Manufacturers, Prepare for Supply Chain Disruptions

The unprecedented impact of COVID-19 has proved that to innovate past the post-COVID-19 world, businesses require an ever-increasing velocity and scale of analysis to succeed in the face of unforeseen market shifts. The National Association of Manufacturers released the findings of a survey of manufacturing leaders examining the economic and operational impacts of COVID-19.

The survey highlights that 53.1% of manufacturers anticipate a change in operations and 35.5% of manufacturers are facing supply chain disruptions as a result of COVID-19.

To respond to change, activities in a production process must be continuously monitored and adjusted as needed. including purchasing of raw materials and the handling of inventories to maintaining the quality standards of the goods produced. Decisions based on continuous analysis of such processes empower manufacturers to predict, prepare, and respond in a proactive and accelerated manner to changing market conditions, including the likes of this crisis and its aftermath.

For manufacturers to lead the way, continuous intelligence is paramount. Gartner predicts that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions. A recently released report by ABI Research also identified Continuous Intelligence as a top technology trend to watch in the year to come.

Powered by advanced analytics and artificial intelligence technologies, continuous intelligence solutions improve the speed, quality, and precision of real-time and near-real-time operational decisions because they ingest higher volume and variety of quality data into the decision making process.

Moreover, these systems are able to process high volumes of data – both from IoT and other disparate systems – quickly, shielding your workforce from overload. Also, as the complexity and number of manufacturing assets increase, these systems are able to apply optimization techniques to evaluate far more options than a person could consider in the same amount of time. When done right, these systems also ensure better safety and distribution of your workforce.

Why now?

Because it is possible to implement continuous intelligence at scale due to advances in device connectivity leading to an explosion of data from sensors, actuators, and other devices, processing capabilities in the cloud, and streaming technologies operating at scale. Further impetus is provided by the advent of 5G to the already-present physical layer technologies, with the promise of openness, better performance, stability, security, and implementation simplicity, for faster use of IoT data in continuous intelligence applications.

Effective continuous intelligence systems display these 4 characteristics:

  1. Continuous Monitoring: Continuous intelligence systems run all the time, listening to and analyzing events as they occur, and making recommendations that require responses by persons or systems.
  2. Continuous Improvement: It’s important to correlate business goals with recommendations to make sure the models are working correctly and if necessary, modify rules and analytics and re-run to get recommendations aligned with business outcomes.
  3. Human Validation: People should be able to validate the intelligence periodically so that actions can be taken when recommendations do not achieve the desired results.
  4. Common Understanding: Although each person may have a personalized view of recommendations specific to their role within the organization, continuous intelligence provides a common operating view of processes across the enterprise.

Continuous intelligence from Makoro™ makes asset performance recommendations to manufacturers so they can deal proactively with changes in market demands. Makoro™ continuously correlates and analyzes data from IoT devices, enterprise, and operational systems and makes simple, real-time, and contextual recommendations that can be directly used by your workforce.

Recommendations from Makoro™ relate to users’ job functions and past interactions and are delivered directly to where they can be acted upon.

Users interact with Makoro™ via simple Accept/Ignore actions, which feed back into Makoro™; Makoro™ learns from the user interactions and re-trains so the recommendations get better continuously.

We invite you to take the Ten-day Makoro™ Challenge and experience continuous intelligence by Makoro™.