Supply Chains: Five Steps To Optimizing Them With Data Analytics

The supply chain is facing major challenges in the pandemic. Companies can optimize their supply chains in five steps by analyzing unstructured data.

Even before the challenges posed by the pandemic, the number of moving parts in supply chain management made it a complicated matter. Those involved at all levels bear the enormous responsibility of minimizing costs and optimizing inventory. At the same time, the quality, availability, and successful delivery of products must be ensured. Companies will face new problems in the future, such as inventory shortages and fluctuating customer demand due to the constant interruptions caused by temporary downtime.

Pure Storage shows five steps in how modern data analytics can help companies solve or cushion problems in supply chains. Companies looking for new ways to optimize their supply chain networks can gain insight into rapidly changing operations by incorporating advanced unstructured data analytics and other innovations such as the Internet of Things (IoT) and IoT edge computing. And win demand trends.

Improved Supply Chain Management Through Analytics

A lack of end-to-end transparency in the entire supply chain leads to delays, unnecessary risks, interruptions, and increased costs. However, it is not always easy to gain transparency. Many companies struggle to make good use of the vast amounts of data scattered across the entire supply chain network.

Supply chain analysis is the analysis of aggregated data from various sources. These include application systems for procurement, inventory, order and warehouse management, order processing, and transport management (including shipping). Part of the problem is that most of this data is unstructured. That makes it more difficult to organize, analyze, and correlate than structured data. However, this data often contains precisely the knowledge that can lead to today’s ground breaking innovations.

Unstructured data can be spread across various systems, applications, and processes that do not communicate with each other. They come in various forms, both as files and as objects. This could be a product image, handwritten text, an audio recording of a customer conversation, or raw sensor data from thousands of points across a company’s ecosystem. Today, technology is helping companies unlock the hidden value of their vast amounts of unstructured data. All that is required is the right Storage and management platform and advanced tools like artificial intelligence and machine learning.

Analysis Of Supply Chains Provides An Overview Of Logistics Networks

Well worth the effort. Analyzing the aggregated data from these various sources in the supply chain can give companies a comprehensive overview of their logistics networks. She can help them understand and resolve the impact of supply chain problems at all levels of the network.

Advanced methods such as predictive analysis can help accurately predict future trends, forecast stocks, and manage resources. On the other hand, the prescriptive analysis answers the “what-if” questions that can help make strategic decisions for supply chain planning. Companies can ask strategic, analytical questions with more accurate data in hand. These will help them develop the right strategies to optimize your supply chain network.

The following shows five ways data analyses can be used to optimize supply chains.

Extended Demand Forecast For Supply Chains

Efficient inventory management is critical to optimizing the supply chain. Overstocking or delivery bottlenecks in popular products can be a nightmare for suppliers. The inability to get a customer what they want can lead to lost revenue as customers migrate to other vendors. Analyzes used in inventory management for an extended demand forecast can increase the efficiency of various processes.

In a survey by Logility, 36 percent of supply chain experts said optimizing inventory management to balance supply and demand is the number one reason they adopt analytics. Real-time analysis of inventory, retail sales, and movement of goods data provides actionable insights that can help companies plan product promotions, determine price changes, and optimize inbound and outbound deliveries.

With real-time insights into inventory operations, companies can optimize budgets and predict how much inventory they need to hold. You can improve inventory management with automatic inventory counts and deliver inventory based on customer preferences and location to reduce distribution costs. When businesses strike the right balance between supply and demand, they can promptly deliver services and products to customers with less lead time.

Predictive Maintenance

By analyzing data from devices and systems integrated with IoT, artificial intelligence, and machine learning, companies can perform predictive maintenance to avoid downtime and improve safety in the manufacturing process. Predictive maintenance is a technique that analyzes historical and real-time data to identify potential plant failures and optimize preventive maintenance plans. With a predictive maintenance strategy, the systems are only serviced when necessary. This avoids unnecessary maintenance work and the associated spare parts and accessories costs. In addition, downtimes due to maintenance work are reduced, and unplanned maintenance work is avoided,

Increased Storage Efficiency

An efficient warehouse workflow is vital to ensure that inventory is in place to meet demand in the shortest possible time. Analyses in the warehousing phase of the supply chain can help optimize and streamline warehouse operations. More than two-thirds of companies state that they do not have complete transparency about their supply chains. However, real-time analytical insights into warehouse operations give them valuable insight into the movement and condition of goods, show them how to best use the storage space, and warn of factors that could lead to disruptions.

Sometimes goods need to be stored in a certain way, for example, due to temperature, fragility, or weight. Unstructured data from IoT sensors and cameras used in the packaging process can ensure the proper Storage of perishable goods. Analyzing this unstructured data can help companies use their available storage capacity more efficiently and better manage their resources, including equipment, vehicles, and employees.

Supply Chains: Real-Time Transport Tracking

Supply chain and logistics teams can leverage report analytics from their transportation management systems to make better-informed decisions. For example, data on traffic patterns on major roads, weather conditions, and fuel consumption can help companies optimize routes. In addition, the frequency of expired and damaged goods can be minimized and delivery times shortened. Companies can track cargo in real-time at all stages of the journey, providing information supporting decision-making and risk management. Companies can use this data to inform their customers of any delays in real-time about changed delivery estimates.

With this large amount of aggregated data, companies can identify and improve inefficient patterns, such as repeated scheduling problems or routes with constant traffic delays. In addition, analytics can help companies determine the best inbound and outbound shipping routes. For this purpose, existing routes, backhaul routes, shipping planning techniques, and other restrictions in connection with the transport are evaluated.

Supply Chains: Improving Sales And Profit Margins

Analytics can help with product placement, pricing, and promotions by providing insights into the balance of supply and demand across stores. Demographics, consumer behavior, and other trends and patterns also help companies shape their sales and marketing strategies. This enables them to retain customers and increase sales.

Point-of-sale data analysis also helps businesses forecast demand based on local, seasonal, and weather factors. With this data, retailers can understand their customers’ usage patterns and buying habits and prepare for spikes, set minimum inventory levels, reduce product recalls, and identify and cross-sell slow-moving inventory.

Powerful Analysis With Unified Fast File And Object Storage

Ultimately, analytics data from the entire supply chain can be an important resource for companies. But that’s only true if they use it to generate insights that drive innovation and streamline their supply chain. Modern storage solutions for consolidating faster file and object data are ideal for this purpose. A Unified Fast File and Object (UFFO) storage platform can help consolidate data silos and provide faster insights.

Regardless of the size or structure of the data companies work with, UFFO concepts deliver multi-dimensional performance on a large scale. At the same time, they offer massive savings in power consumption, capacity, and cooling costs compared to conventional storage solutions. It also gives companies the flexibility to adapt to different data types and store multiple data profiles in a single, scalable system. This makes it possible to react to fluctuations in the supply chain with the necessary knowledge.

Pure Storage offers a modern data experience that enables companies to operate seamlessly across multiple clouds as an automated storage-as-a-service model. Pure Storage helps users to use data. And at the same time, they can reduce the complexity and costs of managing the underlying infrastructure.