How Data Analysis Can Drive Supply Chain Management

How Data Analysis Can Drive Supply Chain Management

How Data Analysis Can Drive Your Supply Chain.jpgThe globalization of today’s automotive supply chain has created at once great opportunities and challenges for OEMs, manufacturers, and suppliers. The automotive supply landscape is filled with complex networks of suppliers, partners, and other players working to create and move products from the production floor to the customer’s door. As we’ve discussed in previous entries, the name of the game in today’s automotive supply chain industry is cutting through this complexity in order to create more visibility, agility, and transparency for companies in successfully managing production and meeting delivery deadlines, and one of the ways planners and managers can achieve these invaluable insights into their overall supply situation is through the gathering, analysis, and deployment of data.

In such a variant-rich industry, a data-driven supply chain is critical in remaining efficient, productive, and competitive, especially on a global stage. But the problem with creating or structuring a data-driven supply is many planners and managers don’t truly understand —even in 2017 — how to evaluate, analyze, or act on the large volumes of data at their disposal. Moreover, many planners and managers still fail to realize just how valuable data is in leveraging lean supply chain principles.

So, how exactly can a data-driven supply chain concretely impact the way an OEM, manufacturer, or supplier functions in a global supply network? What are the benefits of creating a data-driven supply stream? How can a data-driven supply strategy be a disruptive force in how companies operate? With these questions in mind, let’s examine how exactly data can drive your supply chain to greater efficiency, productivity, and visibility.

Greater organizational-wide insight

On a macro level, the gathering, sharing, and coordination around data can provide greater context and insight organization-wide into the overall supply situation. This means more collaborators at each touch point of the value chain will have a greater understanding of demand and production benchmarks, objectives, goals, and outcomes, which will help break down functional silos, increase communication and collaboration, and lend itself to a more efficient supply network. It’s a large-scale avenue to view a data-driven supply chain, but it’s also an important value proposition as global supply networks continue to diversify and expand.

Enhanced quality of end-products

How does a data-driven supply chain increase the efficacy of the end-product? Data analysis provides planners and managers a detailed look at how quickly and at what volume products are moving, which can impact how planners and managers utilize demand planning, job allocation and scheduling, inventory management, and resource allocation strategies for optimal production programs. In addition, the development of Big Data and Industry 4.0 concepts means planners and managers can access data on the condition of products both during production programs and in-transit for delivery. Monitoring the condition of parts means OEMs can identify potential quality control issue before they emerge and deploy strategies to combat an issues that may arise.

Creating and navigating complex supplier networks

As we discussed earlier, today’s global automotive supply chain is comprised of vast supplier networks that can be challenging for OEMs to navigate, especially as the nature of automotive manufacturing continues to change with the emergence and proliferation of smart cars and autonomous vehicles. A data-driven supply chain not only allows OEMs to form complex supplier networks to satisfy customer orders for a rapidly-changing end product, but it allows OEMs to successfully navigate the networks in which they have to work. Whether we’re talking using data to strategize at the S&OE level or to best allocate production programs based on facility capacity or production floor restraints, a data-driven supply chain can function in a more responsive way to the challenges of varied supplier networks in which one partner is dependent upon another to ensure smooth production and on-time delivery.

Understanding supply chain management as a whole

Ask any planner or manager and they’ll tell you how the integration of today’s automotive supply chain has merged many aspects of once disparate functions into a more holistic operation. In today’s supply landscape, planning and forecasting impact production, inventory management can alter job scheduling or allocation, and container management strategies can influence how OEMs deploy their transportation or routing platforms. Because data analysis has the ability to connect the dots between these critical supply chain functions, planners can put data and reporting to work in one stage to have a direct impact on another. This removes barriers between different elements of the supply stream and provides greater insight and visibility into the supply situation as a whole.

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