The manufacturing and industrial sector have seen a fair share of disruption even before the whole world of work got upended by the COVID-19 pandemic. Changing customer preferences, dynamic market forces, increasing competitiveness, and continuous disruption have been increasing the pace of change.
These factors have compelled those operating in this space to look at digital transformation to drive resilience, agility, and velocity to business while increasing competitive abilities.
Automation has been an integral part of all digital transformation initiatives. Automation promises to drive productivity, increase efficiencies, reduce effort, and optimize costs. But at the core of automation is the promise to reduce human effort. But sometimes automation strategies end up increasing rather than reducing human effort.
Here is why that happens.
- Tool-first and not platform-first
Most vendors in the automation space aggressively market their tools to organizations. The message seems to be clear – the more tools you have and the greater the number of bots you use, the higher the chance of automation success. This tools-first approach might work in the beginning. However, as the enterprise digitally transforms or matures, it can lead to greater chaos.
One of the main reasons for the failure of an automation strategy is a tool-first approach. Increasing the number of tools only increases the amount of effort. Humans then need to learn different technologies, manage multiple tools, and gather insights from several places to deliver their work. The lack of proper integration further complicates the process.
Organizations need to adopt a platform-first approach to augment automation outcomes, reduce load and drive greater automation adoption.
Effort-intensive and high learning curve
Often the platform and tools’ complexity play a role in sapping productivity. Tools and platforms that have a steep learning curve inhibit adoption and advocacy. It is imperative to ensure that such systems are easy to implement and use to ensure that automation contributes to effort reduction.
Automation initiatives are supposed to reduce effort. The right automation strategy will consider the effort that goes into automation efforts and will first identify the right candidates for automation.
Automation and transformation teams must analyze the processes to identify traits for processes that are well-suited for automation and then design a solution that measurably reduces effort. ‘
Disconnected processes and departmental silos
Automation systems that are not integrated do not deliver the productivity and effort advantage. Automation systems work well and capably reduce the effort when the data inputs and outputs happen seamlessly. If the automation implementation happens in silos, then the data inputs are not complete, and the data does not work capably to deliver greater value.
Avoidable human effort is then needed to collate and deliver this data to the system.
Automation strategies must be deeply aligned with value creation. As such, organizations must build interconnectedness between departments and processes. They also need to assess each step of the value stream and identify ways to remove silos and connect the right processes to create a seamless system that can operate autonomously.
Task automation instead of process automation
Most organizations jump to automate tasks to accelerate automation efforts and drive efficiencies and cost-effectiveness. Gradually, the number of automation fronts multiply willy-nilly within the organization creating a big spaghetti bowl of automated tasks.
The hierarchy of any strategic initiative implementation focus on the overall value that can be created rather than focus on tasks.
While task automation is simple, it can become complex as the volume of automation increases because there’s an almost unlimited number of tasks within each organization. Then moving toward end-to-end process automation becomes more effort-intensive and expensive since the organization has already invested time and efforts to automate tasks.
An automation strategy thus must identify all the candidates for end-to-end automation to truly reduce human effort. Thought must be given to prevent disruption of core functions and increase adoption by simplifying tasks. Automation adds complexity by looking at tasks in isolation and then ends up as effort-intensive exercises since they fail to deliver tangible value.
Facilities management in factories, for example, can look holistically at automation. Automation can be extended to drive perimeter security. It can be used for unattended object identification, bar code scanning, managing alignment of heavy machinery, etc. It can be employed to boost the overall security posture of the facility.
Automation strategies thus must look at the process as a whole and not just the number of tasks and only then can they reduce effort.
Remaining stuck with traditional automation
There is immense progress and evolution taking place in the field of automation. Technologies such as AI are hard at work making automation smarter using elements like computer vision, NLP, etc.
Traditional automated processes require a certain amount of human effort. While billing systems are automated, for example, the data input is done manually. While one part of the process is efficient, the other part remains effort-intensive and error-prone.
On the shop floor, for example, machine maintenance is automated, but machine alignment remains a manual process. This part of the process can be easily automated using cognitive technologies such as computer vision.
Industry 4.0 has propelled the adoption of automation in manufacturing and factory spaces. As traditional business models get replaced and digitization increases, building a strategic automation roadmap becomes essential to deliver great outcomes, drive efficiencies and enhance profitability. Evaluating the ease of use, the complexity of implementation and the learning curve become crucial parts of an automation strategy as these ensure that human effort reduces and does not increase after the implementation.
For automation to truly deliver value, organizations need to define the objectives of automation, align them to the business goals, identify what to measure or improve, and assess ways to use new technologies such as computer vision to augment automation outcomes. Let’s chat about how to make automation truly an effort saver!
