Data is more readily available than ever before for manufacturers. Fuelled by the UK’s Modern Industrial Strategy, today’s smart factories, integrated supply chains and digitally-connected customers are generating huge volumes of it! But all this data means nothing if manufacturers are unable to generate insights, drive action and inform decisions. For Kevin Bull, Product Strategy Director at Columbus, value comes from transformation, not collection. To drive positive business outcomes, manufacturers need a data-driven strategy that will help them remain competitive for years to come.
Against a backdrop of more complex supply chains, higher customer expectations and smaller margins for error, data has become a top priority for 74% of UK organisations to drive performance and inform decisions. Despite this, many manufacturing organisations struggle with data quality, silos and governance. Add to this the wider industry challenges of skills shortages, growing legislation and regulation, and it is clear that they need to take action – and for this they need the right sort of data to drive positive business outcomes.
Success demands foundational discipline: clean data, unified Information and Operational Technology (IT/OT) systems, robust governance and security. With these in place, these large manufacturers can turn data into a competitive advantage in five easy steps. Otherwise, these challenges will persist, and they will get left behind by competitors in the next chapter of the digital revolution.
Step One: Data must drive business outcomes
Data is only valuable when it drives outcomes. Yet a challenge for manufacturers is linking data insights to strategic business objectives such as cost reduction, sustainability or customer satisfaction.
At the very beginning of any data transformation journey, it’s important to establish what a manufacturer wants its data to actually achieve, whether this be optimised supply chain efficiency or improved product quality. This way, clear KPIs that align with business goals ensure that data collection is focused and relevant.
It’s important at this stage to look for quick wins that directly support objectives and can help demonstrate the value of data-driven decision-making early on. Don’t forget the right technology can also be an asset here as when combined with data it can support day-to-day work and decision-making. For instance, if a sensor captures a data anomaly, then corrective actions can be automatically established, and the right people and resources can be actioned.
Step Two: It takes more than technology to be data-driven – a strong workforce culture shouldn’t be underestimated
With the UK currently suffering a major digital skills shortage that is costing the economy £63 billion per year, the challenge now is for manufacturers to cultivate a strong data-driven culture within their workforce. Employees need to trust, understand and feel empowered to work with new systems and processes to leverage the potential of intelligent data insights fully.
Any effective data-driven culture starts at the top with leadership. They need to lead by example, whether that means using the technology, communicating to employees the context behind the importance of accurate data, providing them with the correct training and tools, or involving them in the early stages of adoption and celebrating data-led wins to build employee confidence. Employees that feel seen and heard are more likely to engage with changes if their needs are met.
Step Three: Break down data silos with a unified data foundation
One of the biggest barriers to effective data use is the fragmentation of information across multiple systems such as sensors, machines and ERP systems, as this can cause severe delays and provide incomplete and even misleading insights. So how can manufacturers tackle this issue head-on?
An IT/OT integration ensures different systems communicate with each other and trigger automatic action. For instance, sensor faults generated on shop floor equipment can automatically trigger actions on enterprise systems, resulting in quicker fixes and optimised processes.
What’s more, the consolidation of data from multiple sources into a central platform creates a single source of truth, which gives decision-makers complete visibility of operations and enables them to identify opportunities and improvements faster. Data consistency across systems is also important at this stage as it results in easier identification and trend analysis.
Step Four: Adopt a security-first approach to safeguard valuable data
With more and more manufacturers turning to digital technologies to transform their business and operations, the threat of cyberattacks also rises. In fact, supply chain cyberattacks have surged by a staggering 431% in the past few years, and with manufacturing being the most cyber-attacked industry for five consecutive years. Without strong data governance and security, data quality diminishes, and compliance risks increase with potential catastrophic results for manufacturers.
Yet it’s important that manufacturers understand that cybersecurity is not an IT problem but a company-wide problem that requires a company-wide effort to fix. And that starts with building a security-first architecture that can embed secure-by-design principles into all operations and make cybersecurity a critical step when connecting new systems and tools. Going one step further, manufacturers can look to control data access with multi-factor authentication, role-based access and encryption to protect data.
These cybersecurity measures are only effective if employees understand best practices. This is where the human firewall comes in. At this stage, it’s critical manufacturers arm employees with the knowledge and skills to adapt and excel with more secure workflows.
Step Five: AI integration can take data intelligence to the next level
Today’s digital revolution is being spearheaded by AI, but manufacturers are lagging behind in their adoption! According to recent findings, over half of businesses in information and communication use AI, compared to only 13% in asset-intensive industries such as manufacturing.
The benefits of leveraging AI are endless, whether it’s to anticipate equipment failures, cut downtime and save costs with predictive maintenance, enhance quality with machine learning detecting defects more accurately than humans, or to manage inventory and logistics more effectively to optimise supply chains. AI is quickly becoming an integral part of manufacturers staying competitive in the digital era, but its success hinges on a number of key rules.
But don’t forget about the guardrails
Before putting data into any AI tools, data must be error-free and standardised with key definitions to ensure the AI systems interpret the readings correctly. From here, manufacturers must define compliance rules and create a data catalogue to manage data safely and securely.
Once these guardrails have been established, manufacturers can integrate data from sensors, smart cameras and vibration monitors with edge computing to process data on the shop floor for faster responses, reduced latency and continuous operations. Downtime can become a thing of the past thanks to predictive maintenance tools, which identify potential failures before they impact production, machine lifespans and maintenance costs.
Case in point: Planting the tools for future-proofed operations
For one global food manufacturer that helps farmers, landowners and businesses maximise land potential, the seasonal nature of its work was a massive constraint. Its legacy system was struggling to keep up with its broad portfolio of businesses operating on different systems, and with busy peak season in early spring, they were left with a tight timeframe to implement innovative technology-led solutions.
The company needed to modernise and find a scalable solution tailored to its different business needs and partnered with Columbus to deliver a complex Microsoft Dynamics 365 implementation.
The result? By accounting for the manufacturer’s complex logistics, seasonal deadlines and multi-entry structures, the implementation went live on time, and Columbus helped to streamline logistics, enhance decision-making, eliminate manual tasks and improve ESG reporting.
Lead the way and turn information into intelligence
In today’s digital era, there is no denying that manufacturers are data rich. But the manufacturers that will benefit greatly from it, and the powers of digital transformation, will be those that formulate a robust data-driven strategy – one that establishes clear goals, breaks down data silos, empowers workforces, strengthens data governance and security and leverages AI to help turn data into a strategic asset and competitive advantage.
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