year in the United States alone. Computers may track thousands of data bits for each shipment, including pickup and drop-off times, facility wait times, pricing, tender acceptance, fuel utilized, and GPS coordinates throughout the shipment.
This much data would be impossible for any single individual or even a group of people. There would be 1 billion permutations if you operated in a marketplace with just 1,000 shipments accessible daily and tracked combinations of three shipments at a time. While no single person could handle this amount of data, an ML model can take it easy. The more data given to ML models, the more effective they become.
2. For ML, Repetitive Logistics Activities are Perfect.
There are many aspects of repetitive logistics where ML shines. Here are a few of its capabilities:
- Finding an ideal carrier
- Vetting the page for quality and driver record
- Pricing the shipment
- Confirming tender acceptance
- Arranging pick up and drop-off
- Resolving issues that arise
- Finding a replacement in the event of a falloff
All these tasks and more are the reasons ML performs perfectly for each truckload shipment.
You can find patterns everywhere you find repetition. The build of ML models aims to spot patterns and make independent judgments based on those patterns.
3. When People Multitask, they are Prone to Make Mistakes. ML, on the other hand, is not.
Throughout the day, supply chain professionals may manage dozens, if not hundreds, of shipments. Because of the scale, complexity, and need to react quickly, supply chains are brimming with multitasking employees. Multitasking invariably results in errors. A mistake with a load at any point in the process could trigger a cascade of supply chain issues farther down the line.
Multitasking is a frequent computer capability. In less than 20 milliseconds, ML models can evaluate millions of example data pieces and uncover hundreds of patterns. Not only can ML models perform several analyses at once, but they can also complete jobs fast and accurately.
4. Freight does not take a break; Neither does ML.
Freight businesses operate 24 hours a day, seven days a week, including holidays. People can only work a certain number of hours per day, resulting in service lapses and errors when responsibilities are passed from one person to another as shifts change.
ML models, on the other hand, do not take a break and operate continually. They can keep up with the speed of freight, ensuring that coverage and support are never compromised.
5. Models of Machine Learning are Constantly Improving.
The rate at which people learn in a certain area declines and eventually ceases. Consider a freight broker. According to one Jobsite, a freight broker’s average tenure is only 1-3 years. Brokers with more experience learn at a slower rate in years 4-10 of their careers than in years 1-3.
Machine learning models learn quicker and for longer periods than humans because they evaluate larger volumes of data than humans can. Moreover, models in machine learning do not change jobs or retire.
Technology companies are always seeking opportunities to improve their services to enhance freight invoice auditing. Because the freight invoice auditing trends represent a growing consensus that automation is critical to success, new services must add value by resolving disputes quickly, processing claims payment reimbursements more quickly, and more.
Technology will become the most important factor in all aspects of machine learning and auditing throughout the supply chain. Consider the effects of freight invoice audits on compliance as well. Shippers could lower their risks without having to lift a finger. This portion of the supply chain is where analytics comes in, minimizing overbilling, underbilling, inaccurate freight classification, inadequate carrier reporting of freight status, and a slew of other data points.
Being an expert in the Freight Payment and Audit industry, iTech ensures that Machine Learning leverages all of its services, including data input outsourcing, freight auditing, medical insurance verification, and more.
Reach out to our team today!