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3 Steps toward a Data-Driven Logistics Strategy in Manufacturing

Often, we hear or read that data is playing an increasingly important role in the manufacturing industry. Think of IoT, predictive maintenance, or robotics. But the truth is, many manufacturers still struggle to aggregate, analyze, and leverage data, particularly in logistics, where data is critical to meeting customers’ high demands for a fast, personalized product delivery experience.
The following three steps can help manufacturers leverage data to meet customer expectations today and tomorrow.

Step 1: Eliminate Silos and Synchronize Data

The first step in a data-driven strategy is not actually about the data. It’s about the organization. Today’s shipping environment is complex. Supply chains stretch globally, and growing organizations with different entities or partners worldwide may work with different systems. Moreover, different departments within an organization may have their own systems and “truth,” and the KPIs they pursue might not serve larger organizational goals.

To tackle today’s challenges and prepare operations for tomorrow, departments need to align and work together. This requires companies to take a critical look at their organization and its data strategy. As long as data is fragmented and isolated per department in different systems, the data has limited potential. For example, how can management make strategic decisions about shipping when the logistics department works with different data than the procurement department? But when logistics data and procurement data is synchronized, usually through systems integration with the organization’s system of record, a single source of truth is created and departments across the organization can now make strategic decisions based on real-time data, fueling supply chain efficiencies and increasing transportation savings.

$6 million

Companies can save up to 6 million dollars annually thanks to analyses of track and trace data by machine learning.

Source: Boston Consultancy Group, 2019

Defect tracking, forecasting, and track & trace are good examples of how data alignment can enable manufacturing companies to optimize their supply chain. For example, has a natural disaster jeopardized a delivery of raw materials? In a data-driven supply chain, where systems are integrated to create a supply chain “ecosystem,” the purchasing department is automatically informed of the inbound shipping event and can quickly assess the available amount, quality, purchase price, and shipping cost from their supply channels to backfill the raw material order and get the finished product to the customer. Or maybe machine data indicates a production unit is going to fail. In this case, the production system automatically sends a message to a service team and a 3D print facility near the factory produces and delivers the parts prior to breakdown. Connecting all systems and processes provides continuous insight into performance, fosters collaboration, and aligns departments.

It’s worth nothing that with e-commerce becoming more prominent in manufacturing, a Transportation Management System (TMS) for parcel shipping is a valuable link in a connected, data-driven supply chain. The right TMS for parcel shipping will seamlessly integrate with other supply chain systems, including ERP, WMS, and OMS, bringing data together to reduce the risk of human error in order fulfillment and enabling easy carrier performance monitoring. This not only improves customer service, but with carrier performance data in hand, manufacturers are better positioned to optimize their carrier negotiations.

Step 2: Deploy the Correct Data Analysis Tools

Once the data has been brought together, it is time for the next step: analysis. The reality is, there are innumerable factors that influence supply chain performance. The challenge for most companies is that they lack an easy way to anticipate and respond to these factors. But when the systems are connected and the data is readily available, the real work can begin.

For example, by combining data from a TMS for parcel shipping, the ERP system, and the CRM system, manufacturers have full visibility into their inbound and outbound shipments across all carriers, warehouses, DCs, and factories. And with Business Intelligence, manufacturers can analyze data such as shipping expense by geography, carrier, customer, and SKU, giving manufacturers actionable information for optimizing their shipping strategy – minimizing costs and maximizing customer satisfaction.

Step 3: Keep Learning

Aligning and analyzing data is only the beginning of a data-driven supply chain strategy. Ultimately, manufacturers should learn and improve through a continuous feedback loop. In a continuous learning environment manufacturers will have the right structure, the necessary agility, the ability to solve problems, and the ability to know which problems to solve¬–making it easy to quickly overcome the supply chain hurdles that come their way and making it hard for competitors to keep up.

Identify Opportunities to Improve

Manufacturers seeking to optimize their market value, adapt their products and delivery methods to customer requirements, and fortify their operations against unforeseen disruptions need to be flexible. To achieve this flexibility, the organization, its processes, and its systems need to be connected, creating a continuous feedback loop of shared data. To learn more about how you can create a data-driven supply chain, contact a Logistyx expert today.