There are many areas where real-time data analytics helps businesses increase revenue and operate more efficiently. Manufacturers, for instance, can use real-time supply chain analytics to reinvent their supply chain across sourcing, processing, and distribution of goods, so they can adjust to changing conditions quickly and effectively. Manufacturers can get the visibility they need at any moment, in time to deal with some of their hardest supply chain challenges more effectively – including demand volatility, supply shortages, manufacturing downtime and high warehouse labor costs.

Demand Volatility

Demand volatility happens when there are variations in demand for products in a rapidly changing and unpredictable market. Many factors contribute to demand volatility. Examples include the changing customer preferences and behavior, competitive business maneuvers, upstream supply fluctuations, and your own product and price adjustments.

But how can you effectively align supply with demand when demand is volatile? Forecasts based on what happened in the past are inherently inaccurate in this type of environment. Accessing insights from real-time customer behavior and streamed point-of-sale data can help you understand demand as it’s happening more meaningfully. When used effectively, these insights can provide opportunities to:

• Source new or reallocate existing production components.
• Adjust production levels, shortening lead times and cycles.
• Ensure adequate inventory is available in the right quantity, at the right place, at the right time.
• Create or refine promotions to increase customer demand.

Supply Shortages

We have seen how the COVID-19 pandemic has posed significant challenges for supply chain dynamics and the kinds of business disruptions that it brought on across industries. Beyond the pandemic, the war between Russia and Ukraine and geopolitical concerns in East Asia have led to manufacturers reassessing where their suppliers and manufacturing facilities are located. To counter supply chain shocks, businesses rely on data analytics to help them determine what events are happening in their supply chain.

Can your data analytics help you determine which raw goods, parts, components, and finished products in your supply chain are constrained? Would you be able to determine the reasons why? If so, you may be able to leverage opportunities to buy missing production inputs from an alternative supplier or to resolve a transportation bottleneck by using another shipper. These are just two illustrations of how your business can use data analytics to resolve such challenges. Manufacturers should base decisions not only on whether sales margins will still be positive after adjusting sourcing and transportation, but also on the potential negative impact on the customer experience that supply chain constraints can cause.

Manufacturing Downtime

Downtime in manufacturing and labor costs in warehouses rank among one of the top reasons why there are operational inefficiencies. The average manufacturer deals with 800 hours of downtime per year – or more than 15 hours per week. Downtime is costly. For an automotive manufacturer, they can lose up to $22,000 per minute of downtime. As an increasing number of manufacturers incorporate more Internet of Things (IoT) devices on their plant floors, they also have many opportunities to analyze data from them in real time using advanced analytics and machine learning techniques. Manufacturers would be able to identify and resolve potential problems with production-line equipment before they happen and spot bottlenecks and quality assurance issues faster.

High Warehouse Labor Costs

Labor is typically the largest cost component of a warehouse’s total operating cost. For many manufacturers, labor is almost half of their overall operational costs.

Do you have the latest insights into changing demand so that you can dynamically scale your warehouse workforce based on business needs? Traditional demand forecasting helps manufacturers understand demand and product movement on a weekly, monthly, and yearly basis. However, manufacturers need to move faster if they want maximum efficiency. For example, real-time insights into demand can lead to faster production scheduling adjustments to either avoid or shorten the time of stockouts and to reduce unnecessary labor costs such as overtime.

Real-time supply chain analytics is a must-have for proactive, ambitious companies – not a nice-to-have. For manufacturers, whose success relies on the efficiency of their sourcing, processing, and distribution – real-time supply chain analytics should be a part of their business best practices. The bottom line is that real-time supply chain analytics will help manufacturers increase their revenue targets and metrics for business success.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

Data and analytics are the heartbeat of any business looking to drive revenue and innovate – especially during volatile and uncertain times. Businesses have gradually transitioned away from traditional data centers in favor of cloud data storage and analytics solutions to access data and unlock business insights more easily. We expect more enterprises will transition to the cloud in 2023, as Gartner is projecting that enterprise cloud spending will be nearly $600 billion by next year. That number will continue to climb as data generation continues to explode.

However, for many businesses, relying on the cloud for their data and operational analytics needs is challenging. Distributed workforces located in hard-to-reach areas often suffer from latency due to slow Internet connections. Organizations that rely on real-time insights can’t afford to have a lag between the data that’s being generated and the subsequent analysis.

For maximum output, the flexibility and reliability of cloud services need to be met with the power of edge computing. Here we’ll look at the benefits of bridging cloud and edge for analytics, real-life examples of distributed use cases, and best practices for implementing edge technology.

Operational Analytics is Everywhere

Operational analytics refers to the real-time analysis of information on the internal functions and processes of a business. Examples include: how many orders, calls or service logs are being processed, how much revenue is generated at any given moment, or the current status of shipments or inventories. Industries of all kinds deploy operational analytics, ranging from retailers who use insights to target customers while they’re inside a store, and manufacturers who analyze IoT sensor data to identify and resolve potential problems with production-line equipment before they happen.

Data is being produced at an explosive rate and is growing increasingly complicated to process for analysis. This is particularly true for industries within which data collection is in areas with poor Internet connectivity. Offshore oil platforms are an example. These operations require intense analysis of things such as equipment status, GPS location data, current oil prices and more. Any delay in processing and transmitting data from the oil platform to its headquarters could result in poor maintenance of equipment, support teams troubleshooting issues too slowly, or a miscommunication about the cost of oil.

Edge Analytics Use Cases

To process data in a variety of environments, many businesses have shifted operations to the edge. Edge computing allows businesses to generate, collect, store, and process data locally without relying on Internet connectivity. Rather than worrying about being able to connect to a cloud service provider, users of edge computing can enjoy around-the-clock availability of data and systems, allowing for real-time analysis of operations.

Edge computing is becoming increasingly popular, with Gartner estimating that by 2025, more than 50% of enterprise-critical data will be created and processed outside the data center or cloud. This represents a huge opportunity for businesses to capitalize on edge and merge it with their current cloud architecture to ensure continuity with their analytical efforts.

Bridging the cloud to the edge for analytics also offers unprecedented speed in data processing. Without needing a connection, intelligent decisions can be made on the fly based on data that’s instantly generated and available. Additionally, edge analytics can operate on time series, spatial, and other IoT specific data more securely, without worrying about being targeted by external threat actors.

Another use case of analytics on the edge that’s not often discussed is emergency response management in buildings with smart sensors. Devices like fire and smoke sensors can communicate with a dashboard on the edge in the event of an emergency and can automatically trigger a call to a local emergency response team.

The use cases for cloud integration into the edge are numerous, requiring businesses to take careful steps through implementation to reap the benefits.

Bringing in the Edge

Businesses looking to integrate their cloud solutions into the edge for operational analysis must consider many factors. These include having a crystal-clear picture of where data resides, which data points are relevant, when the data expires, and which data needs to be aggregated. Also, constantly moving data to the edge can be costly and there is the potential for network bandwidth, storage and latency issues.

Building the bridge from cloud to edge for operational analytics can take a lot of work, but will up-level analysis of operations, increase security and provide better real-time insights. Take a deeper look into how Actian is modernizing edge application data processing to make data analytics a breeze here.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

Did you know that Vector is one of the world’s fastest analytics databases? We’re excited to announce that a recent Enterprise Strategy Group evaluation found that Vector can outperform its competitors by up to 7.9x.

Adding to this momentum is the release of Vector 6.3 in early December. Key highlights of the release include making administration easier, enhancing engine automation, and improving programmer productivity. We’re sharing six new data analytics features and benefits that will improve your data analytics journey.

Automatic Diagnostic Log Rotation Ensures Reliable Archives

Since a log file for the Vector X100 analytics engine tends to grow very large over time, log rotation is useful to archive a current log file and open a new one. Previously a manual process, Vector 6.3 introduces automatic diagnostic log rotation based on either the log file’s maximum size or a custom time interval (e.g., every 30 days).

Query Result Caching – Spill to Disk Eliminates Waiting for Available Memory

Vector 6.3 further extends the query result cache with an option to spill cached results to disk when cache memory runs low. A job/workload does not have to wait for memory to free up before completion.

Vector disables spill to disk by default so that its overhead and workspace utilization doesn’t impact your setup after an upgrade. Once enabled, the management database can monitor spill to disk activity.

Smart Min-Max Indexing Improves Memory Management

A Vector database table can have up to a thousand columns. By default, Vector creates Min-Max indexes on all columns that require large amounts of memory when many tables or tables with lots of columns are created.  Min-max reduces the number of columns inspected through new auto-tune functionality that determines scores for index and non-index columns. Based on these scores, Vector decides which columns it should add to the min-max index and which ones it should drop. As a result, this reduces memory consumption without negatively impacting query performance.

Shareable DBA User Defined Functions (UDFs) Increase Developer Productivity

Vector supports the creation of UDFs to use in queries to extend database functionality. With release 6.3, users can now share and reuse UDFs created by DBAs through user groups and authentication, enhancing collaboration and self-service.

Exception Handling for Database Procedures Provides Greater Control

Improved exception handling for database procedures is now available for managing unwanted and unexpected events due to run-time errors caused by faulty design, hardware failure and code issues.

Pattern Matching Makes String Manipulation Easier

Users can now run pattern matching queries with SIMILAR TO on Vector. Vector can determine character string similarity based on character repetition, limiting character sets, character properties such as letter, hex, case, punctuation mark, and grouped characters.

Navigate Your Analytics Journey Better With Vector

Visit our website to learn more about Vector’s extensive performance optimization, features, and use cases for data analytics.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.