Core digital transformation segments such as the Internet of Things is helping fuel the digitalization of production (Industry 4.0) with a potential of over USD 9 billion and connected vehicles at around USD 4 billion. There is no doubt over IoT having the capacity to change business models and value chains. Other than intelligent consumer goods, every business asset can be connected cost-effectively to grow value and productivity. This is driving more and more manufacturers to focus on the topic to gain a competitive edge.

In the current scenario, most of the processes ingest data manually and subsequently perform digital transformation so that the data is used for analysis and relevant operations. Data ingestion is the foundation of all analysis and related actions and hence is extremely critical. Technology enablers such as Bar Codes, QR Codes, RFIDs, NFCs, etc. can be leveraged to improve productivity as well as the reliability of various processes.

Automating data ingestion will eliminate the following downsides of manual data collection and logging:

  • Reliability of data: Automating data ingestion will eradicate the risk of human error. Since the data capturing is critical to all analyses and further actions, we cannot afford to have errors.
  • Rework: Any error would result in either wrong studies and actions or rework on the data capture. Automation of the same will help us avoid spending time and money on these aspects.
  • Time to collect data: Greater productivity can be achieved if the data collection process from systems can be automated. We can automate the mechanical process of data capturing and logging and utilize the same workforce in relatively more productive functions.
  • Data format: Automated data capture will ensure having the data in the desirable formats that can be used by downstream processes at ease.
  • Time to act: Due to delay in time to collect data in the relevant digital format, the time to act on any deviation is mainly delayed where data is collected manually. By the time we get the insights, the damage is already done. Automating data collection will avoid these delays and timely actions can be ensured.

Leveraging technology for end-to-end processes from data capturing, logging, and processing to storing, analysis, and notifications would drastically reduce the time to act on reliable data in case of deviations. This will help organizations detect deviations at the earliest, notifying and escalating it to the relevant authorities for timely actions.

A case in point is the pharmaceutical industry where typically sales personnel send sales data at the end of the week through a comma-separated values file and the relevant authorities take relevant actions at the end of the week based on analysis of the data. The productivity of the whole process can be improved by automating the data capture at the distributor level through a QR Code or Bar Code reader that can read the QR Codes for all boxes or strips of medicines. The process would enable a continuous flow of data and would establish a process-oriented notification system that will encourage the authorities to trigger corrective actions in a timely manner instead of waiting for the whole week. Also, an unproductive piece of the process such as manual data ingestion can be eliminated, and the same workforce can be utilized for more productive functions such as for increasing the reach of the product in the market.

Similar productivity and reliability issues can be fixed through technology enablers in cold supply chains to ensure that when the products reach the distributors, they are in the best conditions as per product specifications. This can be achieved through real-time monitoring of the cold containers. In this case, the most critical piece may be the connectivity piece, especially for inter-continental tracking.

Another case in point is the automated check-in processes at airports that automate the printing of boarding passes, scanning of baggage, checking weight of baggage, and printing of baggage slips. These automations have allowed the rise of a new trend of a mix of self-service and automation model and have improved workforce productivity. From a technology perspective, such solutions employ a mix of sensor data ingestion, machine vision, machine learning, speedy edge computation, connectivity, triggering of notifications, etc.

Sasken’s Industrial and Digital offerings help organizations improve productivity and reliability of their processes through technology enablers. We enable real-time tracking of assets, ingestion of tracking data, early fault detection, drawing relevant insights and acting on insights. The business outcomes of the end-to-end automation of processes are reduction in production lead times, improvement in the shelf life of products, a reduction in wait periods, etc. We are helping clients realize the potential of IoT by delivering greater value through enhanced productivity and improved reliability of processes.

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