5 big challenges for third party logistics in 2019
It’s cliché to say that the next five years are going to be very different for any industry, but if any industry has a genuine shot at reinventing itself, it’s third party logistics (3PL). Why, you ask? The 3PL industry parallels the behavior of customers in real life. Think about how you shop. More importantly, think about how that’s changed over the last five years. Just today I ordered a set of notebooks on Alexa and am expecting my package to be delivered no later than tomorrow. The impact that new shipping expectations has on the supply chain, from D2C (direct to consumer) shipping to rapid-shipping, is huge. We’ve been fortunate to be in the driver’s seat of the changing 3PL landscape at Noodle.ai, working with some of the largest 3PL companies to help prepare for the new world order that is modeled after Amazon-like efficiency.
What does Amazon do really well? It’s not just one thing – they break their business down into individual components, enable machine learning for the most important functions, and ultimately interconnect these components. When they made AWS internally for their own needs, they thought about how to plug it into other parts of the business to improve overall system reliability and lower costs. When they made Amazon Prime 2-day, they thought about how to get that extended to other sellers to improve overall shipping times, not just for items sold by Amazon.
That philosophy of interconnectedness is at the core of Noodle.ai’s applications for 3PL.
What does the changing landscape of 3PL mean for 2019? Let’s examine five of the top challenges:
- Asset utilization returns to the board room
By all measures 2018 will set a new record for new truck orders (consider Class-8 for the purpose of this post). A surging economic cycle combined with electronic logging device (ELD) restrictions creates a need for more trucks. This in turn translates to increased capital expenditures. For a medium-to-large sized operator, the combination of managing an increased number of assets with an increasing load volume is likely to result in suboptimal asset allocation. Smart logistics leaders have recognized the need to utilize advanced AI and machine learning techniques to exploit network density and predictively string together continuous moves to ensure high asset utilization and driver retention.
- It’s no longer your father’s brokerage (non-asset) world
Non-asset divisions and companies have had it easy the last few years. A combination of ELD restriction, capacity constraints, increased load volume, and favorable spot rate market has resulted in tremendous growth and profitability. Many 3PLs now see their brokerage divisions as a “growth-driver” of the company. Forward-thinking leaders are already gearing for this next change by utilizing advanced techniques for bid pricing and carrier selection to drive more throughput. “A pricing strategy to simply look up DAT is not really a strategy,” said one VP of a non-asset company. Similarly, calling a long list of carriers to cover a load is suboptimal. The smarter operators are trying to predict capacity direction and taking advantage of deadhead or home-base routes while reducing their overall line-haul costs.
- Conquering your fleet cost-per-mile
One of the most overlooked levers in an asset-based 3PL company is fleet maintenance. A typical medium-to-large sized operator has anywhere from 2,000 to upwards of 5,000 tractors that cost approximately $10k – $12k per year to maintain. One VP of a medium sized fleet said “Our default maintenance strategy is to follow the DOT manual–it’s not terribly efficient.” The data doesn’t lie – for many companies, an increase in their preventive maintenance budget has not resulted in a meaningful corresponding decrease in repair orders or CPM. Establishing causal attribution is one of the hardest things in this space due to the large number of dimensions at play.
- Stop playing guessing games about solving your labor plan
3PLs with a large footprint of distribution centers are all too familiar with the challenges of labor planning which makes up over a third of the cost of running the business. “Our process to plan labor isn’t very scientific, and that means we frequently over-allocate or under-allocate” said one General Manager of a large DC catering to a top national retailer. Scaling labor planning across the enterprise is further complicated by the different planning styles and tools in place at each facility. Complexity is the enemy of consistency when it comes to these tools and systems. Existing warehouse management systems weren’t really built to adjust dynamically to the dance of the inbound and outbound demand, let alone predict daily labor count. This is yet another problem that’s tailor-made for application of artificial intelligence and machine learning techniques.
- Delivery performance expectations will continue to rise
The 3PL industry continues to experience heavy influence from e-commerce. This can cause double-digit growth in volume with a corresponding requirement of expedited delivery times and a need for increased reliability. With this increasing external pressure on freight capacity, capitalizing on the scarcity of available carrier capacity is essential and will help you reduce waste and minimize operating risks. Applying data science and machine learning to these challenges through our apps will help you create the interconnected, efficient, intelligent system you need.
If you want to master these challenges in 2019, you need to set yourself up for success. Talk to us about our suite of apps that can solve many of these 3PL challenges and put you on the road to radical efficiency, better asset allocation, and cost reduction.
- Webcast replay: Navigate turbulent times in your supply chain
- Coping with COVID-19 in supply chain planning
- Disaster-proof your supply chain with AI and be ready for the next global event
- AI can reduce the impact of black swan events like COVID-19 on your supply chain
- Noodle.ai named to the 2020 CB Insights AI 100 List of Most Innovative Artificial Intelligence Startups