February 21, 2022 Logistics Data Transformation: First Steps

Volume 25 | Issue 1

Digitizing and analyzing data to improve logistics and supply chain performance and prevent disruption. Where to begin.

A journey of a thousand steps begins with the first.

Okay, Lao Tzu wasn’t a logistics manager, but the point still applies all these centuries later. Supply chain disruptions during the pandemic underlined for many companies the absolute necessity to digitize their logistics management systems. Problems inherent in outdated processes aren’t going away when the pandemic does finally end. Indeed, COVID served to bring these issues to light.

The result is that logistics is no longer about just handling physical goods. Logistics is the effective management of data flows created by the adoption of new technologies and the digitization of supply chains.

Previous to the pandemic, as Arun Kochar, a partner in the strategic operations practice of global consulting firm Kearney, points out, “Digitization of the supply chain has been an elusive goal—always on the horizon but slow to materialize.” If nothing else, the pandemic has made many companies get closer to that horizon. But, again as Kochar notes, “The path forward can be daunting: a steep learning curve, internal resistance to change, siloed decision making, multiple sources of information, and the scarcity of cost-effective, customizable technology solutions have left many companies stuck in the experimenting and testing stages.”

So, how do you get unstuck and take first steps towards the data transformation of your logistics journey?

First Off, Don’t Get Baffled by Technology

Unquestionably, effective implementation of new technologies can streamline logistics management of the supply chain with cost-efficiencies. The key word here is effective. It’s easy to get dazzled by the latest shiny promise of digitization or machine learning or whatever technical solution promises to solve all your problems. It won’t.

As Sebastien Breteau, founder and CEO of supply chain solutions provider QIMA, observes, “The supply chain technology landscape has been abuzz with nascent technologies being touted as the next big thing before they’ve quite come of age…From a supply chain perspective should a business really focus on implementing early-stage technologies, like blockchain, Internet of Things (IoT) and drones, when it is still facing challenges in moving past manual pen and paper data entry or relying on Excel spreadsheets?”

The pandemic accelerated the adoption of new technologies. But adoption of any new technology is almost by definition disruptive. It’s a new way of doing something. It can also mean new unanticipated problems. Which brings us to the concept of “digital transformation.”

While digital transformation has become a buzzword that gets bandied about a lot, it actually is a useful descriptor, though the terminology is perhaps a bit misleading. It’s not just about digitization and technology. It’s about how digitization and technology:

• Solves a specific business problem;
• The lessons learned by solving it;
• The implications of what’s learned to other logistics processes.

That’s why it’s called a journey, another buzzword, but also another apt descriptor. Because it is a journey, a continual learning process. In addition to solving a problem, a technology solution generates more data. And what you learn from that data not only helps you refine the original solution, but helps you figure out how to improve other aspects of your business and your logistics.

As MIT Professor at Yale University’s Center for Transportation and Logistics, Dr. Yossi Sheffi, emphasizes, “Don’t think in terms of what IoT, robotics, blockchain, or automated tracking can do for you; think in terms of the problems first and then look at the technologies themselves. If you’re grappling with low productivity levels in a key warehouse, for instance, don’t just automatically zero in on a robotics investment… come up with ways to creatively augment and support human labor in that setting. If robotics is the answer, then by all means, explore that as a potential option. If not, then keep an open mind about other alternatives that can propel your company one step further toward the digital supply chain.”

What’s key here is that digital transformation of logistics doesn’t begin with technology. It begins with understanding your logistics system and its interactions with your supply chain.

Understand Your Supply Chain

“The very first step to digitization [is] understanding the current situation of your supply chain, identifying the risks that each supplier brings,” Breteau believes, “Once potential risks are attributed to suppliers (and their suppliers), the digital transformation journey can then begin by identifying the best solutions for bridging gaps and preventing complications.”

The most obvious place to start is to look at what’s causing you the most problems and consider:

• What are the most critical parts of the supply chain? How do things get from point A to point B? Are there better ways to get from point A to point B?
• What are the bottlenecks (existing and potential) for these critical parts and how might they be avoided or resolved?
• What factors are under direct control, and what factors can’t you control, but at least anticipate alternate strategies when they occur?
• Which problems need addressing in order of priority?
• What data can be collected to help better understand all of the above?

Key Digitization Tools

Logistics management looks at the following aspects of the supply chain:

• Types of transport (rail, ship, truck);
• Infrastructure (roads, railways, terminals);
• Warehousing and support services;
• Transport management (e.g., shipment tracking, cargo allocations, customer service levels, delivery expectations, maintenance and repairs);
• Transmission of information and documentation; Logistics data transformation looks at how to apply digitization to the above aspects of the supply chain in order to:
• Automate repetitive and routine tasks;
• Improve connectivity among operational components;
• Develop digital platforms that increase visibility of transportation and supply chain interactions and identify pain points;
• Deploy digital solutions that improve operational efficiencies, e.g., IoT, cloud computing, AI (Artificial Learning), Machine Learning (ML), Big Data analytics;
• Develop improved synergies between logistics services and other business, partners, suppliers and regulators;
• Ensure the integrity and confidentiality of information is protected (cybersecurity);
• Adopt a data-driven approach to make decisions and develop strategies.

Start Small, then Build Out

The first step in beginning any data transformation journey is to take “baby steps.” Focus on one identified problem. Develop a short-term (30-90 days) “proof of concept” trial to address the problem. At the end of the trial, you have enough information to develop a use case for:

• How initial assumptions were (or were not realized);
• Data collected, and lessons learned from the data;
• How best to deploy and manage the technologies, as well as how to integrate with the technologies both internally and with your partner network;
• A change management plan to invest relevant stakeholders in the goals and benefits of the technical solution;
• How to scale to other logistics, supply chain and business processes. The last is perhaps the most important. The use case becomes a solution blueprint. Which is not to say the blueprint remains static. The journey is far from over. It is just beginning.

Develop Analytics

Raw data collected from a use case is just that— raw. It isn’t yet actionable intelligence. How do you sort through all the collected data to identify what is relevant to improve your forecasts and transform your operations?

There are various data modeling tools to do this, as well as data scientists and data analysts trained in finding patterns and trends to uncover insights. These patterns might relate to such concerns as supplier quality, transportation management, product planning to scale back freight costs and minimize risk. Increasingly, AI and machine learning programs supplement if not totally automate many of these actions.

Look for New Uses

The one problem with characterizing this process as a journey is that it implies an end-point, a destination. But in actual practice, the data transformation journey is continual. You constantly look at the data and develop new benchmarks to improve your processes and efficiencies. And as you improve your processes and efficiencies, the data you generate leads to the discovery of what can lead to even further improvements.

Formulate a Strategy

Data underpins decisions about logistics operation. It leads to harmonization of individual digitization initiative and systems. And it can work seamlessly with little or even no human intervention. For example, a supplier notifies you a shipment can’t be delivered on time. This information is automatically entered into your planning and scheduling system so other elements of the supply chain depending on that shipment can make appropriate adjustments, or even contact an alternate supplier to possibly arrange a delivery on-time.

Which is not to say people are somehow left out of the loop. Every “digital decision” is fully transparent and accessible for human managers to review. And to learn from to form the basis of future strategies and continue successfully along the data transformation journey.

As Gaurav Mangla, CEO and co-founder of Pikkrr Plus fullfillment solutions, writes in Entrepreneur, “According to a 2020 McKinsey & Co. report, about 85 per cent of global supply chains faced a reduction in operations during the pandemic, and around six per cent shut down completely. This sudden shift led to unprecedented delays and operational challenges. Many logistics operators were already struggling to cope with the disruptions. There were no pre-defined strategies in place to deal with severe risks. However, digital transformation empowered logistics leaders to put customers first, be flexible and proactive in their operations, automate processes like never before, and prepare for the future of work by modernizing systems and infrastructures.”

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