Network Solutions - Americas at Expeditors International

In the role of Director of Network Solutions, Melissa leads the Americas analytics services team, collaborates with customers, and helps them achieve better performance in their networks and freight flows by aligning these strategies to the enterprise. This is accomplished by understanding and diagnosing existing challenges and using advanced analytics and modeling techniques to deliver a clear, quantified, and configured path forward for each client.

Prior to joining Expeditors in 2010, Melissa worked for a leading consumer electronics company as the Director of Logistics and Transportation. Melissa has spent over 25 years in supply chain and logistics, developing diverse expertise and competencies in supply chain strategy, analytics and modeling, and financial analysis.

Since joining Expeditors in 2010, she has worked closely with many current and prospective customers across several industries, targeting supply chain improvement initiatives. Melissa holds an MBA and MS in Logistics from the University of Maryland and is a licensed US customs house broker based in Charleston.

1. Could you provide an overview of your role and responsibilities within the company?

As the Director, Network Solutions, I focus on working with business leaders, leveraging supply chain data to improve supply chain network performance, and empowering data-based decision-making. My team brings value to our customers through a data-led approach that improves the performance of their supply chains. It is my privilege to lead a team of project managers and engineers in the Americas who translate business objectives and data into design models that enable our customers to make data-based decisions about their nodes, networks, and product flows. I am intensely focused on developing the next generation of leaders for our company.

2. Industries across the globe continue to battle the Great Supply Chain Disruption. They’re striving to optimize execution, diminish risk, improve dexterity, and identify ways to gain a real competitive advantage. To achieve these goals, it will be essential to create data-driven networks, maximize the latest digital transformation capabilities, prioritize risk management and resilience, streamline logistics, and much more—your views on this.

While I agree with this statement and appreciate the complex nature of the challenge, learning is another important dimension. Not training or skills development, not individual knowledge; instead, I’m referring to the collective intelligence embedded throughout our supply chain ecosystem. I believe the leveraging this collective knowledge and the developments in applying machine learning for data analysis have the potential to make this possible.

Why? 

The fragility of global networks exposed during the Great Supply Chain Disruption reinforced a valuable lesson—the supply chain is not the sum of its parts but a dynamic ecosystem where the parties are interdependent and interconnected. When we acknowledge and believe that our success depends on all parties and capabilities in the ecosystem, it opens us to thinking, designing, and acting differently, ultimately creating a new playbook. Those who unlock and leverage the intelligence embedded in the ecosystem will create a competitive advantage. This advantage can be sustained with the situational awareness to navigate the changing landscape effectively.

“In a world with a changing landscape, competitive advantage will erode with static or fixed variables like master data, logistics parameters, or inventory levels. In this context, introducing additional variables to the data model expands the possible outcomes and insights”

Developing situational awareness is critical in this 24/7/365 world. The rapid expansion of supply chain risk management and environmental, social, and governance software solutions are focused on solving these needs. It’s a fascinating space that is rapidly evolving. Disruptions can create either chaos or opportunity; let’s choose opportunity and create favorable business consequences!

Awareness and learning rely on data that exist in many forms. Businesses create and use massive amounts of data through the normal course of operations. We need to uncover the story the data tells and create meaningful insights to solve these complex business challenges at operational, tactical, and strategic levels. Regardless of the focus area, the common thread here is analytics, and we need to elevate its role in logistics decision-making and strategy. To do this, disparate data sources need to be harmonized. Bits and bytes are transformed into human consumable forms that describe and diagnose what has happened through visual analytics. When this is done at scale, business leaders are empowered with fact-based insight. Today’s capabilities in advanced analytics, new technologies, and talent are a powerful combination for processing vast amounts of data from disparate and diverse sources. By deploying techniques to identify meaningful patterns and trends by separating the signal from the noise, actionable insights at scale can be achieved.

Automating the data analysis workflow shortens the time to value, an essential feature to sustaining situational awareness in a changing landscape. Add in learning, specifically machine learning algorithms, and the workflow gets faster, and with every iteration and exposure to new data, the insights become more reliable. As reliability increases, so does the value. Deeper and more holistic insights strengthen business decisions from network optimization that balance the competing priorities of efficiency, flexibility, risk mitigation, and others.

3.How will continuous logistics disruption drive the need for constant master data maintenance and refined logistics parameters and inventory levels?

In my line of work, I see companies of all sizes have master data disparity and duplication across the enterprise and their supply chain ecosystem. While it is a common occurrence, it creates waste. Static master data assumes a static operating environment, which is the antithesis of continuous disruption.

As reliable intelligence from the supply chain ecosystem is derived faster and decisions strengthened, over time, the incremental value of decisions may plateau without innovating. In a world with a changing landscape, competitive advantage will erode with static or fixed variables like master data, logistics parameters, or inventory levels. In this context, introducing additional variables to the data model expands the possible outcomes and insights.

What would be your piece of advice for your fellow peers and leaders?

Start your journey with a solid foundation with the basics before deploying new technologies and infrastructure.

• Find peers and leaders who are equally as passionate about the need.

• Map your value chain.

• Assess your baseline and identify dependencies.

• Map your data landscape and identify where data gaps exist in your value chain.

• Prioritize and act.

• Quality matters, always.