Manual data entry and converting documents absorb valuable time and resources. While many prefer to do things the old fashion way, modern supply chain management teams have found that machine learning tools are the perfect upgrade to their toolbox. By incorporating AI and machine learning into daily operations, shippers can sync their data easily for heightened accuracy across various data streams.
Because operation accuracy and insight increase with machine learning tools, shippers can track the efficiency of every penny spent and profit margin based on every penny gained. As time becomes increasingly valuable in an overworked industry, a May 2022 report featured on PR Newswire said, "the global logistics automation industry was pegged at $49.70 billion in 2020, and is expected to reach $147.38 billion by 2030, growing at a CAGR of 11.9% from 2021 to 2030." While artificial intelligence progress will likely impact this value growth, much will occur from the widespread adoption of machine learning tools in shipping.
Machine learning builds off core artificial intelligence technology to develop or learn based on data. Explainable machine learning presents solutions to people, and some tools often miss the decision-making process. For example, supply chain logistics uses explainable machine learning daily through popular apps such as route planning software.
Deep learning models take machine learning tools further with algorithms inspired by the human brain that make artificial neural networks based on consistently fed data. Deep learning technologies automate the collection and connection of baseline data sets for shippers to gain a conclusive visual of the inner workings of their operation. When current activity runs across baseline data, deep learning models can distinguish anomalies and be programmed to notify the human employees who can amend the irregularity.
For example, this technology can optimize transportation spending by tracking irregularities in accessorial fees, unapplied dedicated carrier discounts, and more. In addition, deep learning analytical tools are handy as rampant disruptions plague the industry, leaving human employees disoriented with changing trends and regulations. Yet, with competent explainable deep learning, supply chain management teams can focus on an improved customer experience at a fraction of the cost, even in a disorienting season.
While some people may have a "one and done" or "buy once, cry once" mentality towards technology upgrades, the truth is that technology is becoming obsolete at an alarming rate. Without consistent, up-to-date methods, shippers can quickly lose access to reliable services that once revolutionized their process. Losing technological relevance is especially detrimental to businesses in a struggling economy. Statista, "Supply chain disruptions are an economic hardship, costing organizations around the world an average of 184 million U.S. dollars per year according to a 2021 survey."
Labor shortages, bottlenecked ports, and bull-whipped inventory levels have played significant roles in supply chain disruptions, yet old technology is rising as its leading cause for operational disruptions. For example, when a shipper's track and trace software is suddenly unavailable, an entire day could be lost answering the berating waves of customer phone calls. In addition, wrong track and trace software complicate coordination with carriers and 3PLs, increasing dwell times, dead hauls, delays, missed deliveries, and even damaged product.
With increased service complications, shippers gain an unfortunate boost to their OSD report findings and added freight claim management responsibilities. By avoiding artificial intelligence progress, transportation service providers lose more than they gain using outdated technology that requires constant supervision. Yet, shippers don't have to settle for poor network and data visibility software.
Although not all machine learning tools are equal, the right artificial intelligence solution can drastically improve business performance without requiring additional labor or time. Supply chain management teams must consider the following nine points to get the most out of automation.
The first step to incorporating any artificial intelligence progress within the supply chain is to collect all data in real-time. It is only through the consolidation of all data in real-time that shippers can get a complete understanding of the inner workings of their operation to reveal areas needing improvement.
Once data streams get thoroughly combined through a single source of truth, machine learning tools can translate raw data into strategy. With unbiased scrutiny, a business intelligence tool can provide actionable analytics on demand or at regular intervals to inform decision-makers effectively.
Key performance indicators (KPIs) are an essential tool within business logistics. By leveraging the artificial intelligence progress of the last few years, shippers can utilize machine learning tools to both records and monitor logistic KPIs that provide valuable insights into current operational functions.
To maximize logistics cost-saving opportunities, shipping companies must be capable of tracking their expenses to evaluate their profit margin. An intelligent supply chain logistics tool can quickly and efficiently produce budget vs. actual statements and optimize payment cycles to improve cash flow.
The fast-paced nature of the supply chain makes consistent training a difficult task. Machine learning tools can help team leads with more efficient, automated management of their responsibilities during training and as a tool for training sessions.
Change can be difficult for many, but delaying upgrades amidst artificial intelligence progress is detrimental to business. Instead, shippers can optimize their operations by following the industry-wide embracement of tech evolution within the hardware and software sectors.
Social norms have changed since the pandemic's reign, including within business networking. As a result, shippers can utilize machine learning tools to automate communications to industry partners for better collaboration opportunities in and out of niche sectors.
Unless a shipper works with a dedicated carrier service, freight accounting will always look very different from load to load as various carriers fulfill capacity needs. By automating accounts payable and accounts receivable, shipping companies can benefit from a streamlined, simplified financial experience they can pass onto their network.
Shipping companies are better set up for carrier contract optimization when incorporating an intelligent supply chain logistics program to start a holistic view of company operations. Machine learning tools set shippers up for carrier success, whether learning to leverage different trailer types for the various retail supply chain needs or evaluating the effectiveness of carrier partners.
Across the many uses for machine learning tools within the supply chain, business intelligence tools shine the most as anomaly detection. Though the sources for anomalies could stem from human error, miscoded processes, glitches, and more, it is vital that shippers immediately know how, where, and why it occurred. This knowledge empowers shippers with the following action steps to correct present mishaps and prevent future ones.
Given the amount of artificial intelligence progress, surprising mistakes seem to be the most reliable factor within the market. However, according to Zippia, U.S. retail operations have a supply chain accuracy of 63%. This can result in significant delays and re-stocking issues. For example, 34% of businesses have shipped an order late due to selling a product that wasn't in stock.
Shipping companies gain valuable insights when they have a streamlined data system that an explainable machine learning program can quickly and efficiently scan. Instead of settling for double-digit freight failure, shippers can use actionable insights to protect their operations from mishaps, such as automatically flag systems from selling products that are out of stock.
Shipping company decision-makers have to make countless daily decisions that could change the trajectory of their business. Explainable machine learning tools offer shipping executives confidence in decision-making with calculated direction and the reasoning behind the guidance. However, not every ML business intelligence tool is the same. Intelligent Audit stands out among business intelligence companies due to its passion for self-growth, passed on to its customers by the excellent products they provide as analytical resources. Start leveraging your data with Intelligent Audit's machine learning tools today to take your company's operations to the next level.
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