The logistics and delivery industry is always a massive, intricate puzzle. Every package, with its unique dimensions and destination, presents the challenge of finding the optimal route amid many possibilities. Traditionally, as I recently spoke about during my presentation “AI Before It Was Cool” at the Trimble Insight Tech Conference + Expo, humans played this game, often making it feel like a repetitive spin on a Vegas slot machine. However, the dawn of AI (artificial intelligence) in logistics promises a transformative change.
OneRail is at the forefront of this revolution — we’ve been using AI in our platform for the past three years. Using AI’s power in transportation and logistics, OneRail has achieved impressive feats in the delivery ecosystem: A staggering 98% on-time rate and a platform processing billions of transactions, whether omnichannel or unified commerce. Yet this is only the start. As the complexity of the industry grows, the promise of AI delivery and decision-making shines even brighter.
The Rising Importance of Data
Projected to reach $407 billion by 2027, the AI market could also elevate the US GDP by 21% by 2030. In logistics, where data is as vital as oil, how do data collection and fidelity merge with AI to capitalize?
The Pillars of Data: Collection and Fidelity
Every click and transaction leaves a digital trace of our choices. Yet, while collecting this data offers rich business insights, it’s about more than quantity. The trustworthiness of data, known as data fidelity, is equally if not more important. Without it, businesses risk chasing false leads, wasting time and harming their reputation. This concept has many layers to consider:
- Accuracy – Ensuring the data reflects the real value.
- Completeness – Having all the necessary information, as gaps can skew insights.
- Consistency – Uniformity across different sources and formats prevents confusion.
- Timeliness – Current and available data to make real-time decisions.
Powering AI for Better Decision-making
Data is the lifeblood of AI and machine learning (ML). The fuel that allows these technologies to learn, adapt and evolve. With vast, diverse datasets, machine learning applications refine their skill, improving pattern recognition over time. The emergence of big data has shifted many organizations from hypothesis-driven models to a “data-first” approach, valuing the granularity and nuance of vast datasets. Yet, even small datasets have their role, with emerging AI tools transforming processes even when massive data isn’t available.
Understanding AI in Logistics
The world of logistics is undergoing a seismic shift, thanks to the infusion of AI, silently orchestrating supply chains as packages crisscross the globe and delivery trucks weave through cities. But what exactly does AI mean in the context of logistics? Let’s clarify some common terms.
Machine Learning
At its core, machine learning is like teaching computers to learn from experience. Imagine a child touching a hot stove; the pain teaches them to avoid it in the future. Similarly, ML models get fed vast amounts of data, training them to recognize patterns and make decisions. In the realm of AI in logistics, ML is the brainpower behind forecasting product demand or preemptively flagging potential shipment delays, ensuring that the supply chain flows smoothly.
Predictive Analytics
Picture this: you’re about to embark on a road trip, and a friend tells you the best times to avoid traffic. That’s predictive analytics in action, albeit on a much simpler scale. It dives into historical data, combines it with statistical algorithms and ML techniques, and paints a picture of potential future scenarios. In the world of AI in transportation and logistics, this means anticipating peak shipping times, determining the best routes to avoid congestion and ensuring packages reach their destination on time.
Reinforcement Learning
Have you ever played a video game where every right move gets you points, and every wrong one deducts them? That’s the essence of reinforcement learning. Here, agents (or algorithms) interact with their environment, making decisions and receiving feedback through rewards or penalties. It’s a continuous loop of action and reaction. In AI delivery and logistics, this concept is invaluable. For instance, it can dynamically optimize delivery routes based on real-time feedback, ensuring that the package reaches its destination and does so in the most efficient way possible.
Real-world Applications
Many of you have heard about the buzz surrounding ChatGPT by OpenAI. Did you know that GPT is primarily a prediction machine? It predicts the next word in a sentence quite accurately based on the context provided by the user. Similarly, OneRail can analyze data from our business to forecast outcomes, such as on-time rates or potential savings. Additionally, given that 65% of consumers are willing to pay a premium for faster, more reliable deliveries, this could significantly boost profits, especially amidst the challenges of congested streets and increasing demands.
Optimal Package Delivery Paths
Picture Sarah, a small-business owner who ships artisanal crafts nationwide. Each package is a promise to her customer, and she can’t afford delays. So, when sending a delicate piece to a loyal buyer in a different city, she relies on something other than traditional routes. Instead, she leans on advanced ML models, which sift through countless routes to find the most efficient path. As unpredictable as deliveries can be, from weather-related disruptions to traffic jams to road closures, predictive analytics can help her foresee these potential hiccups, ensuring her crafts reach their new homes on time without a hitch.
Efficient Deliveries
Have you ever wondered why some products arrive faster than others, even from the same store? Behind the scenes, predictive analytics is at play, forecasting demand in specific regions. This insight enables companies to position their stock strategically, slashing delivery times. And in an era where every penny counts, dynamic pricing takes center stage. ML models actively adjust delivery prices in real time, considering factors like demand, distance and time of day. Think of it as the stock market of AI delivery, where prices fluctuate to balance demand and supply, ensuring both the customer and the company get the best deal.
Humans and Machines: A Synergistic Approach
We’ve all heard of those dystopian sci-fi stories of machines or robot overlords replacing humanity. But, instead of the feared machine takeover, reality shows that AI in logistics is shaping a future where humans and machines work hand in hand, amplifying each other’s strengths.
The Misconception of AI Replacing Jobs
The pandemic might have seemed the perfect time for AI to take over human jobs. Still, as Ty Findley from Ironspring Ventures pointed out at the Trimble conference, that didn’t happen. Instead of seeing AI as a job replacer, viewing it as an activity reshaper is more accurate. Repetitive and mundane tasks may transition into digital activities, but this doesn’t spell the end for entire occupations. In essence, while AI excels at handling repetitive requests, as highlighted by Trevor Ward of Rippey AI, it creates space for humans to shine in areas where they naturally excel.
Combining Human Intuition and AI’s Data-Processing Capabilities
Chris Torrence from Optym emphasized that while humans are invaluable assets in any organization, our brains cannot process the vast amounts of data that AI can. In this unified blend of human and machine, AI shoulders the data-heavy tasks, allowing humans to channel their energy into creative problem-solving, nonlinear thinking, and relationship building. Chief growth officer at freight tech consulting firm Metafora, Ryan Schreiber’s perspective resonates with this, as he notes that when leveraged rightly, technology can magnify human productivity.
Real-world Implications: Supercharging Logistics Efficiency
OneRail’s story perfectly captures the powerful synergy between humans and AI. Before integrating AI, Onerail’s dispatchers managed 80 loads a day. With AI’s assistance, they now handle a staggering 3,200 loads daily. This increase showcases AI’s efficiency and role in empowering logistics staff to surpass previous limits and significantly boost their operational capacity. Moreover, by leveraging AI, companies can reduce errors in manual data entry and proactively forecast maintenance needs for vehicles or machinery.
Last Mile Deliveries: AI’s Pivotal Role
Logistics, the unsung hero of our digital era, ensures our packages arrive safely and quickly. Now, with the infusion of AI into this sector, there’s a promising horizon ready to address the industry’s most pressing challenges with renewed vigor.
The Last Mile Delivery Conundrum
The last leg of a product’s journey, often termed last mile delivery, presents a unique set of challenges. Delivering directly to individuals — whether at their homes or businesses — adds complexity, unlike the more straightforward task of shipping to distribution centers. The clock is always ticking, with many of these deliveries being time-sensitive, adding pressure to maintain punctuality. Moreover, this final stretch often comes with a heavy price tag, making it the costliest phase in the entire delivery process.
AI: Revolutionizing Delivery Dynamics
Despite these challenges, AI emerges as the hero, bringing innovative solutions to the forefront.
- Dynamic Routes: Gone are the days of static delivery routes. With AI’s prowess, routes can be adjusted in real-time, considering factors like traffic congestion or sudden weather changes, ensuring timely deliveries.
- Precision in Predicting Delivery Windows: No more waiting endlessly for a package! AI’s capability to analyze various parameters allows it to offer customers precise delivery time estimates.
- Proactive Exceptions Management: In the unpredictable world of logistics, exceptions are inevitable. However, AI can foresee potential hiccups, like missed deliveries, and provide proactive solutions, whether alternative delivery options or timely customer notifications.
The Future Outlook
AI’s imprint on logistics becomes increasingly evident as we prepare for the future. No longer just an assistant for mundane tasks, AI is gearing up to be the brainpower behind resilient, efficient and agile supply chains.
- Supply Chain Optimization: AI’s predictive prowess can help inventory management by fine-tuning stock levels and mitigating overstock and stockout scenarios. AI also offers insights into supplier performance, ensuring a more reliable supply chain.
- Predictive Capabilities: From natural disasters to strikes, AI can foresee potential disruptions, crafting alternative strategies on the fly.
- Job Dynamics: AI’s integration can reshape job roles, but the future will likely emphasize augmentation over replacement.
- Adapting to Tech Evolution: As AI constantly evolves, there’s an imperative for ongoing learning within logistics. Companies should offer periodic tech upskilling sessions and foster collaborations with tech innovators, ensuring they’re always a step ahead.
The Future of AI in OneRail: Unlocking New Value
In our exploration of AI in logistics, the vast potential of the future stands out. I see a growing appetite for AI-driven data products. This momentum reflects the industry’s eagerness to leverage data, particularly when enhanced by AI. OneRail’s impressive 98% on-time rate and its OmniPoint™ Platform’s capability to process billions of transactions showcase the immense possibilities.
Yet, as we experience this technological wave, the importance of human collaboration remains paramount. It’s about a partnership where humans and AI work together, each enhancing the other. For those in logistics, now’s the time to use AI’s full capabilities and drive the industry to new heights of efficiency and innovation — the future beckons with exciting promise.
To explore this further, schedule a demo with OneRail today.
