In 2008, Dr. Raymond Panko published a paper detailing examples of human error in data entry. His findings? When manually entering data into a spreadsheet, the probability of human error was 18-40%. And that number is likely to increase as spreadsheets grow more complex.
In other words, errors are common, even with the best intentions. What does that mean for a shipper dealing with hundreds or thousands of freight invoices monthly?
One solution is to turn to big shipping data. Shippers can get a handle on their expenses by tracking freight invoices and improving their logistics strategy.
Far from a buzzword, big data analytics in logistics and supply chain management are helping companies gain insight into their current performance while optimizing future operations. But to truly bring value, it must be done right. According to analysts at McKinsey, "Most of the data generated in a supply chain falls outside the scope of just one enterprise or entity," a dynamic that makes it hard to know if you're dealing with good or bad data or even the original source. Or in some cases, there are so many data sources and raw data it can feel overwhelming, leading to inaction and not getting the most out of the data. That's why it's critical to have an audit partner who can ingest data across all systems.
"Big data" refers to volumes of data that are too large or come in too quickly to process using traditional methods. It can include structured data (meaning it is clearly defined and searchable, like phone numbers or addresses) or unstructured (the data is qualitative and must exist in its native format, like social media posts).
With capabilities like Internet of Things technology becoming more commonplace, big data is more accessible than ever before — the trick is knowing how to benefit from it. The sheer size of big data could potentially overwhelm a business. But companies can also mine data for meaningful insights and use the information gleaned from it to make better business decisions.
Big data has five characteristics, known as the five big V's: variety, veracity, velocity, volume, and value. Each pertains to intake and organization, two essential qualities to establish a foundation of effective analytics in supply chain management.
We'll explain each in a little more detail below.
It's one thing to have access to data. But for a business to truly gain the value of big data, an analysis must become routine, and insights must be assimilated into relevant company departments.
For that to happen requires the same steps as most other business processes. It necessitates a team of data scientists, to start. An organization must hire a group of analysts or train existing team members to oversee or perform data analysis.
To harness the power of big data analytics in transportation, companies also must integrate it with current systems. In other words, the data must be easy and fast to access for any team member who requires it.
Finally, it is essential to routinely share the insights gleaned from big data with appropriate stakeholders to ensure that the information has the most impact and makes a difference.
In short, do not restrict big data analysis to siloes. Logistics analytics is an inherently scientific exercise, requiring a certain degree of training and expertise. However, relegating these activities to specialists without creating the workflows, touchpoints, and maximal sharing will not produce the results that a company needs to improve workflows and solve business challenges. Executives who wish to glean as much value as possible from big data need to ensure that one final step happens — making insights accessible and actionable.
There are seemingly endless opportunities to optimize operations in logistics. Companies that avail themselves of data analytics for logistics insights have an opportunity to cut costs, maximize efficiency, and, most of all, improve the customer's overall experience. Here are a few ways shippers can use big data in supply chain and logistics.
Big data enables shippers to determine what and how much inventory they need to avoid stockouts or over-ordering. Managers can use advanced analytics to scrutinize consumer trends and process large amounts of past sales data to forecast demand for a product and then plan inventory accordingly. This prevents a company from either wasting money and warehouse space on merchandise that isn't needed or falling short and losing sales.
As e-commerce crosses the $1 trillion mark, more and more companies are adopting an omnichannel strategy. Businesses that sell through multiple channels need the data from these different sources to flow into one central location. Big data gives sellers a real-time window into the volume of items sold, stored, or returned. Business owners aren't stuck updating inventory levels across different channels or, worse — dealing with the fallout when information isn't up to date.
At first glance, human behavioral patterns might appear too vast and variable to have a helpful business context. But as it turns out, human behavior is largely predictable. Behavioral analysis has moved beyond the realm of magazine quizzes and into biometrics, cybersecurity, and even human resources. In supply chain management, large data sets can help companies evaluate employee and vendor performance, root out inefficiencies, and determine where assistance is needed. Big data can also empower businesses to offer customers personalized marketing or dynamic pricing.
Visibility in the supply chain has been a hot topic for years, but its importance really can't be overstated. The combination of real-time monitoring and analytics to make sense of the information flow has enabled companies to plan better transportation schedules and work around bottlenecks. As an added bonus, companies can update their customers and vendors on delivery times or delays.
Lenders historically have relied primarily on buyers' and suppliers' credit ratings to make lending decisions. The downside to this approach is that a credit score depends on many factors, not just reliability in delivering goods or paying promptly. When assessing risk, big data in logistics allows finance providers to consider additional pertinent factors, like on-time payments, late shipments, order performance and canceled orders.
The core of freight audit and payment is ensuring that bills are paid on time and correctly. But by incorporating big data, shippers have a holistic view of their operations that can help them make better-informed decisions across transportation modes. Shippers can see if they use the wrong carrier, transportation mode, or service level.
When there are hundreds of millions of data entries in question, it's unlikely that any one transaction would get caught by the human eye. Unfortunately, one small error is all it takes to spend thousands on erroneous charges. Machine learning works by ingesting large quantities of data and then identifying patterns — and outliers. When applied to freight bills, these algorithms can pick up on anomalous spending spikes and invoice errors that the human eye might not have caught.
For example, Once you clearly understand that something has changed, you can begin to assess whether it truly is a problem and determine its impact on your business. The visibility Intelligent Audit provides makes this possible.
The problem could be as simple as a misplaced comma in data entry, transforming a 10-pound shipment into 1,000 pounds.
Most shippers have metrics to monitor the standard Key Performance Indicators (KPIs) - spend, volumes, cost/shipment, cost/kg, cost/mile, etc. For a shipper spending millions on transportation annually, it is unlikely that an anomalous pattern will occur that changes any of those KPIs at a macro level. When they figure it out, it's after they have spent irrevocable money that could have been an easy fix had they been made aware of it quickly.
This is where true unsupervised machine learning is impactful. Artificial intelligence can detect the slightest new pattern in your transportation—data across any KPI—and makes you aware quickly before spending millions of dollars on a technical glitch or human error.
Most logistics data is notoriously unwieldy. It's spread out, comes from disparate sources, and often uses varying units of measure. And that doesn't even cover mistakes and typos. Shipping data can be difficult to wrangle, but there's no point in collecting this information unless it can be mined for insights. The beauty of big data analytics and machine learning techniques is that they turn unmanageable data sets into clean, normalized performance assessments and predictions.
It's hard to imagine a company consciously choosing to spend more on a service that's central to its business. Yet, shippers who don't use data to optimize their logistics network are doing precisely that. Network optimization helps shippers ensure they work with the right carriers and meet relevant KPIs. Of course, to truly optimize a carrier network, shippers first need a flow of data to evaluate their shipping performance.
Big data entails large volumes, large varieties, and a large velocity of continuous information. It doesn't take much imagination to think of all the ways to provide valuable business insights — or how an organization could become inundated by the information flowing in.
The right freight audit provider will leverage, cleanse, normalize, and yield continuous data sets into meaningful business analytics and actionable strategies. Intelligent Audit provides data-agnostic, industry-agnostic solutions to help shippers make the best decisions for their business. To find out how to fast-track continuous improvement, contact Intelligent Audit today.
Set up a call with one of our experts to discuss how Intelligent Audit can help your business uncover opportunities for cost reduction and supply chain improvements through automated freight audit and recovery, business intelligence and analytics, contract optimization, and more.
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