Improved business agility is when large organisations make faster, more accurate, data driven decisions and improve the speed with which these decisions are implemented.
Some examples of how IoT and Advanced Analytics will promote business agility are:
1. The collection of data at source. Data sources include:
a. Sensors on the factory or mine floor, on vehicles, on building facilities.
b. Data collected from relevant images and videos. Think of farmers that use image data collected by satellites and drones to analyse the health and progress of crops. Images and video of traffic patterns used by traffic management organisations. Crime prevention relying on video to determine criminal behaviour patterns instead of physical attributes of criminals.
c. Operational management systems such as ERP.
d. Operational management systems of suppliers, logistics providers and customers
e. Relevant information about the market and general business environment sourced from various data and information providers on the Internet.
2. Making fast operational decisions close to source using edge analytics.
An example of this is:
a. A sensor indicates excessive temperature.
b. The edge device or process contains built in logic that uses a pre-defined set of rules to determine that the temperature falls outside of acceptable parameters. These pre-defined rules are constantly updated using machine learning.
c. The edge device makes an automated decision to shut the machine down to prevent damage.
d. The edge device gateway communicates directly with the machine controller and instructs it to switch off.
Another example:
a. Social media or information from retail outlets indicate a run on a specific stock item.
b. An algorithm and process could identify this run and divert stock from planned destinations to the “hot” destination, thereby maximising customer satisfaction and company profits. Nobody wants excessive stock in one region and under stock in a region where demand has spiked in contradiction to forecasts.
3. Sending the data to a central data store.
4. Using stream analytics and machine learning to analyse data, determine trends and develop actionable information.
5. Deliver the actionable information in the form of dashboards or alerts.
6. Integrate back to the line of business or ERP systems to implement automated decisions based on pre-defined business rules. These pre-defined rules are constantly updated using machine learning. An example of this when a Job Card is automatically created for a repair that was highlighted by the predictive maintenance program.