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Global supply chains in the current business world still lack transparency. What’s more, business leaders often deal with prolonged delays in reporting cycles. There is no surprise considering the supply chain is usually spread across multiple manufacturing and logistics entities with numerous tiers of suppliers. Supply chain data is typically collected and stored in departmental silos. That is why supply chain optimization is often restricted from gaining complete and clear views on vital KPIs such as overall performance, spending, and cost savings. As a result, information about real-time data in the supply chain industry is often left unknown and reported only on an infrequent and unplanned basis. 

From smart sensors to cloud-based management systems, the real-time supply chain delivers a constant flow of real-time data. This accesses greater visibility, agility, and in-the-moment appropriate decision-making via the latest technologies like artificial intelligence (AI), machine learning, data analysis, and predictive analytics tools. 

In this blog, we will explore the latest technologies that are helping supply chain businesses to thrive in this contemporary marketplace with real-time data delivery. But first, let’s explore the real-time data: 

What Is Real-Time Data? 

When the data is accessible immediately after it is generated and stored, it is considered real-time data, which is why it is classified as the core of real-time decision-making. 

Accumulating vast amounts of real-time data and extracting insights has become paramount in today’s marketplace, making it invaluable for businesses. 

Real-time data enables businesses to avoid unforeseen situations like understocked or overstocked inventory, thus enabling them to be more proactive than reactive. 

Ensure greater visibility within your supply chain management when you tap into real-time data from these digital applications, systems, and processes: 

  • Cloud-Based Solutions 

Previously, businesses had to employ system administrators at the local network level to handle data storage networks by applying a cloud-based supply chain solution. Storage is distributed across users, spreading the cost across locations and creating more effective processing that can prevent costly downtime and data loss. 

This comprehensive connectivity also provides better upfront planning. As companies have more immediate access to data to power their decisions and scale more effectively, new locations can be incorporated into this system instead of building from scratch. 

With effective and robust data, businesses can efficiently manage shipping load, select the lowest cost shipping options, and improvise routes with multimodal stops — everything that directly improves your supply chain efficiency and reduces costs. 

  • Smart Sensors 

The cloud is not just connecting the links in the supply chain data. Smart sensors connected to particular machines at critical points along the supply chain can deliver real-time data-gathering on site and at an individual item level compared to previous methods that required decision-makers to go through extensive processes and reporting. These sensors can then feed the information to the cloud, enabling instant monitoring and operations coordination, insight into service logistics, and maintenance. 

Across the supply chain industry, when decision-makers are equipped with real-time reports, they can preemptively deal with bottlenecks or breaks in the chain instead of belatedly reacting to delays. 

  • Automated Processing 

Thanks to technologies like Internet of Things (IoT)backed machinery and AI-powered robotics in the supply chain industry’s processing, big data is abundant. In fact, the new difficulties lie in harnessing the constant stream of real-time data to guide automated processing and in-the-moment decision-making. Thanks to predictive analytics, this doesn’t have to stop with the sea of red flags for human management to solve. 

Instead of depending on traditional processes to respond to alerts raised by the system when someone goes out of programmed bounds (e.g., late deliveries), AI-backed automated processing can apply predictive analytics to estimate what will happen in the future. This shifts supply chain management from reacting to data, even if it is real-time, to responding to and planning for a set of future outcomes. 

  • AI-Powered Demand Planning 

Evaluating precision-based demand has always been integral to supply chain management. Organizations have previously suffered from overspending by purchasing excessive inventory, thereby putting extra pressure on costs and storage. Efficient demand planning software powered by artificial intelligence can provide accurate reporting on your future demand with real-time analytics, adapting consistently to new information and trends, allowing you to run a leaner supply chain without missing out on substantial selling opportunities. 

Businesses are shifting toward machine learning to help adapt and update forecasting in real time, hoping this efficient, data-driven approach will reveal new ways to reduce costs in operations and improve revenue. Applying artificial intelligence to your business can improve risk management and resolution of possible future supply chain disruptions from days to minutes. 

Utilize Practical Solutions with TransImpact 

TransImpact’s supply chain optimization enables you to access end-to-end supply chain data visibility. Our brilliant state-of-the-art solution provides accurate reporting based on your historical data, so you can make better strategic decisions, reduce costs, and increase your profits. Talk to our experts or book a demo to learn more about our solutions.

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