Automating tasks can help manufacturers increase production speed, optimise operations and decrease customer complaint redressal time. Intelligent automation (IA), a combination of artificial intelligence, machine learning and process automation can help reduce labour costs, and optimise production processes.
How can AI-based automation help improve customer experience, efficiency and productivity in manufacturing operations and what are the best practices for implementing automation in the manufacturing sector?
A clear theme continues to emerge in our conversations with clients of all sizes across the manufacturing sector. They have started using Intelligent Automation (the combination of AI and automation technologies) predominantly to improve customer experience and drive operational efficiency. Manufacturers use AI to analyse sensor data and aid production facilities in determining the likelihood of future failures of machinery, allowing for preventive maintenance and repairs to be scheduled in advance. It also helps manufacturing companies to improve their safety ratings and enhance security efforts.
Intelligent automation helps in reducing routine or repetitive tasks which allows workers to focus on areas that are uniquely human – critical thinking, relationship building and deep innovation. Automating tasks can help manufacturers increase production speed, optimise operations and decrease customer complaint redressal time. Intelligent automation also enables better data analysis from production processes which helps faster decision-making by providing critical insights and identifying problems before they even occur.
When embarking on the journey towards intelligent automation, companies should first make intelligent automation a strategic priority. They must focus on clearly defining their business objectives for automation, find the right technology partner and align the budget for making effective investments.
What are some of the key challenges that the manufacturing sector is currently facing and how can automation help mitigate these challenges?
Automation presents various growth opportunities for manufacturers. However, to fully achieve these benefits, it is crucial for manufacturers to be aware of the challenges and develop strategies to mitigate them. Here are some of the key challenges and the solutions that automation brings to the manufacturing sector:
- Talent shortage: Companies are facing an acute shortage of the right people and skill sets to scale operations. However, automated systems can perform many tasks that were previously done by humans, thus enhancing human capabilities and allowing them to focus more time on strategic tasks.
- Data silos: Data is a critical component for growth when it can be harnessed to create insights, but it can prove to be a liability when it is not accessible, disconnected and untrusted. Automation can help mitigate data silos by integrating data from different systems and making it accessible across the organisation.
- Return on investment: Many companies start with what’s narrow and easy to automate, but do not focus on the big outcomes/ROI they seek and lack a holistic strategy and the skills to effectively automate at scale. By implementing automation in a strategic and targeted manner, manufacturers can achieve a positive ROI and improve the overall effectiveness of their operations.
- Cost reduction: Manufacturing costs are always a concern, and manufacturers must find ways to reduce costs without sacrificing quality. Automation can help reduce labour costs, and optimise production processes, all of which can help reduce costs.
How can automation help drive innovation and what are some of the key technologies that are driving transformation in the manufacturing sector?
Automation can drive innovation in the manufacturing sector by enabling faster experimentation, enhancing collaboration, enterprise visibility and time to market. In combination with technologies like hybrid cloud, AI, IoT, edge computing, 5G and digital twins, manufacturers can significantly accelerate their digital transformation and solve critical business challenges.
For example, a popular retail brand Carhartt often deals with unpredictable spikes in demand – particularly during peak holiday shopping season. During such a spike in demand, they uncovered issues between their website and multiple critical back-end systems, including inventory and customer loyalty systems. Using IBM Turbonomic, Carhartt was able to identify and proactively prevent these costly performance issues and avoid downtime during the busy holiday season, achieving record holiday sales. In addition to driving business value by improving its resource utilisation by 15 per cent and making its cloud environment 45 per cent more efficient, they were able to free up their IT teams to focus on innovation projects.
Consider another example of home appliance maker Electrolux. They needed to get a unified view of their complex IT environment to maintain operations and get ahead of issues. With IBM Cloud Pak for Watson AIOps, resolution times for IT issues were cut down to only one hour, which previously could take up to three weeks to resolve.
How can companies measure the impact of automation on their manufacturing operations
and ensure that their initiatives are aligned with the overall business strategy?
Process mining helps companies to measure and improve operational excellence with data driven process insights and thus measure the impact of automation on their manufacturing operations. Process mining helps businesses make faster, more informed decisions for process improvement through data-driven insights.
Gain complete process transparency using data from your business systems, such as ERP and CRM, pinpoint inefficiencies and prioritise automation by impact and expected ROI. Drive continuous process improvements by triggering corrective actions or generating RPA bots. Process mining also helps businesses to predict the impact and ROI of change initiatives and identify potential operational risks before making investments by performing what-if analysis.
What role does data play in enabling automation in the manufacturing sector?
The ability of organisations to transform their operations and business models is directly proportional to how well they collect, structure, utilise and govern the vast quantities of data they generate every second. Advanced data management capabilities are critical to effectively use technologies like automation to improve manufacturing processes. A data-driven business empowers its workforce access data quickly, process information faster, and take advantage of insights to improve decision making. However, the explosion of data in a modern enterprise often leads to complexity. Reducing complexity in data structures is driven by a standardised data architecture, an enterprise data-governance framework, central data repositories, and automation of data workloads. Manufacturers need to reduce the time needed to prepare, validate, and cleanse data. Implementing an enterprise data lake allows them to curate their existing data and apply it to decision making. Finally, they must leverage data visualisation/ exploration tools.
What are some of the emerging trends in automation in the manufacturing sector and how can companies stay ahead of these trends?
While enterprise-wide intelligent automation is a major trend, as industry 4.O continues to evolve, companies are witnessing other trends like increasing use of collaborative robots (essentially robots that can work safely alongside humans), proliferation of additive manufacturing and the Industrial Internet of Things (IIoT). To leverage these emerging trends, companies should look at investing in skilling their workforce in new technology areas and partnering with technology experts who can help plan and deploy solutions that will benefit them.