Data Analysis As A Differentiating Factor in Supply Chain Management
Nick Ostdick - June 13, 2017
Today’s integrated supply chain is based on data. Big Data, advanced analytics, S&OE reporting, and other forms of information are the vehicle which drives planners and managers to execute planning strategies and platforms for the best overall results both in productivity and profitability. But heaps of data without any real concrete plan for analysis or evaluation is essentially like having a detailed map without any real sense of where you need to go. Without any sense of direction or end destination, even the most advanced map will be of no use in helping you complete your journey.
As such, one of the key differentiators for manufacturing companies in today’s global manufacturing landscape is data analysis and how companies leverage insights from this analysis to drive more efficient planning and production and create stronger links between various parts of the production sequence. Especially for manufacturing companies in variant-rich fields like the automotive industry, data analysis not only gives companies a 360 degree view of their overall production and supply situation, but it also helps foster three of the most important factors in global supply chain management: visibility, agility, and flexibility.
But the problem with creating or structuring a supply network around data analysis and reporting 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 manufacturers function 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, how manufacturing companies can leverage data analysis as a differentiating factor in supply chain management.
Greater cross-organizational 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 manufacturing companies can identify potential quality control issue before they emerge and deploy strategies to combat an issues that may arise.
Negotiating complex supplier networks
As we discussed earlier, today’s global automotive supply chain is comprised of vast supplier networks that can be challenging for manufacturing companies 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 manufacturers to form complex supplier networks to satisfy customer orders for a rapidly-changing end product, but it allows manufacturing companies 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 holistically
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 manufacturing companies 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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