B2B credit management has evolved since 2019. Here’s how to ensure your credit department succeeds. 

The COVID-19 pandemic drastically changed the world, businesses, and their credit departments. It reshaped our economy. In order to meet the changing business landscape, credit managers have adapted quickly to maintain their companies’ financial stability. 

Let’s briefly review the economy before the pandemic started. This will give us a clearer picture of the changes that have happened and the difficulties B2B credit managers now face. We’ll look at how your sales team can become a credit ally and close with tips on how to decision today’s B2B credit with success.

Pre-Pandemic Stability

Before COVID-19, the economy experienced comparatively stable growth. Companies were generally optimistic about their clients’ creditworthiness. The approval process for B2B credit managers was a relatively simple routine. They usually assessed customer creditworthiness based on financial statements, credit reporting, and industry benchmarks. Once a credit limit was approved, customers were generally given net payment terms.

Pandemic-Induced Shifts

The pandemic triggered a series of economic shifts that profoundly affected B2B credit practices. Government stimulus programs, supply chain disruptions, and inflation surges all contributed to a climate of uncertainty and volatility. 

According to the National Association of Credit Management (NACM), total bankruptcy filings increased 18 percent year-over-year (YoY) in 2023. 

As a result of these changes, businesses became more cautious about extending credit and credit managers had to adopt a more rigorous approach to risk assessment. 

6 Key Changes in B2B Credit Management

In-Depth Credit Risk Assessments

Economic changes caused credit managers to become more reliant on data analysis to assess creditworthiness. This includes using financial modeling tools to assess a company’s ability to meet its debt obligations. Credit bureaus and alternative data sources are also leveraged to achieve a comprehensive view a customer’s financial health.

Tighter Credit Terms

As businesses become more risk-averse, they are tightening their credit terms. This can involve shortening payment terms (e.g., from net 60 to net 30), reducing credit limits for existing customers, and issuing lower initial credit lines for new customers. According to a March 2024 report by HighRadius, 52 percent of companies seek extended terms – quite the opposite view. The same report shows that 17 percent of customers blatantly ignore credit terms while another 48 percent intentionally delay payment. This can make building strong customer relationships difficult.

Increased Use of Credit insurance

The rise in economic uncertainty has led to a surge in demand for credit insurance. Credit insurance protects businesses from monetary loss if a customer defaults on their payments. A 2023 survey by AU Group shows that since the third quarter of 2022, the number of business failures in almost every region of the world has risen. In line with that statistic, credit insurers expect growth in their sales over the next six years.

Growing Use of Digital Credit Tools

The pandemic has accelerated the adoption of digital credit tools and automation. Tasks like processing credit applications, credit checks, and collections are now being completed faster and allowing credit teams to focus on exception management.

Collection Challenges

The pandemic caused many businesses to experience cash flow disruptions. It’s made it more difficult for some companies to meet and/or maintain on time payments.

Cash Flow Management

Businesses are focusing on more effective ways to manage their working capital. This can include reworking their collection processes and closely tracking inventory levels.

Opportunity Emerges

All these changes have significantly affected credit managers and their teams. Now, they carry heavier workloads and face increased pressure to mitigate credit related risks. They also need to be able to adapt to rapid changes that may happen in today’s economy. 

While these changes may have increased the burden on credit managers, they’ve also created opportunities for collaboration with sales teams. By working together, credit managers and sales teams can better service their businesses and customers.

5 Ways B2B Credit Managers Can Seek Help from Sales

In today’s risky and fraud-ridden environment, the sales team support in customer onboarding and credit is vital. Credit and sales teams must collaborate to ensure a positive and seamless customer experience. Here are some tips to foster better collaboration:

Educate for an Improved Understanding

Sales teams are crucial in helping gather customer information to assess creditworthiness. Credit managers can help sales teams understand the importance of collecting this information. Sharing its use and how having it can make the approval process faster helps, too. 

Develop a Standardized Form  

A standardized customer information form ensures sales teams collect all the required information. This can help streamline the credit approval process.

Encourage Proactive Customer Updates  

Credit teams must stay updated on customer developments. Encourage the sales team to proactively share any relevant customer updates with the credit department. Discuss what information is “relevant”, so everyone is on the same page.

Have a Joint Review Process  

Joint sales and credit reviews can ensure both teams understand customer creditworthiness. They can help prevent incidents where a customer is given an okay by sales and later is deemed to be a credit risk. At the same time, joint reviews will strengthen the relationship between sales and credit while improving the customer experience.

Foster Open Communication and Trust 

Open communication and trust are essential for effective collaboration between teams. Credit managers should be available to answer sales teams’ questions and provide guidance on any credit-related matters.

Is This the New Normal for B2B Credit Management?

It appears this “new normal” of post-pandemic business is here to stay, and it’s changed credit management for the foreseeable future. Because of this, we must have a more strategic and data-driven approach to B2B credit management. Those credit teams that adapt to these changes and improve collaboration with sales will be well-positioned to thrive in today’s economy. Furthermore, those who stay flexible and committed to delivering exceptional service will aid their company’s success. Will your credit team be the ones to hold revenue back or help drive it forward?

About the Author 

Tracy Mitchell currently holds the position of Director of Accounts Receivable at Trinity Logistics. She has worked at Trinity for nine years, with over five years of those in credit management. She holds a Credit Business Association (CBA) designation. With a deep understanding of the industry’s dynamics, she has firsthand knowledge and provides the company with invaluable insights into the complexities of credit risk assessment, collections, and sales alignment. 

In a world so reliant on digital technology, we often expect (and hope) that our software will be stable. Yet even the most reliable technology platforms can falter. Take the recent digital disruption felt by businesses affected by the CrowdStrike global outage for example.

The recent global outage involving CrowdStrike, one of the world’s leading cybersecurity companies, was a stark reminder that no system is entirely immune to disruption. It’s a harsh reality, but one we need to face head-on. Here’s how the recent outage affected businesses and, most importantly, some essential tips to ensure your operations remain resilient. Read on to safeguard your company’s digital future.

Crowdstrike Global Outage Event

On July 19, 2024, CrowdStrike released an update for its Falcon Sensor software. The update caused a significant global IT outage, crashing millions of Windows computers and displaying what you might otherwise know as the “Blue Screen of Death.”

Around 8.5 million systems worldwide were affected. The outage interrupted businesses of all kinds, including airlines, healthcare, banks, and more. Here are a few examples of the disruption the outage caused.

Airlines

At LaGuardia Airport, the outage caused their baggage handling system to fail, causing significant delays and widespread operational disarray. Wait times were extensive, and many passengers missed their flights.

Delta Airlines was the largest airline affected by the outage. The company had to reset over 40,000 servers and manually cancel 5,000 flights, losing over 500 million dollars in revenue.

Healthcare

Hospitals like the Mayo Clinic, Cleveland Clinic, and Mass General Brigham faced system crashes that affected patient care and administrative functions. Electronic health records went offline, delaying medical procedures and patient admissions. The estimated financial impact on the healthcare sector alone was around $1.94 billion.

Banking

Financial institutions like JPMorgan Chase and Bank of America suffered considerable downtime. Transactions, online banking services, and customer support were affected. The inability to process anything led to customer dissatisfaction and financial losses. The estimated impact on the banking sector contributed heavily to the global economic damage totaling at least $10 billion.

Preventing Digital Disruption in Your Business

The CrowdStrike global outage underscored the importance of being prepared for the unexpected. While such events may be rare, businesses should understand that no service is exempt from disruption. But don’t panic. You can use the practices below to reduce any impact should an incident like the CrowdStrike outage ever happen. 

Have a Strong Incident Response Plan

A well-structured Incident Response Plan (IRP) is crucial for navigating outages. For many businesses, the CrowdStrike outage was a wake-up call about the importance of having a detailed IRP. Most organizations now have plans for cyber threats but remain unprepared for a service outage.

Organizations need well-defined and practiced IRPs. An effective IRP ensures faster recovery and coordinated actions during outages.

A proper incident response plan should have the following components:

• The organization’s incident response strategy and how it supports business objectives
• Roles and responsibilities involved in incident response
• Procedures for each phase of the incident response process
• Communication procedures within the incident response team, with the rest of the organization, and external stakeholders
• How to learn from previous incidents to improve the organization’s security posture

Without a solid IRP, chaos can arise when essential tools and services go down. Time is so critical in these kinds of situations. Companies that don’t respond to incidents fast often face increased downtime and direct revenue loss.

According to a SANS report, companies without a proper IRP take 54 percent longer to contain incidents that cause downtime. Additionally, a study from Ponemom Institute found that organizations without an effective IRP team experienced 54 percent more downtime compared to those with one.

Having an IRP in place is crucial, but the second most important aspect is testing it often! This ensures that the plan is effective, team members are familiar with their roles, and potential gaps are identified before an actual incident occurs.

Practicing the IRP should be done annually or after a major change to your process. Planning and organization are the only ways to mitigate significant disruption. It’s always best to be prepared for the worst!

Review Your Software Deployment Practices

Deploying new software can be a very complex process, especially when it is dependent on other applications or systems. The CrowdStrike global outage was caused directly by this issue, as it was dependent on the Windows operating system.

Here are some best practices for establishing an effective deployment process.

Develop a Patch Management Policy

Define a comprehensive policy that details the procedure for managing patches, specifying roles, responsibilities, and schedules.

Inventory Assets

Keep an updated inventory of all hardware and software assets that need patching.

Prioritize Patches

Assess and schedule patches based on the severity of vulnerabilities and the importance of the systems they impact.

Before deploying patches to production environments, test them in a controlled setting. This way, you can be sure they won’t cause any issues.

Automate Patch Deployment

Automated tools are excellent for streamlining the patch deployment process. This can reduce the risk of human error and ensure timely updates.

Monitor and Audit

Continuously track the patching process and audit patch deployments to ensure compliance and effectiveness.

Have a Rollback Plan

Have a rollback plan in place. This allows you to revert to a previous state should a patch cause problems.

Keep a Vendor Patch Schedules

Stay informed about each vendor’s patch release schedule to plan and prepare for upcoming updates.

Document Everything

Keep detailed records of all patching activities, including what was patched, when, and by whom.

Assess Your Vendor Relationships

Periodic assessment of third-party vendors is necessary to ensure resilience.  The CrowdStrike outage has prompted many organizations to reconsider their vendors. Businesses should assess vendor relationships to confirm they meet the organization’s risk tolerance and operational needs.

Scheduling annual assessments can help keep this task from being forgotten. Assessments should include one for vendor risk and a security questionnaire.

Vendor Risk Assessment

This assessment evaluates the potential risks that the vendor may introduce to your organization. This includes understanding the vendor’s operations, data handling practices, and risk profile.

Security Questionnaire

This should be comprehensive and help you understand the vendor’s security policies, practices, and standards. Topics may include encryption, incident response, access controls, and employee security training.

Consider Diversification

Companies may wish to review their diversification processes when relying on critical software to run their operations. As highlighted by the CrowdStrike outage, over-dependence on a single software solution can expose the business to significant risks. This can include operational disruptions due to software failures, security vulnerabilities, or vendor instability.

Software diversification helps you not be reliant on one system. This can provide contingency options and flexibility in the face of unexpected challenges. By incorporating several complementary software tools or services, companies can enhance resilience, maintain business continuity, and mitigate potential risks.

Building Digital Resilience in the Face of Uncertainty

While incidents like the CrowdStrike outage can be rare, their impact can be severe. The unpredictability of such events can be scary, but with these proactive practices, there’s little to fear. Remember, a resilient business is a prepared business. By taking these steps, you can protect your operations and buckle in for a smooth ride in the digital landscape.

ABOUT THE AUTHORS

Willy Rojas

Engineer II, Infrastructure

Willy has been with Trinity Logistics for eight years. He’s held several other IT positions while here, starting as a Service Desk Intern to Senior Service Desk, IT Systems Administrator II, and Infrastructure Engineer II. Willy finds cybersecurity fascinating because it’s often changing and giving him something new to learn. He also finds satisfaction in knowing that the work he does every day is important, from keeping confidential information secure to keeping business operations running smoothly.  

Dustin O’Bier

Manager, Infrastructure

Dustin has been working at Trinity for 21 years. Previous positions he’s held include Help Desk Specialist, System Administrator I, Senior System Administrator, and IT Systems Manager. Dustin enjoys the collaborative aspect of his role. He loves working alongside a team of people to solve complex problems. Dustin’s main focuses as Manager in Infrastructure cover three distinct aspects; the Core Infrastructure of the Regional Service Centers (RSCs), Security, and the company’s Cloud practice. He finds each focus brings a unique set of challenges, making his role dynamic and engaging.

If you’ve worked in the LTL industry for any bit of time, then you know that it’s always changing. Yes, sometimes that means it gets a bit more complicated. Rates adjust. Rules and processes are modified. Despite all this, there is usually one constant – the core LTL carriers we work with. Yet, in 2023, that changed; we saw the departure of the legacy LTL carrier known as Yellow Corporation. 

The closing of such a large and well-established LTL carrier is very rare. The industry hadn’t felt the void of such a large company since Consolidated Freightways closed 20 years prior. So, what happened? Considering Yellow Corporation was the third largest LTL carrier, what happened to all the freight they handled? 

As someone with a career in LTL, I saw this happen in real-time and have directly seen its ripple effects. I can answer some of those questions and share with you my thoughts, experiences, and observations of this impactful event in LTL history. 

The Fall of Yellow Corporation

Yellow Corporation (commonly referred to as YRC) was no stranger to financial turmoil. The company was laden with debt that was worsened with the Great Recession. It almost put them into filing for bankruptcy in 2009. 

A stint of other factors after that didn’t put them in a better position when COVID-19 rolled around in 2020. YRC was granted a $700 million COVID-relief loan by the U.S. government, which it used nearly half of to cover past due payments to healthcare and pensions, payments on equipment and properties, and interest accrued by its other debts. Fast forward to 2023, and that’s where their final chapter began. 

A few months into 2023, YRC and the Teamsters Union engaged in back-and-forth negotiations. YRC wanted to change operational procedures and sought extra funding to help it pay off its debts. Teamsters disagreed with the proposed changes. We saw news articles and hit pieces about the conflict, week after week. It was nearly impossible for the industry to ignore it. 

In July, whispers began of a possible union strike that would effectively halt YRC’s freight network. This was the writing on the wall for many shippers and third-party logistics (3PL) companies. At this point, the hull had been punctured, and water pouring in. Do you stay or do you go? 

YRC and its subsidiaries were promptly disabled from countless TMS platforms. No customer wanted their freight stuck in limbo if Teamsters were to go on strike against YRC. Because of this, YRC saw a sharp decline in freight volume and tonnage. A company that was in financial disarray was now losing its primary source of revenue. 

On July 30^th, Yellow Corporation ceased all operations. The Teamsters had not agreed to the negotiations, and the 11^th hour came and went. So, what now? 

The Aftermath of YRC’s Closing

YRC’s exit affected two parties: shippers using LTL and other LTL carriers. 

For shippers using LTL, they were two buckets: those who had already begun shifting their freight to other carriers in their pricing roster and those unfortunate enough to still have most or all freight with YRC. The latter had a more difficult situation to overcome as they now had to find an LTL carrier to move their freight without paying an arm and a leg. 

For LTL carriers, YRC’s existing freight had to go somewhere, so they had to figure out how to absorb it. Carriers such as Estes, FedEx, and XPO and their capabilities were pushed to their limit, now drinking from a firehose of incoming freight. Volumes increased drastically, and with such a rapid rise came decreased capacity. 

LTL carriers were making the difficult decision to exclude certain shippers in favor of others just to service accounts and keep their networks moving without bottlenecking. This left many smaller shippers stranded with a shorter list of available LTL carriers. 

As carriers became inundated with freight, their operating ratios took a hit, and something had to be done to regain control. A season of atypical general rate increases (GRI) began. LTL carriers needed to remain profitable lest they succumb to a fate like Yellow. 

3PLs and shippers alike started getting notifications from their carrier representatives about rates going up. Shipping LTL got more expensive now that the carriers had to pick and choose who they serviced with their finite capacity. The increased rate structures also priced out shippers that were used to YRC’s competitively priced tariffs or couldn’t stomach the increases.

For many shippers and 3PLs, the immediate aftermath of the Yellow Corporation bankruptcy was unlike any they had previously experienced. 

Now, that’s the long and short of it, but how are things today? Surely, the disappearance of a significant LTL carrier like that would have lasting, irreversible affects. 

Well, yes, but also no. 

The Current Impact of YRC’s Closing

Today the LTL industry has mostly stabilized. YRC’s freight volume has dispersed, and the dust has settled. The LTL carriers have course-corrected their capacity concerns. 

After the YRC bankruptcy, there were also new questions to answer, one of which was “What happens to their assets?” Those went through the bankruptcy courts, but the LTL carriers were eager to get a piece of it. 

The purchased terminals and trailers meant increased footprint and capacity, which can be the difference between being the best and the biggest for LTL carriers. Several carriers bid to acquire the terminals left behind by Yellow Corporation.

Estes Express, a prominent national LTL carrier, was one of the larger victors in the bidding war. As one of Trinity’s carrier relationships, I asked Estes if they could share the impact YRC’s exit had on their company. Here’s what President and COO Webb Estes had to say: 

While LTL carriers, larger shippers, and 3PLs came out in the black or relatively unscathed, others did not. Smaller shippers with all their freight lanes with YRC had no backup plans except to pay increased, non-discounted LTL rates with other carriers or risk their business operations. 

How Did Trinity Logistics Fare?

At Trinity, those first few months after the bankruptcy were interesting! We saw many new shippers start a relationship with us and saw some complications in LTL carrier transit lanes that bottlenecked. Don’t worry, they were quickly resolved. Since Trinity has a broad roster of national and regional LTL carrier contracts in place, our shipper relationships were able to use our rates to course correct from the YRC closure and effectively avoid any critical disruption. 

Is the last time we’ll see an industry-shaking event in the LTL space? Likely not. For now, the industry is stable, and many LTL carriers are growing and reporting profitable earnings. 

In my 10+ years working in the LTL industry at a 3PL, the Yellow Corporation was always a top LTL carrier for us. Seeing them fade into the wind after decades of LTL service was surreal, and I felt sad for the many YRC employees I’ve grown to know. 

Despite such an impactful event, now written in the history books, it’s a year later, and the LTL landscape is still thriving (and volatile), even with one less player at the table. 

Final Thoughts

Considering the size of Yellow and the steady decline until evaporation from the industry, I actually expected more disarray from it. Sure, the first weeks after the bankruptcy had the GRIs, shipment delays, and new shipper partnerships for Trinity to handle, but after a month or two, it was relatively smooth sailing back to normal. 

I think that speaks volumes to the age we live in. The amount of technology and time-saving efficiencies that LTL carriers invest in year after year. It allowed the industry to absorb the freight volume of one of the largest LTL carriers in the world and it did so in less than 60 days! It’s kind of crazy and a testament to the LTL industry and its controlled chaos. 

Working with Yellow for so many years, I grew familiar with some of the names worked there. People we would see at conferences, have calls with or see on emails. People who had been in the industry much longer than I have, had extensive backgrounds, and grew their roots at Yellow. 

The bankruptcy landed them in the middle of it all, but many of them went on to other LTL carriers and took their experience, adding value there. I think that’s a silver lining here. Despite the financial decision of Yellow as a company, it had people on its roster that brought purpose to LTL and now these people are creating an impact for other carriers and customers alike. For how vast it is, the LTL industry can be closeknit, so to see those former Yellow employees succeed at other LTL carriers is a bright spot in this saga. 

ABOUT THE AUTHOR

Curt Kouts holds the Director of LTL position at Trinity Logistics. Kouts has been with Trinity and in the logistics industry for 14 years, having held several titles among carrier vetting, account management, and within the LTL Team itself. His main responsibilities as Director focus on elevating Trinity’s LTL customers’ experience, helping the LTL Team support in operations and billing, and aiding the company in overall LTL sales and success. Kouts finds the LTL industry incredibly challenging, presenting him and his Team a ton of problems that they have a passion for solving. He enjoys learning more about LTL whenever possible and overall, making LTL an experience that keeps all his customers, both internal and external, coming back.

Artificial Intelligence (AI). You see it mentioned all over the news headlines. You overhear your coworkers discussing it in the breakroom. Even your family members bring it up at get-togethers.

Much like when the internet first came to be, people are both amazed and uncertain about it. I often hear and see the same questions come up. What is the history of AI? When did it start? What exactly is AI? Is it just ChatGPT? What kinds of AI are there? Will AI take my job from me? Will AI take over the human race? (Definitely no to the last one!)

As someone who works in the technology field, I’d love to answer those exact questions for you and share some of my own thoughts on AI. 

What is Artificial Intelligence?

Let’s answer this question first. Artificial intelligence is the science of making machines think, process, and create like humans. It has become a term applied to applications that can perform tasks a human could do, like analyzing data or replying to customers online. 

The History of Artificial Intelligence 

You might be surprised to learn that AI has existed for a while. 

1950s

The Start of AI 

The first application of artificial intelligence was the Turing Test. In 1950, Alan Turing tested a machine’s ability to exhibit behavior equal to a human. The test was widely influential and believed to be the start of AI.  

In 1956, “Artificial Intelligence” was officially coined by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon at the Dartmouth Conference. The conference is seen as the founding event of AI. 

1960s

Early Research and Optimism 

Early AI programs began to develop during this time. Computer scientists and researchers eagerly explored methods to create intelligent machines and programs.

Joseph Weizenbaum created ELIZA, a natural language processing program to explore communication between people and machines. Later, Terry Winograd created  SHRDLU, a program that understood language in a restricted environment. 

1970s

The AI Winter 

Early enthusiasm from the 1950s and 1960s fell due to limited computational power and unrealistic expectations. There was a significant decline in interest and funding for AI, so projects fell by the wayside. You’ll often see this time in history called the “AI Winter.”

1980s

Expert Systems Bring Renewed Interest 

Despite the slowdown, some projects continued, albeit with slow progress. Expert systems, designed to mimic human decision-making abilities, developed and were a turning point in AI. These systems proved that AI could be used and beneficial in businesses and industries. Many commercial fields, such as medicine and finance, began using expert systems. 

1990s

Machine Learning and Real-World Applications 

Here’s where AI started gaining momentum. During this time, we shifted from rule-based systems to Machine Learning. Machine Learning is just that – a machine or program that can learn from data. We see a lot of Machine Learning in today’s applications, like self-driving cars or facial recognition.

Machine Learning developed so well that in 1997, IBM’s Deep Blue became the first computer system to defeat world chess champion Gary Kasparov. This moment showcased AI’s potential for complex problem-solving and ability to think like a person. 

2000s

Big Data Offers AI Advancements

Up until now, AI was limited by the amount and quality of data it could train and test with Machine Learning. In the 2000s, big data came into play, giving AI access to massive amounts of data from various sources. Machine Learning had more information to train on, increasing its capability to learn complex patterns and make accurate predictions. 

Additionally, as advances made in data storage and processing technologies led to the development of more sophisticated Machine Learning algorithms, like Deep Learning. 

2010s

Breakthroughs With Deep Learning 

Deep Learning was a breakthrough in the current modernization of AI. It enabled machines to learn from large datasets and make predictions or decisions based on that. It’s made significant breakthroughs in various fields and can perform such tasks like classifying images.

In 2012,  AlexNet won, no dominated, the ImageNet Large Scale Visual Recognition Challenge. This significant event was the first widely recognized successful application of Deep Learning. 

In 2016 Google’s AlphaGo AI played a game of Go against world champion Lee Se-dol and won. Shocked, Se-dol said AlphaGo played a “nearly perfect game.” Creator DeepMind said the machine studied older games and spent a lot of time playing the game, over and over, each time learning from its mistakes and getting better. A notable moment in history, demonstrating the power of reinforcement learning with AI. 

2020s

Generative AI

Today’s largest and known impact is Generative AI, able to create new things based on previous data. There’s been a widespread adoption of Generative AI, including in writing, music, photography, even video. We’re also beginning to see AI across industries, from healthcare and finance to autonomous vehicles.

Common Forms of AI

Computer Vision

Computer Vision is a field of AI that enables machines to interpret and make decisions based on visual data. It involves acquiring, processing, analyzing, and understanding images and data to produce numerical or symbolic information. Common applications include facial recognition, object detection, medical image analysis, and autonomous vehicles. 

Honestly, Computer Vision is my favorite field of AI. I’ve had the opportunity to work on some extremely interesting use cases of Computer Vision. One was using augmented reality lenses (like virtual reality goggles) to train combat medics using Computer Vision. The Computer Vision with augmented reality added a level of realism to the virtual training, which used to be unattainable. While I would love to go into further detail about what the AI looked like, I signed a non-disclosure agreement. You’ll just have to take my word that it was really cool! 

Machine Learning (ML) 

Machine Learning is a subset of AI that allows computers to learn from and make predictions or decisions based on data. It encompasses a variety of techniques such as supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Common applications range from recommendation systems (like Netflix), fraud detection, predictive analytics, and personalization. 

Deep Learning 

Deep Learning is a subset of machine learning. It involves neural networks with many layers (deep neural networks) that can learn from large amounts of data. It enables machines to learn features and representations from raw data automatically. Key components include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).

Natural Language Processing (NLP) 

Natural Language Processing (NLP) is a branch of AI that focuses on the interaction between computers and humans through natural language. It enables machines to understand, interpret, generate, and respond to human language. Common applications include machine translation, sentiment analysis, chatbots, and speech recognition.

Generative AI / ChatGPT 

Generative AI refers to AI models that can generate new data like the data its trained on. This includes text, images, music, and more. ChatGPT, a specific (and well known) application of Generative AI, is a language model developed by OpenAI that can generate human-like text based on the input it receives. It uses Deep Learning techniques to produce coherent and contextually relevant responses, making it useful for applications like conversational agents, content creation, and more.

AI is Awesome BUT It Has Some Setbacks 

Don’t Worry, AI is Not Going to Take Away Your Job

Artificial intelligence has some great benefits, like processing and analyzing data in minutes, but it’s not perfect. It’s still not HUMAN, it’s not you, and that is exactly why it won’t replace you in your job. 

Take a lawyer for example. AI has been known to complete the bar exam in the 90^thpercentile. Awesome, right? But that doesn’t mean AI is going to perform better than an actual lawyer in a case. It just means the AI is better at answering the test because of its training with the text.

AI is very good and fast at anything with text, but it’s terrible at motor functions and mimicking a person in a non-scripted environment. Like with Generative AI – at some point I know you’ve seen or heard something created by it and have had the thought, “This is definitely AI.”

Ethical Components

As AI continues to be adopted and widely used, I believe it needs legislation around it. For example, do people need to state they are using AI for their work? Can they still claim it’s something created by them if AI created a part or even all of it? Who gets the credit – the person or the machine?

There’s also the concern with creators being miscredited or violations with copyright. There’s been plenty of cases or news headlines in which AI has learned from artists and essentially recycled their work in a slightly new form. Is that a form of stealing? 

AI Can Be Volatile

The development of AI is happening fast. Tomorrow, your current AI platform could be outdated. Things constantly change week to week. The features you might love now could be cut and replaced with something new. It can be hard for people and their own Technology Teams to keep up! And did I mention its hallucinations? Sometimes it likes to make up its own false information, so you should always doublecheck its results!

My Closing Thoughts

AI is real, it’s been here, and is both impressive and scary. It’s also very trendy to mention in any news article or headline, which is why you’re seeing it mentioned a lot. Nor, again, is it coming for your job anytime soon. While some articles may have you believe it will provide us world peace by the end of the year, it is still limited in capability. 

This is not to understate the need for legislation so that it is responsibly used, but rather, a presentation of the facts of AI’s impressive feats, and numbered flaws. It’s important to remember that while things like ChatGPT and Generative AI are newer, we have a long history of AI development, going back nearly as far as computers, and likely, further back than many of our lifetimes. 

About the Author

Michael Adams is a Data Scientist I at Trinity Logistics. Adams holds a Master’s Degree in Data Science and has worked three years in the field, including his 2 years at Trinity. In his current role he focuses on applying Data Science techniques and methodologies to solve difficult problems, using AI to improve business outcomes, and supporting Trinity’s Data Engineering initiatives to improve quality assurance, ETL processes, and data cleanliness. 

Outside of his role at Trinity, Adams has a couple of personal Computer Vision and AI projects of his own! When he’s not tinkering away at those, he enjoys being outdoors, either hiking or kayaking! 

Cargo theft and fraud is a common topic in many circles these days. No matter where we look, we see it mentioned in news headlines and see its impacts on price increases on the store shelves. In fact, CargoNet recently shared data showcasing that cargo theft has reached a 10-year high, increasing 59 percent year-over-year (YoY) in the U.S. alone.

While cargo theft and fraud have always been a risk in the world of logistics, the kind of theft and fraud we see today is evolving from what we used to know. Years ago, common cargo fraud involved extortion attempts via loads held hostage as well as t-check scams, where bad actors would book shipments and ask for a fuel advance, take the money, and disappear, often without completing delivery or even picking up the shipment they received the payment advance for.

While these kinds of fraud still occur, they are not as prevalent as they once were. Today’s cargo theft and fraud involve new strategies, and it’s important that companies stay aware of emerging tactics and remain vigilant to protect themselves from unnecessary and tragic losses.

The Evolution of Cargo Theft and Fraud

In the past, cargo theft was commonly known as the act of stealing from a truck or even unlawfully taking cargo from a storage facility. Usually this might occur when the truck driver was asleep or when the parked truck was left unattended in an unsecured lot. Now, cargo theft claims for lost product can be especially difficult to navigate as many insurance carriers have specific language and exclusions written within the pages of cargo policies limiting circumstances under which they will cover cargo payment for cargo theft incidents.

For example, some policies may dictate that the truck must travel within a specific radius only and cannot be left unattended unless in a secured lot. Unfortunately, the definition of “secured lot” is wide-ranging and debatable. Does “secured lot” mean there’s a locked fence, good lighting, cameras, surveillance, or a physical security employee on patrol? There’s simply a lot of confusion and unknowns when it comes to cargo theft in general.

The Federal Trade Commission (FTC) recently reported that the top scam type for 2023 involved imposters. We in logistics and other sectors have been experiencing this concept. Scenarios expand beyond just plain and simple identity theft, at least in the logistics industry. The current phase of cargo theft involving identity misappropriation is very strategic. In fact, CargoNet also shared data that strategic theft has increased 430 percent year-over-year (YoY). As quoted by Scott Cornell of Travelers Insurance, “Strategic theft is when they use various means to trick you into giving them the freight and that’s through methods like identity theft, fictitious pickups, double brokering scams, those methods are where we’re seeing the biggest increase over the last 18 months.”

This strategic cargo theft makes incidents that were already puzzling that much more difficult to solve. This kind of cargo theft often spans over multiple state lines (interstate) as opposed to intrastate. When this occurs, jurisdiction is not given to local or state authorities but may extend to federal jurisdiction. However, to establish a case seeking federal help, not only would more than one state need to be involved, but the cargo value must warrant federal attention to exceed $100,000 and perhaps even $500,000 or more, depending on the states involved. You may have police consider strategic cargo theft a civil matter that will not be taken under investigation.

Further, with these scenarios, there is confusion concerning who takes or issues the theft report Should that be the shipper, the receiver, or the freight broker? Is the report filed with law enforcement jurisdiction at the origin where freight was often last seen, or should reports be filed at the delivery jurisdiction, where the cargo never arrived? The cargo owner may attempt to file a police report where the transfer of goods occurred, which is the location where the driver took possession of the cargo for transport. 

The State of Strategic Cargo Scams

Certain states like California and Texas have become hotspots for these strategic cargo scams, often involving imposter identities. Many of these fraudsters operate outside the U.S., finding and exploiting weaknesses in the system so products may end up outside the country. This further hurts the U.S. economy when items are produced here and those companies don’t get a financial return, thus losing money that should be going back into the economy. There are other additional costs affecting manufacturers to consider, such as lost time, materials, and labor to duplicate the goods that were originally stolen. 

The Federal Motor Carrier Safety Administration (FMCSA) system, originally designed to verify motor carrier legitimacy, has become a target. Phishing scams and other methods have targeted carriers with a motive to change a carrier’s contact information and use their MC authority for nefarious purposes. Bad actors have been caught posing as valid, reputable carrier companies. They often go as far as creating web domains similar to those of established carriers or caught purchasing MC numbers from those going out of business or wishing to exit, especially with the recent market slowdown, with the new buyer updating that company’s contact information. They look and “run” as the previous motor carrier, using that previous authority and reputation to run their scams. This facade allows them to gain possession of or intercept the cargo and divert it to a pre-arranged location for a quick disappearance. The FMCSA is aware of these trending cargo theft scams and is actively looking to improve their vetting and even get rid of MC numbers for carriers.

There’s also a prevalence in double brokering. This is when an unauthorized carrier accepts a shipment or assigns it to another carrier lacking the proper authority to broker it in the first place. It can also involve the re-brokering of shipments without authorization to legitimate entities with broker authority. This creates a chain of uncertainty and lack of control over carrier selection while increasing the likelihood of unpaid delivering carriers, and double payments required from freight brokers and shippers, paying more than agreed for delivery services. 

Load boards, while usually valuable tools for shippers and freight brokers to connect with available carriers, are also now breeding grounds for fraud. Fake postings and compromised information have created a haven for scam artists. The more information presented on a load posting, such as commodity, value, or even location, the more opportunity for these scammers to successfully replicate fake loads and even steal rate confirmations from legitimate actors. Detailed information provided on load boards make loads easier targets for those with motive to engage in strategic theft and double brokering. 

Cargo fraud involving identity theft is not limited to carrier companies. Scammers also pose as shipper companies. These shipping requests will often come in as inbound leads rather than relationship leads. Scammers create fake web domains and use reputable companies to gain credit access, then use that access to pose as both a shipper and carrier. They’ll orchestrate this fake shipment of theirs, make it look as if it has been delivered with forged paperwork, and collect payment before being found out.

A lot of these shipments involving strategic cargo theft end up being diverted to alternate warehouses. In these cases, it is common for the carrier booked to be legitimate but for a bad actor to somehow get in the middle and divert the driver to deliver to a different location altogether, promising an increase in money. Once the freight arrives at the alternate location, there’s a truck en route or waiting to whisk away the freight quickly. Diverted items do not stay in one place for long, making it difficult to track and recover the cargo.  

How to Be Proactive and Prevent Cargo Theft and Fraud

Trust, But Verify

Whether dealing with a shipper company or carrier, thorough verification is needed.

For shippers requests, use sources like ZoomInfo, LinkedIn, and Google. Research the company along with the contact who reached out. Remember that most shipper scams involve inbound leads as opposed to solicited new business or existing relationships. Before agreeing to arrange or transport the movement of goods, use Google Maps to ensure the requested pickup and delivery locations are legitimate.

For example, if the shipping company is a well-known business but Google Maps Street View shows you a location that doesn’t look like one of theirs, something might be off. Another red flag could be if the commodity to be shipped doesn’t line up with their standard business, such as an electronics company trying to ship lumber or rice.

For carrier requests, first vet that they have authority to operate with the FMCSA. View their safety rating and be cognizant of shipper or load-specific requirements, such as drivers who have TWIC cards, and commodity specifics, like cargo type and value. Confirm the carrier has adequate insurance coverage for the shipment they are booking, such as reefer breakdown coverage, if they are to haul a temperature-controlled shipment. Cross-check any other shipment requirements, such as interstate authority or hazmat certifications.

Review relationship and load history if they have a shipment history with you and ensure the new request lines up with previous hauls. Be aware of MC misappropriating. Check if the FMCSA information or profile has recently been updated. Some things to look out for are contact, address, or email address changes. Double-check that the carrier company is not associated with any known bad actors and adhere to your company’s own internal vetting standards.

There are various informational and vetting applications like Highway and Truckstop to further qualify carriers. Ensure the equipment the carrier owns lines up with the equipment required for the shipment. For example, it wouldn’t make sense if a carrier books a temperature-controlled shipment when the carrier owns no refrigerated trailers.

Use Relationships, Limit Load Boards

While load boards offer convenience, strong relationships with carriers provide a significant advantage. Building trust and gaining a deeper understanding of their business reduces your risk of cargo theft and fraud.

When load boards are needed, be strategic with your postings and focus on the security of the information you share. Scam artists often use load boards as a resource for information to replicate or find their next victim. They often look at the locations, the kind of cargo, and more. Avoid posting detailed descriptions of your cargo. Keep the information you share as simple and minimal as possible. Protect that information until the carrier has been both vetted and confirmed.

Trust Your Gut

If something feels wrong at any time, report it to your Risk Team or dedicated personnel right away. With cargo theft, time is of the essence. The first 48 hours are critical as stolen freight typically doesn’t stay in the same place very long. Your best chance of recovering it is within those early hours of noticing it. 

Your first line of defense to prevent cargo theft and fraud incidents is your pickup location. Those workers have the eyes and, hopefully, surveillance to see if the truck coming in is the right one. They can check that the MC on the truck is correct, that the driver has the right equipment for that shipment’s requirements, that the driver is the one booked, and that they know the correct location they need to deliver to.

Shippers can also be proactive and place trackers in with their freight, which can be beneficial for trip progress tracking as well if the load gets stolen. While many carriers do have trackers these days, it’s best not to rely solely on them. Scam artists have been known to disable trackers or ping them to another cell phone, making the shipment look like it’s traveling where it is supposed to go, while it is, in fact, stopped or delivering to an alternate location.

Shippers can also ensure they use strong seals that are tamper-proof. Ensure your dock workers are informed and proactive in reducing potential incidents. Shippers may also consider investing in extra shipper’s interest for its product, for a first-party insurance policy which provides a layer of extra protection. Develop strong relationships with the freight brokers you work with. Know who the emergency personnel are on the shipper and broker side and be armed with their contact information so you know exactly who to contact at all times of the day should a theft or another emergency occur during shipping.

Knowledge is Power

Stay in the know of what’s going on and trending in cargo theft and fraud by networking with known associations, like CargoNet and the Transport Asset Protection Association (TAPA), as well as the additional connection of the Transportation Intermediaries Association (TIA) for freight brokers. These organizations constantly educate on evolving threats to keep members aware.

Ensure you have an emergency plan in place and educate your team so you can be well prepared for an incident. Develop relationships with law enforcement and investigation agencies who can help you put the word out about your loss and assist in finding your freight or the scam artists involved.

Combatting Cargo Theft is a Shared Responsibility

Combatting cargo theft and fraud requires collaboration from all within the logistics industry. Shippers, brokers, carriers, legislators and law enforcement must work together to create a more transparent environment with strict accountability to make it more difficult for thieves and fraudsters to operate.

Cargo fraud is growing, and tactics are ever-changing. Even with all the right measures in place, fraud may not be 100 percent preventable. That said, though we can’t stop it all, we can implement not only prevention measures but response strategies. By staying vigilant and educated, we can collectively demonstrate to these bad actors that it’s not worth the effort anymore. It is up to us to create a future where the movement of goods is more efficient and secure than it is today.

About the Author

Kristin Deno currently holds the role of Director of Operational Risk at Trinity Logistics. Deno holds a Certified Cargo Claims Professional certification through the Certified Claims Professional Accreditation Council (CCPAC), with almost 15 years of experience and knowledge in claims, insurance, compliance, and risk. She has a passion for knowledge and servant leadership, always looking to grow professionally and share her expertise to those interested. She’s well known at Trinity for assisting fellow Team Members, Shippers, and Carriers with complex claims and doing all she can to mitigate any potentially concerning scenarios. Deno consistently looks for opportunities to share her knowledge and insight outside of Trinity, having recently attended the TIA’s 2023 Policy Forum in Washington, D.C., Traveler’s and CargoNet’s Cargo Theft and Transportation in Q4 2023, and the TAPA T1 National Cargo Theft Summit in May 2024. 

Businesses constantly seek innovative solutions for their supply chains to streamline operations, reduce costs, and enhance customer experience. Generative Artificial Intelligence (Gen AI) offers promises to fulfill those exact wishes. But can it do just that? I’m going to answer that question for you as well as offer some practical insights into how you should be implementing Generative AI in your supply chain. 

What is Generative AI?

Before we dive into its impact, let’s briefly discuss what exactly is Generative AI. Now, there’s a lot of definitions out there, but here’s how I define it. It’s a system that can create new patterns based on the interpretations of learned patterns. It also comes down to three main components.

Foundational Model

This is where the training/learning takes place, where you’re teaching the AI how to look at things and look at input.

Large Language Model (LLM)

This model is trained on vast amounts of text, can interpret what you’re asking of it, and can put a response in words that you can understand.

Natural Language Processing Model/Chatbot

This is the most visible portion of current Generative AI implementations, like ChatGPT. This model component is generally a conversational chatbot leveraging the LLM to create content. It was first trained on a foundational model and then fine-tuned with human feedback. 

Generative AI is first trained on a foundational model and then fine-tuned with human feedback and additional data.

Now, none of these are really new technologies. They’re just available more widely today because of the lowered cost of processing power, rise in cloud technology availability, and ability to push it out in more cost-effective manners. 10- 15 years ago, it was far too expensive. As prices have come down and processors have become more powerful (think Moore’s Law), it’s allowed Generative AI to become more easily accessible. 

When you talk to Generative AI it feels like it understands you. Spoiler alert, it doesn’t. Its responses are based on data it has consumed and a resultant powerful prediction mechanism. Don’t think that Gen AI “understands”; it doesn’t. This has significant impact when it comes to focusing a Generative AI into a particular industry, like logistics. Generative AI can’t apply specific nuances of an individual industry unless it has been trained on them, and even then, there are other obstacles to Gen AI being useful outside of targeted use cases. This is a key point in how we think of getting Generative AI to effectively impact supply chains. 

Public vs Private Generative AI

When you use Generative AI, there’s a big thing you should be concerned about – public vs. private Generative AI.

Public AI

We talked about ChatGPT. That is one example of a public version of Generative AI. Anything you input in there is being reused for training purposes. Any of the prompts you put in, it’s using that to train itself and feed back into that foundational model for others to use. That becomes more or less public domain once you put it in there. You must think about it that way. It’s like posting something on the internet – it’s there forever. 

Public AI is great to use for idea generation, but only put in anonymized data, that’s if you put in any data at all. 

I also cannot stress this enough – fact check what it generates. Generative AI will make stuff up – the industry term is hallucinations. It’s the worst people pleaser you can imagine. It will give you what you ask for, whether that’s true or not. There are advances in the works to help “fact check” the tool, but you must remember whatever Generative AI does today is opaque. We don’t know where it’s pulling this information from. You’ve got to go in and check its responses.

Private AI

There are opportunities to use private Generative AI. Amazon Bedrock and Microsoft’s OpenAI implementation have private options to use such that the prompts and any data you put in are not used for training the core foundational model. But you must make sure you’re on the right (private) version of the tool. 

The second thing is that if you feed it data, you must make sure that data is right because Generative AI has no clue. It’s assuming what you’re telling it is true. Though it does have the ability to fact check your data, it will only do so if you ask – and ask correctly. You must have clean data and validate the results that come out of it.

Again, private AI is great for idea generation but not so great yet as a fully automated toolset that can be used to drive business forward. It’s getting there, but not there yet. Private AI options will get there faster for certain topics, industries, and businesses than public AI. 

Generative AI Latest Features 

There are some new features that have come out for Generative AI recently. One of those is the ability to relate images and text. You can put an image up, and it can write a story about that image. You can input text, and it can create an image, like Dal-E. 

There’s also code creation and injection. It can generate code, and when I say code, I mean programming. OpenAI has functionality where you can do code injection. Github has its copilot technology where it can write code alongside you based on a plain English prompt. 

Another feature is enables tools to call digital actions. So, I can go from a Generative AI tool and call out to create an action in another application. This is where we shift over into physical actions that we find in Logistics 4.0. For example, a robot in a warehouse that results in a physical action, or it could just be something as small as, if this event takes place, then make this phone call or change this data. 

The Impact of Generative AI in Supply Chain

What’s the logistics impact of Gen AI? Biggest one is data analysis. 

Data Analysis

Generative AI is great at taking data in and putting it into a format that can be easily used to extract meaning. Think about this from a summarization perspective. You’ve got somebody on a call with a customer. Based on their phone number, Generative AI identifies them and comes up with a prompt that tells you the last 30 days of that customer’s history (products, contacts, issues, etc.) so you can have a much more informed conversation with them. 

Trending is another great one to look at across data sets. As you put the data sets in and tell Generative AI to relate these two things, you can start to see trends against each other. It’s interesting what you can do with those pieces.

Then there’s assistive information delivery. At points in time where actions need to be taken, information can be delivered as a prompt to the person performing the action. This can happen in real-time with some tools, or it can happen in the background.

These are all pieces that you can put into place today.

Predictive Analysis

A primary use case within logistics is predictive data analysis. 

Routing is one but that’s going to take some work to be effective. That’s because it needs specific knowledge sets around the model and those knowledge sets are not available to most of generic models. There isn’t a specific logistics model yet that understands all the nuances to the different modes and can bring in the data sets that are necessary to truly understand routing. There are tools you can use for routing right now, but they’re not Generative AI tools.

Exception identification is another one. Again, this is a large data problem and there are a lot of factors that come into it. Any supply chain is essentially a complex system. Being able to identify those exceptions earlier is going to be something Generative AI will help with when it comes to logistics and supply chains.

Hyper-personalization is a great one. That’s getting information into a format that speaks to a particular person, entity, or customer. You can hyper-personalize a piece of communication to them based on the information you know to make sure it’s something you can use to improve service delivery. 

These benefits of Generative AI are really where we’re going to be able to improve service delivery across supply chains as it evolves. 

How to Use Generative AI in Supply Chains Today

You’re likely wondering, how can I use it today? I’m happy to report that there are several different ways you can being using it. 

Improving Customer Experience

One of those ways is improving customer experience. Using a private AI, you can feed it customer data. Then, when you’re talking with customers you can bring up insightful information. It can bring up things like a notice of a customer’s birthday and save you time from having to go and look up specific pieces of information. 

If you’re feeding the data into a system, you can use these private models to intake that data and provide relevant information back to someone that’s interacting with a customer. This is where I like to remind everyone that Generative AI isn’t a replacement for people in customer service but is great for putting information into a digestible format to support people in their roles. 

Summarization

One of the big things is summarization. Summarization is an awesome feature of Generative AI. It’s ability for it to take data and create a summary is a tool that you can use today. Getting summaries of articles, getting summaries of customer history, getting summaries of a lot of different types of data is something the current application can do and do it well. You still have to fact check it, though!

Personalization

The other big piece is the creation of personalization. Getting that data in and creating a personalized email with some information about who you’re sending to is something that Generative AI can do and at scale. There are tools you can use to do that and it’s something that you can put into place today and try out. 

Think About Your Data

I would encourage everyone to get started and try things. It’s not going to be perfect out of the gate. I can tell you that right now. 

One of the key factors that you must think about as we move into this Generative AI world, is data. Good, clean data is at the core of successful AI use. You must think about how clean your data is, what data you’re going to send to an AI, are you public or private, all those pieces. 

Accuracy and consistency are the core. You must establish your data governance. It’s great if you have clean data, but if you start feeding bad data in again, all you’re going to do is skew your results coming out. Now is the time to start evaluating your data sets. Understand where you have good data, how your data is inputted, where you can start, and where you need some work. Then, get your data in line and start to think about how you can enrich that or what can you do with it.

Before You Get Started…

There are other pieces to consider before you fully dive into Generative AI. 

There’s an ethical component here. There’s change that’s coming. People’s jobs are going to be affected. You must think about how this impacts your team. What trainings do you need to provide? How do you get people brought along for this ride instead of leaving them behind? What’s the impact to your customers? If you’re relying on this as an assistive technology, how are you making sure that you’re still servicing your customer effectively? 

One great thing about automation is that it makes things happen in the background. The bad thing about automation is things happen in the background. Unless you understand what’s happening, can react to that, and leverage it to effectively service your customers, you’ll end up doing them a disservice. That’s true for any automation and Generative AI is no exception. 

Then there’s change management. How do you prepare your people? How do you get to the point where you have prompt engineers? 

Each AI works differently. If you give the same prompt to different AI applications, you’ll get different answers. So, how do you understand how to engineer those prompts? How do you identify this skill? These are critical questions you’ll need to answer over the next few years as we move into this Generative AI world. 

FAQs 

Are There Logistics Focused Tools in the Market Today Leveraging Gen AI?

There are. I would say that the use of Gen AI in logistics tools is still a little new. Tools like Parade, which is a very specific toolset around carrier relationships is leveraging some Gen AI capabilities. Most Gen AI is relatively generic. Tools like Observe.ai or Freshworks are available today with some Gen AI capabilities. 

How Do You Measure Success When Leveraging Generative AI Initially?

We always look at success when we look at automation as reclaimed time. What we mean by that is, are we giving someone a technology that assists to a point where we’re able to redirect them to more strategic activity. The other one is really asking the people who are impacted by it. You need to get feedback so you can understand if it’s being helpful or harmful.

When Should I Start Using Generative AI?

Now! Encourage its use, though with appropriate guardrails and guidelines. It’s going to be a disruptor. Gen AI is not a disruptor quite yet, but it’s getting there, fast. The widespread availability and use will accelerate its impact on many industries and roles. 

What’s an Example of How Trinity Logistics is using Generative AI today?

We’re using it in a couple of different ways. One tool we use is Observe.ai, which is a natural language processing tool. We use it to look at our interactions with customers, look at sentiments, look at certain events, look at what type of interaction was this, and that uses that Gen AI to provide us with that information. Then we have a Development Team, so we’ve started working with some of the code copilot, so assistive coding technology. 

As the industry pivots from Logistics 3.0 to Logistics 4.0, the role of Generative AI, a subset of artificial intelligence that can generate data like what it’s trained on, is becoming significant. 

What could and should you do with this technology?  Let’s follow, Alex, a seasoned logistics manager, into his journey with Generative AI in logistics. Hopefully, it will help you determine how to best apply this to your business.

Logistics 3.0 vs Logistics 4.0

Amid the hustle of daily logistics operations, Alex has found himself standing at a crossroads. The days of Logistics 3.0, with its familiar integration of technology and automation, were beginning to fade. A new era, Logistics 4.0, was dawning, bringing with it the promise of Generative AI.

In Logistics 3.0, Alex had seen the wonders of electronic data interchange, warehouse management systems, and transportation management systems. These tools paved the way for efficiency, streamlining operations, and data storage. Yet, something was missing. While data accumulated, its full potential remained untapped, and decisions largely depended on human insight.

Enter Logistics 4.0, a realm where technology is integrated and intertwined with every aspect of operations. Generative AI became Alex’s trusted advisor, offering capabilities that went beyond conventional wisdom. Here, data was stored and analyzed in real-time, creating predictive models, forecasting demand, and even simulating myriad scenarios.

Generative AI: A Helping Hand

With the introduction of Generative AI, decision-making saw a paradigm shift. No longer were decisions merely human-driven. Now, vast datasets could be processed with unparalleled speed and insights delivered directly to the team. 

Alex marveled as the AI suggested new routing strategies that outperformed traditional methods. When it came to risk management, AI could paint potential risk scenarios from existing data, enabling further preparedness against unforeseen challenges.

A New Era

Where Generative AI truly shone was in its influence on hyper-personalization and customer experience. 

Alex recalled a customer who had faced delivery delays in the past. Now before the customer could express any concerns, Generative AI analyzed previous interactions, preemptively providing solutions to identify and allow proactive mitigation of any delays. Such predictive customer service seemed like magic!

Customers were no longer treated with one-size-fits-all solutions either. Analyzing their preferences and behaviors, Generative AI tailored communications to fit an individual’s needs, background, and details. Natural Language Processing, powered by the same AI, equipped teams with accurate and individualized customer information. Moreover, dynamic pricing models consider each customer’s history, offering personalized pricing and discounts.

The power of Generative AI didn’t stop there. It sifted through mountains of customer feedback, highlighting sentiment and crafting actionable insights. This dynamic tool kept the team agile and evolving customer needs stay met.

In this new era of Logistics 4.0, Alex realized that the industry was undergoing more than just a technological shift. It was a transformation of values and priorities. Every customer interaction was now an opportunity to offer a more tailored, predictive experience that exceeded expectations.

Generative AI Concerns

Yet, as these capabilities expand, so do ethical concerns. Alex understood the importance of utilizing AI ethically, ensuring that it complements human skills rather than replacing them. He was also aware of the apprehensions among his team members.  They feared that AI might render their roles obsolete.

To address these concerns, Alex led training programs, emphasizing the useful relationship between AI and people. He shared how Generative AI could handle vast datasets, leaving the strategic and empathetic decisions to people, who were irreplaceable in these capacities. This synergy would ensure efficient logistics while preserving job roles, emphasizing the value of human touch in an AI-driven world.

Customer interactions presented another challenge. With Generative AI’s potential to personalize experiences, there was the risk of infringing on individual privacy. Alex championed transparent communication. He educated customers about how their data was used and the measures in place to protect their privacy. Training sessions were organized to equip team members with knowledge about ethical data usage, ensuring customers felt safe and valued.

Alex also recognized the importance of avoiding the misuse of AI. Though AI could suggest new routing strategies or predict potential challenges, the decision-making power remained with people. Alex believed that AI should inform decisions, not make them autonomously, especially when people’s livelihoods were at stake.

A Whole New World of Logistics

As he looked ahead, Alex felt optimistic. In this age, where brand loyalty was deeply rooted in personalized experiences, he knew that with Generative AI by his side, they were poised to lead, innovate, and set new standards in the world of logistics.

The leap from Logistics 3.0 to 4.0 marks a significant shift from manual and reactive operations to automated, intelligent, and proactive systems. Generative AI stands at the forefront of this revolution, providing the tools necessary for logistics companies to harness the power of their data, make informed decisions, and stay ahead in the competitive market.