Many British manufacturers have put off spending until there is clarity on the terms of a Brexit deal, and there is concern that a slowdown could jeopardise UK jobs and external investment. However, breakthroughs in areas such as automation and big data analytics have given industry new weapons to rebuff these challenges and work more efficiently.
Artificial Intelligence (AI) and machine learning have gained significant momentum over the past few years, which has had implications for how manufacturers operate. In fact, recent research showed that 80% of enterprises already have some form of AI in their production processes.
For manufacturers, this technology is particularly important because they have the power to disrupt the way products are made, moved and sold. Rather than guessing which materials are most appropriate for various products, selecting a sub-par logistics provider or spending months researching which markets are most applicable for certain items, automated solutions can continually and accurately recognise trends and make data-driven decisions for you, all without human intervention.
Furthermore, AI and machine learning have the potential to
disrupt their entire supply chain, allowing industry to produce products at lower costs and avoid processing issues such as delayed deliveries. Additionally, they can reduce inventory levels by having access to more intelligent planning systems that update according to real-time warehouse status or
current market needs.
However, it’s easy to forget that automated systems are powerless without data fuelling them. Therefore, manufacturers should be wary of trying to jump directly into a strategy based on automation without first creating a solid data analytics foundation.
Dig deeper
Historically, manufacturers have worked with management consultants to improve their IT-driven processes and core operations such as purchasing, logistics, and production. However, this practice is often lengthy and expensive. They also typically rely on the existing operations teams to collect data and provide context, often resulting in significant delay to the organisation and processes being analysed.
This is more difficult in large manufacturers running a wide range of complex processes, as it is difficult for them to transform while running at full capacity. In these instances, one of the main challenges is how to gain transparency across the seemingly infinite volume processes taking place across the business.
To address this, organisations are starting to combine big data analytics capabilities with AI to look deeper into their core business processes. This category of big data analysis called ‘process mining’, uses automation and the digital footprint that organisations leave behind in their IT systems to automatically visualise their operational processes. If the manufacturing business is working with a consultant, the consultant can use this technology to refocus their core competencies on interpreting and prioritising analysis results.
The perfect partnership
Data analytics has allowed manufacturers to make more informed decisions by utilising the swathes of information collected to uncover hidden patterns, correlations and customer preferences. However, it has limitations. While traditional analytics software can provide some insight, it has always required a hypothesis from users, to shine a light to see where things work well or poorly. Without these questions, the benefits of analytics are limited.
Given the huge number of different departments, individuals and moving parts that make up a manufacturing organisation, business process oversights can occur daily and can sometimes remain undetected for months, or even years. For example, delivery lead times must be maintained on an individual material code basis for every location, so the production planning team knows what time to expect each material to be replenished.
If the planned lead time for any material is too short, it easily results in costly ‘out of stock’ situations. These cross-departmental inefficiencies can seriously harm manufacturing operations by slowing organisational throughput, impacting working capital, extending customer response times and creating team performance bottlenecks.
Process mining technology provides manufacturers with complete transparency into how their processes are working in real-time, enabling them to pinpoint and remove inefficiencies. The role of automation takes this further, meaning that loops or blockages can be identified and immediately updated.
Giving corporate data a full ‘body’ scan in this way using AI-powered analytics can solve problems manufacturers did not even know they had, reducing inventory costs, identifying production bottlenecks, improving delivery punctuality, and optimising logistics between production sites, distribution centres and end-users.
For manufacturers to spur greater innovation, they must understand a couple of key factors. Firstly, the value of complete transparency, to uncover more data than human workers could collect manually. Understanding processes at this granular level acts as a vital foundation for the automation of business processes.
Furthermore, understanding that data is like the oil that robots operate on is essential, as it is the only input that automated systems must factor into decisions and actions. Those that learn how to combine machine learning and AI with process mining will be best placed to harness this new era of data analytics and ensure more efficient operations along their supply chain.
Author profile:
Bastian Nominacher is co-founder of process mining company, Celonis.
