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Supply chains generate large amounts of data – data you have to turn into real insights to improve the performance of the supply chain. Data management plays an instrumental role in resolving supply chain pain points at strategic and operational levels. Even so, few companies have been able to streamline their data and use it to transform and define their supply chains.
Poor data management translates to inept data analysis. Data is nuanced and subtle. It requires a structured process to fully explore, evaluate and capture any opportunities it presents. In this piece we’ll explore the challenges restricting the full impact of data management in the supply chain, as well as the areas that benefit most from enhanced data management.
Data is the oil of the fourth industrial revolution. As a result, companies invested heavily in intricate analytic technology in order to effectively interpret and understand their data. Surprisingly enough, investing in machine learning, predictive modeling, and other advanced analytics systems has added to their supply chain problems.
Companies have focused more on data management infrastructure than on the data itself. The right data provides the right results. It’s just as important to structure and cleanse the data as it is to analyze it. Clean, organized, and accurate data will provide the insights needed to make better decisions. Building analytics on bad data renders the most sophisticated infrastructure susceptible.
The other challenge is that organizations lack a common data language. Different departments and functions produce different types of data. Bringing together and interpreting such data is a complex process that can trip even the most experienced of data analysts. At the same time, most data languages are imprecise. It’s quite the challenge to navigate departmental silos. There’s a need for a more integrated process – a common language if you will. One that will underpin all telecommunications among departments, companies, and across entire industries.
With 19% of supply chain leaders looking to leverage machine learning, according to a survey by Logility and APICS, the time has come to escape limited legacy systems and processes. There’s a growing need to respond to customer mandates faster. Doing so requires more accurate data to optimize inventory and fulfillment initiatives. The big data revolution is unavoidable.
Only through the innovative use of big data tools can you better understand the dynamics of your supply chains and uncover new opportunities.
Supply chain optimization is all about making the best use of technology to improve the performance of supply networks. This begs the question: how do you create a high-performing supply chain that is both profitable and sustainable? There are three supply chain management areas that benefit most from enhanced data management.
To increase profitability, you have to capitalize on opportunities as soon as they present themselves. Data analysis is the key to keeping up with supply and demand. You can factor in risks and unusual circumstances. Nowadays, companies must increasingly act on global terms. And this brings about a host of risks that must be accounted for.
For instance, few companies were prepared for a crisis such as the coronavirus pandemic. As a result, global supply chains came to an abrupt halt with the onset of lockdowns. Data management and analysis can help supply chain managers determine optimal inventory levels and optimize their critical resources.
It’s a fast-paced world. To keep up with customer demands and maintain a competitive edge, companies have to ensure efficient order fulfillment and traceability. Data management allows you to gather accurate product information, which improves traceability and streamlines distribution.
In many ways, Amazon has changed the game in this respect with its incredibly short delivery times. With effective data management, any business, no matter the industry, can offer similar experiences for its customers and clients.
Data is the new currency. By generating value from data, companies can optimize their processes. The end result being more cost savings and better-satisfied customers. After all, even the most accurate data is meaningless unless leveraged in decision-making. On this note, data management provides transparency and visibility into supply chain processes and operations, which can help eliminate hidden costs and meaningless business activities.
Supply chains are highly complex. From sourcing and manufacturing to warehousing and distribution, the amount of data generated is nothing short of chaotic. It’s important to break down and untangle the data if any useful information is to be derived from it. Effective data management allows you to integrate data from the various departments in which it resides and harmonize it.
As mentioned, only the right data can provide the right insights. Integrating data using a common language can help formulate strategies, streamline operations and ensure customer satisfaction.
Sometimes the challenge is not with the systems but with the data itself. Supply chains are complicated and often lack end-to-end visibility. And this ultimately inhibits your ability to meet customer needs. A well-designed data management process addresses such issues providing businesses the competitive edge and agility to be resilient even under trying circumstances.
Are you struggling with your data management? Contact us to learn more about our team of consultants, including subject matter experts in the area of data and analytics.