How manufacturers can prioritize technology investments in a new normal
This article was originally featured in the Manufacturing Leadership Journal in October 2020. ©2020 Manufacturing Leadership Council, a division of the National Association of Manufacturers. All Rights Reserved.
Transformative technologies create agility and enable new manufacturing business models for a modern economy. But turning these tools into tangible financial value requires navigating a complex array of possibilities. Further complicating the situation is the economic impact of the COVID-19 pandemic on investments in digital transformation. Many manufacturers are now unable to prioritize investments that do not have clear-cut and near-term financial value.
From our perspective, manufacturers can accelerate transformation by leveraging their current assets – one of the most valuable is data constantly produced by the operational technology they already use. That data holds the key to solving current business issues and capitalizing on new opportunities.
Identifying specific tools that can help mine and use that data to address immediate needs is a pragmatic approach to transformative technology – especially given the current environment.
The COVID-19 pandemic has exposed vulnerabilities, forcing businesses to think differently to survive and thrive in a new environment. As a result, many have reprioritized digital transformation to better serve their workforce and customers.
Manufacturers that have experienced less drastic disruption to their businesses may be better positioned to continue investing in transformative technology. But those hit hardest may have the most compelling need to do so rapidly to close unsustainable gaps in their processes and improve agility and visibility into operations. Why? Because transformative technology is uniquely suited for helping them connect and understand shop floor data that is crucial to making intelligent decisions around the business impacts of the pandemic.
To increase revenue, improve operational efficiency, reduce costs, or respond to new customer needs, manufacturers must first navigate their way through the complex and rapidly evolving array of technologies driving this fourth industrial revolution. In addition to the Industrial Internet of Things (IIoT), there’s artificial intelligence, machine learning, digital twin, process mining, predictive modeling, cognitive analytics, visualization, simulation, collaborative robotics, augmented and virtual realities, 5G networks, blockchain, additive manufacturing – and many more.
When executives envision Industry 4.0, there’s a tendency to think about the end state – perhaps that dark factory or warehouse where everything is automated and then monitored and managed remotely. The pandemic and needs for social distancing may have accelerated such thinking. But in reality, that scenario is still far into the future for most organizations. And it’s far from practical for most companies, particularly those struggling to regain stability amid continuing economic uncertainty.
The question then becomes: How can manufacturers use transformative technology to deliver tangible benefits today, while also positioning themselves for an automated future?
Adopting any new technology involves a significant investment of resources, so making the right choice is critical. We see a broad spectrum of willingness. At one end are those still extremely resistant to adapt and invest. At the other are those who bite off more than they can chew – taking on too many new technology initiatives or too big of a vision.
Most manufacturers are somewhere in the middle, experimenting with selected transformative technologies. Often, these trials have evolved from pressure – either top-down or bottom-up – to try something because it is the easiest to implement or the latest and greatest trend. Right now, many are still in an experimental or early stage of deployment. For example, while 82% of manufacturers have either implemented, piloted, or considered IIoT, 70% have not moved past a pilot. In many cases, these efforts lack a clear business objective or measurable business case that’s tied to an expected value.
That last point is key:
Manufacturers must figure out where best to invest their very limited resources to achieve the greatest balance of benefits across people, operations, and customers.
We believe that happens when manufacturers shift their perspective – thinking not in terms of what new technology do we need to invest in, but how they can better leverage the technology assets they already have.
Manufacturers have already made significant investments in operations technology (OT) and software. This technology collects constant and unlimited volumes of data from manufacturing lines that can be leveraged to derive actionable insights and drive value. Consider the breadth of sources:
Most manufacturers do not take full advantage of all the OT data they already have. According to Forrester Research, up to 73% of data goes unused for analytics. There are various reasons for this. Some organizations aren’t aware of all the data that currently exists and may not know how to extract or combine data so that it is useful. Or they may lack the skills and capabilities to uncover insights and translate them into impact on the P&L.
Furthermore, to analyze that data, organizations need to connect it to the IT network. But many are hesitant to do so because of cybersecurity concerns and the need for IT investment to establish proper security, bandwidth, and data storage. For example, due to legacy systems, aging IT infrastructure, or recent mergers or acquisitions, organizations may lack centralized data storage capabilities that facilitate use of data by analytics and business intelligence platforms. And IT teams may lack the necessary skills to manage connected IT/OT infrastructure.
Fortunately, these barriers are surmountable. By focusing on ways to make better use of their abundance of existing data, manufacturers can leverage their existing assets and investments to accelerate transformation and generate near-term value, without a huge investment or long-term initiative.
Before investing in transformative technologies, first determine area(s) where you need to increase value and/or reduce costs. This should start with a broad look at financials and performance. Think about the big issues that affect the P&L, cost of goods sold, and baseline key performance indicators. Examples might include output capacity, labor efficiency or utilization, excess inventory, unplanned downtime, high scrap rates, or increasing reactive maintenance expenses. Then, determine the data insights needed to address the issue. This will guide investment toward transformative technology that can deliver that insight and ultimately, value to the organization.
The following are two examples of opportunities of how to use data and transformative technologies to address common business problems:
The issue: Prior to the pandemic, many manufacturers struggled to reduce or limit field maintenance and service costs. Now, with health concerns related to personal contact, views toward field services have shifted even more.
The opportunity: A manufacturer currently sends a technician to its customer every month to examine the equipment and identify any servicing or maintenance needs and/or on an emergency basis due to unexpected or unplanned failure. By using technology to establish real-time visibility into product health and usage, the manufacturer can now optimize the frequency and duration of travel to service the product and minimize the human touchpoints required.
The solution – smart field services enabled by IIoT: By embedding sensors within a product and connecting them to the network, a manufacturer can track product performance and health in real time. When performance of certain components or operations slips outside of defined thresholds, the sensors will trigger alerts to the manufacturer and/or the customer – signaling a service need before a failure occurs. The manufacturer can then schedule a service visit based on need rather than the calendar. In addition, it can order parts proactively, schedule the best technician for the specific repair scenario, and warn the customer about an impending service need. In some cases, software updates can be pushed directly to a product – as Tesla does – without needing a service call.
Benefits for the customer: Real-time insight into product health enables the manufacturer and customer to address issues before they affect the customer’s operations, minimizing unplanned downtime. Customers can also plan and schedule downtime for service.
Benefits to the manufacturer: Reducing the frequency of service lessens the need to focus resources on urgent situations and enables more consistent and balanced scheduling. Maintenance work orders that have been predicted and identified by smart field service technology can be clustered within the maintenance schedule to further reduce technician visits. Furthermore, if the manufacturer can diagnose the need for a replacement part remotely, then it can order the parts to be picked up by a technician before the first service call, eliminating the need for follow-up visits. The manufacturer can also gather more data about product health and usage to provide input into R&D to further innovate or develop other products.
The issue: Few manufacturers are recession proof. They suffer in downturns when customers cannot make major capital investments. The pandemic has cast a spotlight on this.
The opportunity: Technology opens the door for manufacturers to adapt their business models for a modern economy and evolving customer needs. One way is by replicating the software sector’s success in offering subscription models to create reliable new revenue streams that offset the impact of sales downturns.
The solution – a product-as-a-service (PaaS) model: In a PaaS model, the customer purchases a desired result rather than a product or equipment that delivers the result. One example is a commercial HVAC system, where the customer pays for consistent temperature and air control rather than buying the system. Another scenario is a large commercial printer, where the customer purchases printing services rather than the printing equipment, which would represent a significant capital investment. More specifically, the manufacturer installs the printer based on the customer’s specifications, ensures that it is operational, stocks it with materials, and maintains it, while also retaining ownership of the asset.
Examples of this concept in the market today include:
Similar to smart field service, the technologies enabling this PaaS solution include sensors embedded on products and on network connections, allowing the manufacturer to monitor health, status, and use remotely, without having to visit the customer site. This enables the manufacturer to maintain the product according to the customer’s needs. In some cases, the customer can also purchase access to software that enables remote monitoring or troubleshooting.
Benefits for the customer: The PaaS model – where the customer buys the output rather than the machine – is more responsive to some customers’ current needs and a nice alternative, especially for those that don’t necessarily want to make a long-term commitment on large capital expenditure. The model also creates value beyond the product itself. The customer receives regular upgrades and maintenance at no additional cost and can reduce the need for internal service personnel. Based on ongoing usage analysis, the manufacturer can upgrade or switch equipment to better meet the customer’s desired result. Combined with real-time monitoring capabilities that signal maintenance before it becomes a problem, the solution can help limit unplanned downtime.
Benefits for the manufacturer: Subscription models increase customer loyalty and retention and decrease fluctuations in revenue, which can help organizations better predict financial performance and make more confident budgeting and operations decisions. It also can reduce sales friction with customers and/or shorten sales cycles due to the shift from capital to operating expenditure. A manufacturer may also be able to redeploy used products as long as they are still able to deliver the desired result. Additionally, it can leverage product usage data to feed the R&D and innovation process.
This PaaS example requires a bigger shift than smart field service – but it can be accomplished through a multi-phase effort rather than a complete overhaul. For example, a manufacturer can use product usage data from the field service organization to test the feasibility of a PaaS or another new business model.
While some manufacturers are well down the path toward digital maturity, others are struggling with where to begin. For example, 80% of manufacturers still use paper-based or manual keyboard entry data collection systems. And given the environment, the approach to adopting transformative technology needs to be practical, realistic, and achievable. The following are several key questions to address to pave the path to value:
Once you have defined the business issue or opportunity, determine whether you have the data to identify the root cause of the issue. An example is a molding machine with a 20% scrap rate, which leads to increases material costs, fill rate misses, and maintenance costs, as well as lower labor efficiency.
Then, consider what information you need to address and solve the problem. From our experience, most companies can analyze what is happening or has happened, but have less ability to predict what will happen or prescribe action before a failure inhibits operations – capabilities that are increasingly critical in an environment that demands agility. Furthermore, certain molding machines now have software that allows the manufacturer to monitor the machine parameters and understand when something is off in the machine. To capture this information from older molding machines, the manufacturer would likely have to add sensors that monitor for specific parameters. This produces real-time data that allows operators to stop a machine right at the time it creates scrap. In the future, machine learning models will be able to use this data to predict when the machine will start to produce scrap, prior to that actually happening.
Inventory your product, OT, and plant infrastructure data. What already exists that can be used to help address the issue? What are the gaps in data that must be filled? How easy and/or costly is it to fill those data gaps? In the molding machine example, important data gaps might include temperature, pressure, and speed of different areas of the machine.
For example, if you need to drive down COGS and increase manufacturing capacity utilization to improve margin, then artificial intelligence or machine learning may be useful for analyzing data about cycle time, scrap rate, downtime on a SKU/asset level, and labor efficiency and modeling potential solution scenarios. If field service/maintenance calls are increasing, then adding sensors that provide real-time alerts and monitoring could help address the problem.
Develop a case focused on value to the business. This should include a net present value analysis that considers initial costs, ongoing costs, and ongoing returns over time. Add a probability-of-success variable to quantify the expected value.
Technology and data alone will not solve business problems. You also need people who understand how to analyze, integrate, and interpret data from different facets of operations and turn it into actionable insights. This will likely require people with data science or engineering backgrounds, as well as an understanding of both operations and business strategy. That is an evolving skillset in manufacturing and one that is holding many companies back from digital transformation. For example, 40% of manufacturers say they aren’t implementing IIoT due to lack of required skills.
Companies have traditionally limited IT connectivity with the shop floor due to security concerns, as well as hardware constraints. The latter is easier to address today with Wi-Fi or Bluetooth technology. Security is still a valid concern, but can be addressed with the right industrial control system and cybersecurity and master data management approaches.
A live feed and transparency between the shop floor and internal supply chain is beneficial because it allows the executive team and other managers to make quicker, intelligent, and more accurate decisions. For example, when an advanced ERP or supply chain planning platform senses delays in raw material supply from an integrated supplier’s ERP system, it can trigger a change in how the shop floor queues product line changeovers to maintain overall output and efficiency. Similarly, connecting OT data with HRIS data can increase visibility to labor utilization – for example, allowing a company to track the number of shop floor manhours per manufacturing line output and analyze it for trends and changes.
Lastly, make sure that the IT organization managing transformative technologies understands the connection between its work and the business goal.
It is not uncommon to have people on the plant floor who have worked decades in a specific way. When introducing new technology to existing teams, it’s important to consider the people involved. Develop and communicate a compelling vision so people understand the value to the company, beyond the context of their own area of operations. While their specific role or operation may directly improve, their ability or willingness to change could have a dramatic impact on a downstream operation or the business as a whole. They must understand how to use data and technology to achieve goals. And most importantly, they need to understand how it benefits them. For example, it can help them eliminate time wasted by having to re-do certain work, or allows them to connect with customers more efficiently.
Additionally, cultivate supporters and build executive buy-in. Executive leadership for transformation is critical, but many manufacturers have not yet established it. For example, McKinsey found that only about a third of companies have appointed a C-level executive to lead digital manufacturing efforts. Other key steps include identifying and removing obstacles and identifying short-term goals to build momentum. It is virtually impossible to over-invest in change management.
Large-scale Industry 4.0 transformation may be the long-term goal, but in the current environment, most manufacturers must find a way to start small and build on initial successes.
Understanding how small, targeted investments can meet immediate business needs is particularly important as companies navigate out of the pandemic and into a new normal.
This approach focuses on goals such as stabilizing revenue, improving planning capabilities, and addressing issues that impact the P&L right now. But it also sets the foundation and builds momentum for longer-term transformation, like that virtually managed, dark factory of the future.
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