- Why is Predictive Analysis in IoT critical?
- All you need to know Predictive Analytics using Streaming IoT Data
- Examples of Why is Predictive Analytics in IoT
- Predictive Upkeep
- Automated Inventory Control
- Predictive Analytical Visits and Customer Appreciation
- Management of the Supply Chain through Predictive Analysis
- Insights from Predictive Analytics in Play: Case Study of CISCO Plan for Saving Energy
- Final Thoughts
- Topics in Predictive Analytics
When you hear the term “Internet of Things,” you may think of something hi-tech, like a smart TV or AI automated gadgets. Some individuals consider the logistics of their assets and how to integrate them with their operations effectively, which they acquire through predictive analysis.
Many sectors now depend on data to drive an analytics value chain that results in a competitive business advantage.
Predictive analytics is crucial for extracting value from real-time data from IoT devices.
Those with a background in transportation, on the other hand, have a unique advantage in data model exploration since more advanced predictive analytic methods may depend on valid real-world data. The final result is a more accurate evaluation of the value produced from activities.
Why is Predictive Analysis in IoT critical?
Predictive analytics is key to getting the most out of IoT. When you connect devices and add a few sensors or meters, you’ll soon produce more data than your team can manage. Even with alerts and other messages, your team may get overwhelmed if IoT data begins to flow quicker than you can process it. According to research in 2020, less than 1% of data produced today gets evaluated. Predictive analytics can help you filter the data and understand what’s coming in and how to manage it.
All you need to know Predictive Analytics using Streaming IoT Data
Data from the IoT is rapidly growing. Any IoT stream contains too much data to be analyzed in real-time by a person. Few apps even provide past IoT data in forms that consumers can use for most businesses that collect IoT data. Companies must, however, make use of IoT data in some way. Aside from that, IoT data is stored simply because it exists. IoT data is an expense, not an asset, without application and a purpose. Machine learning and predictive analytics may help solve the issue of having too much data to deal with. Traditional business intelligence simply displays previous events in the IoT data stream. Predictive analytics informs individuals and business systems what they should do right now.
Examples of Why is Predictive Analytics in IoT
Here are some examples of predictive analytics using streaming IoT data:
Maintenance and detecting when equipment or systems need to be worked on to avoid issues is perhaps the most practical touted application of IoT data for small-scale and large-scale businesses.
Suppose a component or sensor is out of its usual working range. In that case, a technician is assigned, just like an elevator mechanic arriving at an office building, to repair an elevator before it breaks down.
When applied to IoT data, predictive analytics takes it a step further by looking at the patterns of those same sensors to predict when they will go outside of average limits.
Automated Inventory Control
Suppliers may maintain track of their consumers by using IoT data from their goods. If the client gives the supplier access to how and when goods are used or sold, the supplier can monitor the use and anticipate restock using predictive analysis.
Customers will profit from the fact that they will be well-supplied. Suppliers keep their clients supplied proactively, which reduces the expense of maintaining and servicing them.
One can automatically predict when to send goods, lowers operational costs, and guarantees a more consistent supply through predictive analysis.
Predictive Analytical Visits and Customer Appreciation
Retailers would want to know who their salespeople are conversing with when they go into the shop. When a consumer is going to enter the shop or site, IoT data alerts the merchant or industrial supplier. The company may know when a client is approaching or entering the business by using geofencing in an app on a smartphone or customer device (imagine IoT location data within a product).
Predictive analytical algorithms then analyze the customer’s history and profile to recommend the best product for cross-selling or the most probable cause for their visit.
One thing to remember is to keep the anticipated data narrow. When you provide a client too much information to act on in front of them, it may lead to confusion and inactivity.
Management of the Supply Chain through Predictive Analysis
Because many products travel through the supply chain from producer to end-user, IoT enables various supply chain layers to collaborate. Sharing IoT data throughout the supply chain allows for improved data integration, which leads to better predictive analytical models.
Consider a cooperative arrangement, where IoT data about the final customer’s item usage is transferred to the supply chain. Up the supply chain, suppliers and vendors may combine data from many clients to make far more accurate predictions of future requirements. Those same upstream suppliers may share their IoT data with their customers to keep them informed about their orders, lead times, quality, and other potentially valuable data points.
IoT data can also help the downstream supply chain business’s predictive operations, revenue, and customer service changes.
Insights from Predictive Analytics in Play: Case Study of CISCO Plan for Saving Energy
Energy is a significant component in managing the cost of production in an effective supply chain.
Cisco’s supply chain team recently tested a tool to get insight into energy costs at a contract manufacturing facility in Malaysia. They installed a network of 1,500 sensors across the manufacturing floor and utilized energy predictive analytics software to collect data on energy use. This data provided them with incredible insight into the energy efficiency of specific equipment, systems, and manufacturing processes.
For example, energy consumption can vary dramatically, even for identical models. They replaced underperforming equipment and tweaked chamber operations for the best efficiency and energy usage. Consequently, the plant’s energy usage was reduced by 15 to 20%, saving $1 million per year.
The more data you have, and the longer your history, the more accurate your prediction. The predictive analytical system can tell you what’s likely to happen.
Many predictive analytics systems and algorithms are available now, focusing on a particular industry, application, or environment. These systems examine data as received, compare it to previous observations, link the effect of various factors such as the environment, weather, materials, or activities, and forecast patterns with statistical accuracy.
Topics in Predictive Analytics
- AI for Predictive Analytics
- Companies Using Predictive Analytics
- IoT and Predictive Analytics
- Predictive Analytics for Business Forecasting and Planning
- Predictive Analytics for Call Center
- Predictive Analytics for Sales Forecasting
- Predictive Analytics for Workforce Planning and People Management
- Predictive Analytics in Banking – Use Cases, Metrics, Examples and More
- Predictive Analytics in Health Insurance
- Predictive Analytics in Procurement
- Predictive Analytics Models
- Predictive Maintenance Analytics