It’s now cliché to say that sustainability is on the minds and budgets of executives. In a way, it has always been there, especially regarding logistics. All other things being equal, less fuel consumption and fewer miles driven result in lower costs. Reducing transportation costs, shaving off shipping time, and generally using transportation equipment more efficiently have historically been important and even strategic parts of minimizing the cost of goods sold.
But even though they are related, efficiency and sustainability aren’t the same, and the distinctions are becoming more evident every year. To start with, as disruptions become the norm (another cliché) and error margins become razor-thin, the supply chain’s decisions are increasingly interdependent. It’s a little like running: when you’re on flat, predictable terrain, you can easily take a quick look at your environment, pick out your destination, and run in that direction with your eyes closed. No problem. But on uneven terrain, it’s important to see each step as you take it. Today’s environment isn’t just uneven; it shifts as you move. What’s coming next: a flat spot, a bump, a hole, or sloping ground? These factors will determine where you step, how you brace your foot, and even whether you pick an entirely new path. Alternatively, your steps must adjust when your plans adjust. It’s no good to have legs that go on robotically running in yesterday’s direction when you now know it’s better to head somewhere else.
These observations all imply that transportation has to be executed in a way that factors in the planned destination and as much data as possible about your landscape. It should also continuously re-assess all assumptions. As with a runner, where you want to go determines how you should run, and how you run determines where you can go, and the landscape determines where you can feasibly get to. In a dynamic environment, all this information should flow in a continuous feedback loop that keeps you on the most stable, reliable, beneficial, and efficient path.
What does all this have to do with sustainability? Well, it’s because every decision across supply chains is interdependent, yet each affects and is affected by the others. Sourcing decisions impact manufacturing. Manufacturi affects sales volumes and target markets. Target markets influence product design, which in turn comes full circle to affecting sourcing again. And transportation is sandwiched in the middle of all these (and many more) stages and micro-stages. So when you’re trying to improve sustainability in transportation, it’s important that transportation teams inform — and be informed by — all the other supply chain processes in something close to real time.
Going beyond emissions calculations
Transportation is a natural place to start on the journey to net zero. Of all the Scope 3 areas, it is one of the few where greenhouse gas emissions are straightforward to measure. All the variables, such as weight, mileage, fuel type, and equipment, are known — or at least knowable. Calculating your current footprint is meaningful and valuable, but it’s also retrospective, similar to building your product and only then, after it’s too late, tallying up the cost. With transportation, just like with cost, the better way is to know the impact ahead of time so you can identify ways to reduce it. But even when you act to reduce your emissions, you’re often doing so within the context of the pre-specified transportation plan that was developed in a silo and remains in a silo, humming on in the same direction even when new information means it’s better to adjust course.
Defining what’s possible by asking “what if”
What if transportation planning could “see” into the demand plan to be sure to book enough capacity (and not more) to move the volume of goods produced? What if companies had insight into how much demand was so reliable that they could slow down supply and transportation and still get goods to market on time — and with lower emissions? To build upon that, what if the forecast were so accurate that companies could cut their safety stock levels, reducing their footprint — including the transportation footprint — required to meet demand? What if sophisticated AI could look at the universe of available data and tell you how much inventory to stock at each location to meet your needs and avoid expedites? What if, through better planning like this, you gained the time to switch to slower transportation modes that cost much less and also emit less CO2? What if you had advance warning of a storm that places your shipment in jeopardy? Then you could adjust course sooner rather than backtracking later, emitting more CO2 and risking the shipment and all its environmental footprint incurred up to that point.
Capabilities like these are especially valuable in scenarios where the disruption affects the shipments of materials or components in a way that has ripple effects across dozens or hundreds of other shipments. A quick readjustment exponentially benefits costs, carbon, and service levels.
And vice versa, what if the demand planning function could see what was happening in the world of sourcing and logistics, and could identify constraints, understand the business and environmental impact, and quickly form and execute a new optimal plan that minimized redundant steps, costs, emissions, and waste?
I’m describing a supply chain that operates like an organism: it takes in as much information as possible from all available sources, derives meaning, forms optimal plans, and executes those plans while constantly re-assessing and re-adjusting to accomplish its goals with as little energy and risk as possible.
Establishing three things, all at the same time, all working together
Even in isolation, transportation has a huge role to play in making supply chains more sustainable by moving goods more efficiently and with lower emissions. However, when connected to all the other functions in the chain, logistics becomes an enabling function to reduce emissions upstream and downstream throughout virtually all business processes involved in making, moving, and selling goods.
Achieving this potential takes three things.
- It takes data. Lots of data. Mind-boggling volumes of data flowing in, through, and back out of the organization continuously from as many sources as possible. It’s difficult to see how data flows at this scope and scale could be achieved without a supply chain business network connecting the various tiers and ecosystems and enabling the flow.
- Second, this scope and scale of flowing data quickly surpasses the capability of any human analyst — or even a massive team of analysts. Making sense of the masses of data, identifying optimal decisions, and monitoring the execution of those decisions takes sophisticated AI and specialized tools that translate disparate data into a common language, like turning noise into a symphony.
- Functional tools and applications that orchestrate the many business processes to bring a product from cradle to grave — and even back to cradle in the emerging circular economy — are the last essential component.
Fortunately, solutions exist today that specialize in connecting complex, multi-tier supply chains on a business network, making sense of the resulting data flows through AI, and driving highly efficient collaboration and execution through workflows and tools that take account of end-buyers, supply chain planning, sourcing, manufacturing, transportation, sales, and even final delivery. There are many different onramps on the road to net zero, and as we begin this new year, there has never been a more relevant time to begin the journey.
Daniel Smith is Director of Product Marketing for ESG at e2open.