Smart Manufacturing: What Are The Challenges?
Industry 4.0 is disrupting every link in the manufacturing value chain, with Connectivity, Cloud, Big Data, IoT, AI and Virtualization all acting in concert to create a new model of value creation based on data. But manufacturers have been slow to go digital, leaving them vulnerable to disruptive startups. This blog will examine one of them, and look at what’s holding back the old guard.
Legacy Infrastructure is Obsolete
Virtualization of the production environment has been slowed by IT systems that weren’t designed with inexpensive connectivity, cloud, and data storage in mind. What’s more, full digital operations are still risky, as shutting down an entire assembly line just to fix a software or network failure can be cripplingly expensive.
Connectivity Requirements are High
Machine vision and cooperative robots can require sub-millisecond connection latency and data rates of 10 Gbps, far more than what your typical onsite Wi-Fi can deliver. But fortunately, next-gen wireless network solutions are on the way that can deliver the bandwidth, latency, and reliability that smart manufacturers need, at up to 50% less cost and 10% less energy consumption.
There’s a Growing Skills Gap
Data analytics are a central facet of manufacturing today, and a source of insight into processes, faults, consumer habits, and more. But there’s a problem. McKinsey estimates there will soon be a shortage of around 1.5 million analytics experts, and that’s just in the United States. This is leaving many companies unsure of how and where to deploy analytics solutions, or how to use the huge volumes of data being generated. And even when data scientists are on the payroll, there’s another problem:
There’s Also a Growing Trust Gap
In a survey by Tata Consulting on big data analytics in manufacturing, the top problem identified by enterprises is building trust between data scientists and functional managers, which is creating a gap between data insights and strategic decision-making in turn. The second biggest problem is determining what data to use for which business decisions.
The third biggest problem is an inability to handle the volume and velocity of the data being generated. Simply put, manufacturers are not making the most out of their data.
What About Best Practices?
The complexity of the manufacturing industry means that no coherent industry-wide digital transformation strategy exists, leaving individual businesses to digitalize at different rates and in different directions. Moreover, many companies lack the agility to quickly shift away from more traditional goals such as lean manufacturing. Indeed, the aforementioned Tata Consulting survey found that the top three benefits of data analytics for manufacturers are still in line with those old-school optimization processes – tracking product defects & quality, supply planning, and identifying manufacturing process defects.
Given manufacturing’s commitment to lean processes, they’ve been relatively fast movers in analytics, smart sensors, and Industrial IoT (IIoT), which is all well and good, but the productivity gains from Six Sigma and lean manufacturing have tapered off over the last five years, as processes are now as about optimized as they can be using these methods.
There’s Also Security
In March 2017, Manufacturing Business Technology reported that manufacturing is the second-most hacked industry, after healthcare, and this is largely due to inadequate investment in security. Although cyber attacks cost businesses US$400 billion in 2015, and is set to rise to US$2.1 trillion per year by 2019, cyber security, like data analytics, lacks practitioners, with the non-profit information security advocacy group ISACA predicting a global shortage of two million cyber security professionals by 2019.
And to make matters worse, the transition to Industry 4.0 is creating larger attack surfaces for hackers thanks to a vast number of connected IIoT devices, more complex networks than before, and big data processing happening in the cloud. Many companies lack a robust E2E Information Security Solution that protects against attacks via servers, clients, the web, software, and DDoS. On the R&D link of the chain, IPR and sensitive data require a network solution that separates the R&D intranet from the office extranet so that connections can be secure, with collaboration encouraged.
Are You Being Unmade?
Today’s consumers expect more personalization and faster delivery, both of which require a shift towards mass customization, strong digital infrastructure, and, more recently, drone delivery. However, traditional manufacturers have been slow to embrace the mindset of markets of one. In fact, it comes in last in the aforementioned Tata Consulting survey.
One company looking to take advantage of this reticence is Unmade, a fashion startup that enables customers to customize garments before they’re made, enabling custom designs to be produced at the same unit cost as mass-scale goods. To avoid over-production and waste, Unmade’s business model depends on three elements – personalization, e-commerce and on-demand manufacturing. An online personalization editor allows customers to change colors, patterns, and logos on garments; the ecommerce model allows existing stock and customized pieces to be sold together; and on-demand manufacturing sends orders to partnering knitwear factories to be made.
A press evaluation describes this model as, “The tools of factory production available at the click of a mouse, with no penalty for short productions runs” – that’s disruption.
Disclaimer: Any views and/or opinions expressed in this post by individual authors or contributors are their personal views and/or opinions and do not necessarily reflect the views and/or opinions of Huawei Technologies.
2 thoughts on “Smart Manufacturing: What Are The Challenges?”
That’s crazy Gary!!!
Thanks for sharing. Really a great read.