Digital transformation in manufacturing: the benefits

Digital transformation is not an option. The debate on whether manufacturers should deploy emerging technology ended some time ago, and a universal truth within the sector is that standing still is not an option.

Manufacturing has faced myriad challenges in recent years which have placed huge pressures on manufacturing operations and exposed frailties and vulnerabilities which had not been exposed previously – as Warren Buffet said: “It’s only when the tide goes out that you realise who’s been swimming naked.”

In order to alleviate that pressure, manufacturers have recognised the need to be more efficient, streamlined, agile and resilient; and at the heart of all that is technology. From ERP systems to automation and robots, factory of the future technology is enhancing manufacturing operations like never before, and safeguarding them against any future disruption that may, and probably will, come along.

But what are the benefits to deploying technology? What impact are these technologies actually having on the bottom line of manufacturing businesses? And what does the ROI look like?

Production efficiency	Improved CX
Business transparency	Enhanced decision making
Sustainability	Competitive edge
Supply chain resilience	Enhanced energy management
Inventory management	Real-time data
Reduced costs	Predictive maintenance
Reduced time to market

---

Production efficiency

By moving away from traditional paper-based and manual processes, manufacturers can benefit from significant production efficiency gains. One of the ways this happens is as a result of being able to identify every tiny issue in the production cycle. With complete visibility at every stage, manufacturers can focus on tackling small issues that cause inefficiencies in the process.

Leveraging digital technologies allows manufacturers to reduce the time, effort and resources required to produce their products, without sacrificing quality or improving upon the initial product quality.

Implementing automation, for example, can reduce the time needed to execute repetitive tasks such as data entry, order processing and inventory management; freeing up valuable time and resources for more productive tasks.

Digital transformation can also optimise manufacturing workflows by leveraging real-time data analytics to identify inefficiencies and bottlenecks in manufacturing processes, enabling manufacturers to take corrective action quickly and improve areas where improvements can be made.

In another example, machine monitoring technology provides reliable real-time data on machine utilisation, enhancing resource planning and enabling the manufacturer to identify areas for process improvement and cost reduction.

In another example, manufacturing execution systems (MES) can improve a company’s efficiency and provide complete traceability of all products and processes. An MES can also allow a company to refine production methods through the introduction of controls and standardisation, further supporting quality management systems.

Business transparency

Business transparency or supply chain transparency in manufacturing is vital for success. It allows businesses and consumers to understand how goods are produced and distributed. This includes knowing where and how products are made, the labour practices involved, the journey of products from source to consumer and any environmental impacts that occur along the way.

Digitising business processes requires clear information management, which boosts transparency and accountability. Being transparent with data and your supply chain management can better ensure customer trust. Additionally, if processes are digitally tracked, management can trust how things are done.

Within manufacturing supply chains, digital technologies can aid in the transparency of the business. An example of this is using technology such as barcoding or tagging; products can be tracked from start to finish and ensure sustainable and ethical practices are used during transportation. This allows businesses to confidently show customers their supply chains.

Other examples of digital transformation systems that aid in business transparency are digital record-keeping and data management, which can make information more easily accessible to the public and regulators; blockchain technology to create tamper-proof records of transactions; and AI to identify and flag potentially fraudulent activities.

Sustainability

The deployment of digital technologies offers manufacturers vital visibility into their systems and processes, and therefore allows them greater control over how, where and when their products are manufactured. A knock-on impact of this is that it provides a window into an organisation’s emissions. As the saying goes, you can’t manage what you can’t measure, and digital technology provides the vital information that manufacturers need in order to act on their emissions, deploy reduction strategies and ultimately hit looming net zero targets.

Digitalisation and sustainability go hand-in-hand – digital innovation will play a fundamental role in the mission towards decarbonisation and net zero through the advancement of cleaner technologies. Embracing digital solutions allows businesses to optimise operations, reduce waste and enhance resource management. One such example is additive manufacturing, or 3D printing, which can create complex parts on-demand, such as automotive parts or customised medical implants, reducing waste and streamlining supply chains.

The use of tools such as artificial intelligence and machine learning can also offer data-driven insights, allowing organisations to track metrics and leverage data to analyse and demonstrate environmental impact. By helping to identify energy inefficiencies and optimise consumption, adopting digital technologies can form a key part of companies’ energy efficiency strategies and reduce carbon footprints.

Digital transformation will also help to improve efficiency and flexibility, helping to streamline work processes and allow businesses to adapt to industry demands. The adoption of technologies such as digital twins, for example, can help manufacturers simulate real situations and their outcomes, ultimately allowing them to make strategic decisions and enhance performance, while speeding up design cycles.

Embedding sensors in machines that constantly monitor operations, enables real-time data analysis which can then be applied in decision-making, allowing organisations to optimise processes and strategies for improved results. Leveraging these emerging technologies can also allow manufacturers to accurately predict consumer demand, and as a result, optimise product development, inventory management and supply chain efficiency.

Supply chain resilience

A variety of challenges over the last few years have placed enormous pressure on manufacturing supply chains. A ‘perfect storm’ has impacted the manufacturing sector meaning that supply chain resilience is one of the highest priorities on boardroom agendas across the country.

The reality of manufacturing is that to ensure production, processes and operations are successful, supply chains need to be relied upon, and any challenges or disruption can have a huge impact on inventory, flow of deliveries, availability of components, trust among suppliers and partners, time to market and, ultimately, brand reputation.

Digital technologies enable manufacturers to have greater visibility up and down their supply chains, which can help mitigate disruption and plan for future challenges. Technological innovation represents an opportunity to build resilience and gain an edge. In fact, the advances in smart automation and data-driven insight are opening the door for manufacturers to collaborate more effectively.

By becoming more data-driven, for example, manufacturing businesses will be able to make faster and more informed decisions. Being more agile in response to market changes reduces the impacts of disruption, wherever it may come from. From capitalising on the Internet of Things (IoT) to breaking down data silos across a business, modernising data infrastructure ensures you can take advantage of efficiencies as the manufacturing industry continues to digitally transform.

Supply chain data contains vital analytics that can power inventory, logistics and product development. Therefore, a cloud data platform can give a near-instant, elastic scalability of compute and storage for real-time insight across a business – even as it grows. This can aid smarter, more data-driven decisions that boost operational efficiencies.

Inventory management

Inventory management in manufacturing is the process of tracking the inventory of products and goods in multiple stages of production. Effective inventory management reduces production costs and streamlines operations.

Businesses must plan and utilise an inventory management system because it directly affects the production of goods and the supply chain. Using technologies to digitalise inventory management has a range of benefits, not only aiding in processes and efficiency, but digital transformation also helps businesses to enhance their customer experience. By digitalising, businesses can provide accurate stock information to customers, reducing backorders and improving satisfaction.

AI is at the forefront of many aspects of digital transformation in manufacturing. Within inventory management it can automate decision making, predict demand patterns and in turn, enhance efficiency.

Cloud technology allows for cross-team collaboration and flexibility which is required in today’s fast paced manufacturing industry. Cloud technology often includes features like barcode scanning, meaning inventory can be seen from anywhere, not just the factory floor, providing a full view of data in real-time. Stock alerts can be created for low stock or high demand products, ensuring correct future ordering.

Newer technology such as augmented reality (AR) can improve warehouse accuracy and provide real-time information to workers, increasing efficiency and reducing errors.

Effective inventory management in manufacturing, enhanced by digital transformation, has long lasting benefits for a business and customer alike.

Reduced costs

In recent years, manufacturing costs have soared in all areas. The energy used in production, materials, transportation and labour, for example, has had an impact on small and large manufacturers alike. It is becoming more expensive for manufacturers to import components overseas due to geopolitical incidents, in turn raising prices of production and then raising prices for customers. Reducing costs is a never-ending goal for manufacturers.

However, digital transformation of manufacturing has been proven to reduce costs and improve productivity. Businesses need to be using the latest technologies to make better, more cost-effective decisions.

Streamlining processes through digital transformation can reduce costs as automation and digital tools will replace manual time-consuming tasks that workers are doing. It also cuts labour costs and requires a smaller, but more skilled workforce.

Reduced time to market

Implementing new technologies into a manufacturing business to automate and make processes more streamlined provides for increased efficiencies, productivity and ultimately profit. It allows products to become more standardised as manufacturers will have more control over the accuracy of the components in the manufacturing process. And because technology reduces the room for any errors, the likelihood of downtime is significantly reduced.

For example, robotics can automate repetitive tasks, reduce production times and increase output by eliminating the need for human labour, redistributing it to a more useful area of the business. In addition, AI and advanced analytics can alert a business before machines need maintenance, which reduces downtime and increases throughput on factory lines.

And, digital manufacturing can create a virtual production environment allowing engineers to identify and address production issues before they arise. When building new products, or introducing changes to existing products, companies can speed time-to-market and identify potential issues by using simulation to visualise production processes and product lifecycles, drive throughput, optimise processes and increase efficiency.

When it comes to speeding time-to-market, digital tools such as sensors, IoT platforms and data management solutions that incorporate advanced analytics, machine learning and failure analysis are critical.

In addition, technologies such as additive manufacturing offer enhanced customisation options, the ability to make parts on demand, and can reduce lead times and minimise excess inventory.

Improved CX          

As Henry Ford once said: “You can have any colour as long as it’s black”. This ‘one size fits all’ statement very much epitomised the early days of mass manufacture, where there was little or no room for consumer nuances or customisation.

However, we live in a very different world today and businesses of all sizes are now heavily focused on giving tailored, end-to-end experiences to their customers. Manufacturing is no different with evolving customer preferences driving the need for a diverse product set with mass customisation options – the automotive sector being a prime example – and a notable shift towards direct-to-consumer models has emerged within the sector.

While this may have been difficult in previous generations, emerging digital technology is now making this a reality.

The advent of AI, for example, has empowered manufacturing firms to elevate customer interactions and deliver superior customer experience (CX). AI is repositioning CRM systems as the nucleus of CX, furnishing the foundational technology necessary for engaging customers and fostering repeat business.

Technology can help enhance CX by personalising marketing campaigns, emails and blogs, thereby improving customer engagement. Moreover, they aid interactions between service teams and customers, expediting issue resolution and improving customer satisfaction.

Customer interactions should consistently prioritise personalisation and customisation. Digital intelligence now makes it easier for manufacturers to provide seamless experiences, considering factors such as customer history, preferences and past issues.

A robust CRM with generative AI capabilities facilitates the development of products and solutions tailored to customer needs. Customer-centric digital transformation initiatives can significantly improve CX, leading to improved customer retention and revenue growth.

Enhanced decision making

No one wants to make decisions on a whim, especially when the success of a business is at stake. Informed decision-making is crucial in the business world and is significantly enhanced by the data provided by digital technologies.

Data-driven decision making is essential for any successful manufacturing business. In the sector, digital tools give companies extensive access to data, enabling them to make informed decisions and take rapid action. The process involves collecting, analysing and interpreting data to identify trends, patterns and insights that guide better decision-making. This approach not only improves the customer experience but also streamlines operations.

One transformative step that manufacturers can take is by integrating ERP (enterprise resource planning) software. It consolidates many business processes and data into a unified platform. The software facilitates better collaboration and communication between departments across the whole business, allowing for enhanced decision making with all elements considered.

Another is PLM (product lifecycle management) which helps manage the complete journey of a product from initial ideation, development, service and disposal. It minimises errors, ensures product quality, accelerates development and facilitates collaboration.

By integrating platforms such as ERP or PLM, businesses can significantly enhance their decision making processes through digital transformation.

Competitive edge

Everyone wants to be a winner and digital transformation helps companies achieve and maintain a competitive edge in their industry. Leaders who are embracing emerging technologies have the potential to learn faster, make data-based decisions, enable greater use of automation and encourage further innovation within the business.

But what’s stopping everyone from being at their best? Slow adoption of technologies. Businesses will fall behind in terms of cost-effectiveness, product quality and customisation capabilities. Not only that, but they will lack the ability to integrate into global value chains and leverage emerging technologies, leading to weakened collaboration and partnerships.

Not only that, but looking at the long-term plan, and not the short-term wins.  Manufacturing operations run around the clock with very limited downtime, making it incredibly difficult for plant managers to allocate time for evaluating and implementing new technology. Companies need to ditch the quick wins and look to integrate long-lasting digital technologies for continued transformation.

To remain competitive, implementing digital transformation technology such as AI and machine learning can allow manufacturing businesses to predict consumer demand accurately, optimising production schedules, inventory management and supply chain operations. Or even just transitioning to a paperless environment can have great effects on business output and help keep up in an ever-changing market.

Enhanced energy management

Few businesses will be able to anticipate changes to the cost of energy because it’s the one commodity most sensitive to change. Still, companies can offset some of the uncertainty by making better use of what’s already being consumed.

As mentioned earlier, when it comes to energy, you can’t manage what you can’t measure. Digital technology has enabled manufacturers of all shapes and sizes to better understand where, when and how they are using energy within their factories, systems and processes – much of it in real-time. That unprecedented visibility is providing organisations with all the information they need to roll out efficiency improvements and lower emissions.

It is predicted that Industry 4.0 technology will improve asset utilisation in manufacturing by 35-40%. Digital tools make it easier to optimise and execute energy strategies based on trends from real-time manufacturing data. Without this level of insight, it’s impossible to know whether energy is being used effectively and if there are assets requiring attention.

Smart energy management also makes it easier for businesses to integrate distributed energy resources as they become available, such as microgrids and battery energy storage systems.

The deployment of digital technology can enable manufacturers to gain real-time visibility of energy usage to highlight ways to manage and reduce energy consumption for reduced costs; promote employee/stakeholder engagement to drive a culture of sustainability throughout the entire organisation; measure the baseline of carbon emissions to act as an indicator of energy efficiency and performance; determine organisational footprint to access opportunities to reduce carbon emissions across multiple business units; highlight energy-inefficient assets with energy usage and production data; and meet commercial and procurement requirements.

The industry’s energy needs will only grow in the future and most businesses will need greater clarity around consumption to remain competitive – especially in the UK. Smarter management will provide that clarity, but also a means to take full advantage of emerging developments in the energy market.

Real-time data

Data is all around us, and businesses across a wide spectrum of sectors are benefiting from the insights that can be gleaned from it. The industrial arena has huge potential to achieve significant efficiencies to help negate production line downtime, create uptime and deliver significant bottom-line cost savings.

One of the key differentiators of modern technology, when compared to previous generations, is the speed in which it can get information to the user. In a sector such as manufacturing, where down-time (even a small amount), can have huge implications on the bottom line, this is vital.

Data is very much the fuel that drives any digital transformation so the quality of the data is essential to success and will provide the insight for manufacturers to improve operational efficiencies, secure long-term growth and remain competitive.

Real-time data, when measuring OEE for example, enables production managers to act quickly to uncover the root cause of problems. Shop floor teams can also use the data to uncover reasons behind process inefficiencies to drive improvement. This in turn creates a culture of continuous improvement, as operators become actively engaged with the system and see the value gained from it.

In addition, real-time visibility of quality issues such as non-conformance events or when a process is trending out of control, is imperative to achieve improved yield, reduced production waste and avoid costly product recalls. And, as mentioned above, measuring and monitoring energy usage in real-time, rather than through monthly or quarterly energy bills, empowers corrective action across the board.

Real-time data also offers manufacturers greater insight to help reduce production loses, optimise maintenance activities and understand labour costs. Investing in digital technology to support increased visibility of assets enables manufacturers to address the root cause of business challenges – often a more efficient and cost-effective alternative to investing in new facilities or machinery.

Predictive maintenance

As the saying goes, prevention is better than cure, and when it comes to maintenance strategies within manufacturing this certainly rings true. Traditionally, businesses have tended to opt for a reactive, run to fail approach, whereby actions are only taken after a piece of equipment has failed. This means that, for any manufacturer using this method, a significant percentage of the work undertaken by maintenance teams essentially revolves around crisis management or ‘firefighting’.

Alternatively, many have deploy a time-based maintenance strategy (preventive). Here, actions are taken based on a predetermined time schedule and on the statistical ageing of different components. However, because it is based around time schedules, equipment can be scheduled for repair or reconditioning either too early (before it’s required) or too late (after the equipment has already failed).

However, the onset of digital technology has ushered in the possibility of a condition-based (predictive) maintenance strategy which means that equipment is first of all digitally connected and based on real-time data.

The status or condition of equipment can then be analysed, and actions taken. This can be beneficial if, for example, an industrial environment is particularly hot which can cause electrical components to age much faster. Therefore, this strategy can make a huge difference for optimising the actions that are taken.

Sometimes it can even postpone actions that would have been taken with a preventive strategy, because it can show that equipment is actually in a better condition than was first thought, thus further enhancing sustainability.

The overall aim of predictive maintenance is to pinpoint potential faults in machinery at the earliest opportunity, with the goal of removing the need for more complex maintenance, repairs and even replacement.

Predictive maintenance takes data from multiple and varied sources, combines it, and uses machine learning techniques to anticipate equipment failure before it happens. Manufacturers can stream data from sensors mounted on their machines to uncover key usage and performance patterns in real-time.

Many businesses are already using continuous monitoring technologies – like Internet of Things (IoT) connected devices – which is a good start; but the key lies in not just simply monitoring the output of various data (which is how many companies use it today), but by taking the next step and employing advanced algorithms and machine learning to take action from real-time insights.

Predictive maintenance is widely considered to be the obvious next step for any business with high-capital assets looking to harness machine learning to control rising equipment maintenance costs.

As a whole, predictive maintenance offers manufacturers a tremendous opportunity to boost operational efficiency. The process can be easily automated through machine learning and cognitive data science, and can lead to reduced maintenance and down-time, improved safety and enhanced inventory management.