Key points
In the current manufacturing climate, which is shrouded in economic uncertainty, it can be tempting to prioritise initiatives with immediate ROI over digital transformation, which typically takes several years to yield full returns. However, the lingering impact of COVID-19 has accelerated the need for digital-first strategies in manufacturing.
Consequently, manufacturers are dedicating substantial time and financial resources to fuel their digital transformation endeavors. According to McKinsey, due to these transformations, it's common to see machine downtime decrease by up to 50%, throughput increase by 10-30%, labor productivity improve by 15-30%, and forecasting accuracy by an impressive 85%.
However, while these investments demonstrate positive results for a select few, the majority are still struggling to expand pilot programs or harness the full potential of technology, hindering their ability to realise substantial returns. Let’s dive into what’s causing these pitfalls before reviewing some changes manufacturers can make to avoid them.
Automating Before Properly Analysing
Manufacturers often get distracted by the allure of technology, discussing specifications and pricing without considering what the core business objectives are. While automation, such as welding or robotics, may be the end goal, it's crucial to explore scalable steps that align technology adoption with overarching objectives.
Often, when companies embark on digital transformations merely as a theoretical exercise, they unintentionally establish isolated delivery teams that lack integration with business leaders, site operations, manufacturing excellence, and central IT. Alternatively, some become overly fixated on replicating the experience of a single site, overlooking the broader complexities within their network.
One approach to mitigate this issue is to consider designating a single leader to take ownership of the project, similar to how software companies utilise product managers. This leader will be responsible for managing various situations, establishing consensus to prioritise the project effectively, making critical decisions, and successfully guiding digital transformation to completion.
This avoids an unrelated team, such as marketing, procuring software without informing IT, utilising their functional budget, and testing corporate policies in the process. Ideally, a product manager should have a cross-functional skill set which allows them to coordinate across all of the relevant job functions, while having particular expertise in the technology to be adopted.
Starting Over and Fear of Failure
For manufacturers, it’s easy to fall into the trap of believing that starting from scratch is necessary for a successful digital transformation. In reality, existing infrastructure should be leveraged. Advancements in APIs and distributed cloud systems should help manufacturers to retain elements that are working well for specific departments while addressing technology gaps in others.
There are plenty of long-term employees and executives who may resist change due to the perceived risk and delayed ROI associated with digital transformation. To alleviate these concerns, you should enable employees to contribute content to improve digital systems and use key performance indicators (KPIs) to track and demonstrate progress.
By emphasising iterative change and foundational system investments, companies can gradually build ROI while staying competitive in a rapidly evolving market.
Additionally, you could highlight which digital solutions contribute to business priorities and where they should be scaled. A value-oriented digital transformation roadmap will enable you to convince key stakeholders of your plan for digital transformation rollout.
Drowning in Data and Missing the Human Touch
Manufacturers can often amass vast amounts of data in their quest for automation. And there are certainly areas where a lot of data is needed to train an AI model. For example, in quality inspection, you need thousands of images to determine good and bad products.
However, this excessive focus on data capture can lead to decision paralysis. Say a manufacturer just needs a few key data points to make a decision, but their analysts are working in overdrive to collect as much data as they can. In that scenario, you may not make the best decision because your data collection doesn't match the specific objectives.
Despite the multitude of advances in automation and AI, human expertise remains absolutely fundamental in manufacturing. In fact, 70% of U.S. manufacturing processes rely on human decision-making.
Manufacturers that communicate effectively with human operators will ensure that everyone understands the logic behind automation decisions and will be able to leverage their expertise to work in harmony with automated processes.
Moreover, manufacturers can choose technologies which include humans in the automation loop if those technologies better suit their business objectives; for example, they can send out work instructions to employee mobile devices or headsets, but still expect employees to perform the underlying work.
Manufacturers that align technology adoption with business objectives, engage their workforce in the process, and ensure data collection remains purpose-driven will see the full potential of their initiatives. Tailoring the transformation to the manufacturing sector, leveraging existing resources, and embracing iterative change will lead to long-term success, positioning manufacturers for growth in the digital age.
