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The evaluation of the Return on Investment (ROI) of Industry 4.0, IIoT, or Smart Factory projects and technologies is often particularly difficult. The added value of Industry 4.0 is unclear and many of it is difficult to measure. Therefore, it is essential to establish a simple ROI story for the successful implementation of Industry 4.0. We clarify what to look out for when selecting providers.
In the end, it's all about money with digitalization
The sometimes difficult measurability of Industry 4.0 projects and technologies often lies in the nebulous definition of goals and use cases, which are often more qualitative than quantitative. They are far removed from the effects and measurable added values of Industry 4.0.
A company exists to make money. The economic performance of a company can easily be seen in the profit and loss statement. As an engineer, I envision this like a funnel: At the top of the funnel (the top line), money comes in from new orders, and at the bottom, after processing the orders, money is left over (the bottom line).
There are three levers available to improve profitability: increasing revenue, enhancing cash flow, or improving efficiency.
By generating more revenue (higher prices or more orders), the funnel widens at the top, and thus overall.
Better cash flow (faster turnover) makes the funnel medium more liquid, increasing the throughput through the funnel, allowing money to flow faster.
Higher efficiency and improved margins enlarge the outflow of the funnel, allowing more of the revenue to remain.
Let's go through the aspects point by point:
Bringing in more revenue:
Industry 4.0 is primarily not a sales tool that helps secure more customers or more contracts. Of course, modern technologies can be used in external presentations to portray oneself as a progressive company, but it is questionable how much effect this has on generated revenue, let alone how this could be measurable.
The vision of a lot size of one fundamentally aims to secure more market share by efficiently serving even the smallest customer inquiries in a self-managing factory and reacting quickly to market demands. The question is whether this vision can become a reality considering the organizational changes that must accompany it. Not to mention that continuous, semi-continuous, or batch production processes are not physically capable of producing in the smallest lot sizes.
Industry 4.0 is also supposed to contribute to higher quality production. Generally, this aims to reduce scrap costs. Quality improvements are thus often not accompanied by an increase in quality that would be noticeable and differentiable for customers in the market, allowing for a price increase without a drop in sales.
Top Line Growth is therefore difficult to realize through Industry 4.0. Although the discussed aspects may sound logical and certainly have some legitimacy, the listed aspects are hardly tangible, the effects hardly measurable, and thus should not be used for a clear ROI calculation.
Improving Cash Flow
Reading Eliyahu Goldratt's classic The Goal, one might get the impression that inventory in any form is bad and negatively impacts production profitability. This argument only works if an organization can always have as much capacity as the market demands in volume and can produce as needed. However, this logic quickly reaches its limits with seasonal or cyclical products - especially when the cyclicality cannot be offset by the product mix.
The underlying argument persists that a company has a higher throughput when customer inquiries can be processed faster, and produced products can be sold more quickly. Depending on the industry and production methods, this effect varies. Setup processes and startup times consume time, resources, and money. Many of these processes are inherent to the nature of things – the thermal inertia of a facility cannot be influenced by intelligence.
The planning and organizational optimization of production is part of so-called operations research and generates secondary effects on the overall productivity of companies. Industry 4.0 technologies can help to establish a reliable data foundation for these optimizations of the Smart Factory. These data are prerequisites for planning and controlling to find the optimal trade-off between small and large production lots, inventory management, delivery times, and production capacity and resource utilization.
In these cases, the employed Industry 4.0 solution is a prerequisite, but the positive effects come from the planning and calculation finesse, which can already achieve significant effects with heuristic data. Thus, the resulting ROI should not be primarily attributed to the Industry 4.0 technologies.
Increasing Efficiency
It becomes clear that Industry 4.0 must increase efficiency and production productivity, and that ROI must be justified and measured here. However, the question remains how this increase in efficiency can be realized. In many respects, production data initially creates only transparency. Deriving actions and added values from this transparency is left to the users:
A large hall dashboard can, for instance, display the current line speed. If the viewer cannot take any action from the presentation because, for example, they do not know what the associated benchmark is, no improvement emerges.
Merely capturing downtime does not prevent downtimes.
The challenge is thus clear. As long as data are simply displayed, the transparency initially brings little in terms of measurable efficiency improvement. This makes the real ROI difficult to grasp. Only when data transparency is transformed into information and concrete actions arise from it, which in turn have a direct effect on the productivity of my production, does the Industry 4.0 solution exert a positive influence on profitability and thus on ROI.
Interim conclusion
In many common chains of argumentation, the added value of digitalization is justified with different variants of transparency. Here, use cases are often presented alongside secondary effects of organizational or operational improvement.
Attributing a clear ROI to these secondary effects is usually difficult since they are not truly measurable. In addition, in such cases, the employed Industry 4.0 technology is necessary but not the driver of the effects achieved. Thus, they should not be primarily attributed to the technology.
Technologies reduce the complexity of the underlying problem but do not solve it. This effect becomes evident when the ROI is justified with saved working hours. Reduced complexity is undoubtedly valuable for making jobs or tasks more attractive. However, in the rarest cases, employees are reduced with X% fewer FTEs.
Thus, there is no measurable monetary added value from more revenue, better cash flow, or higher efficiency. Transparency alone leads to no added value if data-driven optimizations are not derived.
This realization has now been reached by most users of Industry 4.0 technologies. The most recent evaluation by IoT Analytics regarding the most common Smart Factory KPIs shows that the first five KPIs aim to produce more efficiently, save money, and produce measurably more profitably.
This measurability and the simply understandable articulation (story) of how the employed technologies affect these KPIs and lead to a measurable monetary benefit are of great importance: For the purchasing decision, this is crucial, as decision-makers inherently require a quantified economic justification to secure budgets and capacities for digitalization. However, more importantly, the successful implementation and introduction of digital solutions depend on this story.
The success depends on a simple ROI story
When evaluating ROI, one must look closely and understand, question, and adequately reflect the effects of the Industry 4.0 solution for oneself and one's own company. Because: digitalizing production always goes hand in hand with transforming the company. Sometimes this transformation is subtler, sometimes deeper. We all know how hard it is for organizations to change, and how stubbornly an established status quo and familiar processes can persist. To break these patterns, a simple, understandable story is crucial, which is repeated often until it is internalized by everyone.
Change management thus requires a simple story so that employees and colleagues understand the goals and expected added values. Only when this understanding is internalized by a wide mass does digitalization gain the necessary presence in everyday thinking and action, making the acceptance of the new tools easier.
Alongside the simple usability and a modern UI of the solution, this internalization of the story is crucial for the introduction and acceptance. Ultimately, one principle applies to digital solutions: without use, no ROI. The aspects depend on each other:
no ROI story → no acceptance of the solution →
no acceptance of the solution → no ROI
The story must especially contain the following elements:
Clear addressing and naming of the problems and challenges that are the focus
A simple and understandable description of how these problems are to be addressed
A definition of the measurability of success, so that all parties have clear KPIs in mind on which they can base and optimize their actions, and whose gradual improvement keeps them constantly motivated
The overarching contextualization of the pursued goals in a larger context, especially how the initiative helps the company to operate more economically and prepare for the future
An emotional component, describing how the personal situation of the involved parties will improve through the initiative (e.g. by automating tedious tasks)
The ENLYZE Story
In our projects, it is important to us to demonstrate the ROI of our solution in a comprehensible way and to show in what aspects the solutions help to improve a bit every day. This progress should be measurable so that all parties understand whether they are moving collectively toward the defined goals or whether corrective interventions must be made.
In the ENLYZE story, there is thus a measurable and easily articulable core benefit. Furthermore, there are certainly various secondary effects, such as that the traceability of production anomalies becomes easier, KPIs and reports are created and evaluated automatically, and the manual documentation effort is reduced. But at its core lies a straightforward story of continuous improvement in productivity with a defined ROI that serves as a measurement for success and justifies the investment decision. All effects are interdependent and create a comprehensive whole.
Our simple logic aims at reducing process variance and thereby establishing stable, replicable, and efficient production. In three steps, productivity in manufacturing improves (see graphic):
In the first step, the goal is to capture the status quo. Within the first weeks of data recording, it becomes evident how great the process variance is. Initial deductions can already be made from this:
1. How great is the overall productivity potential, and how far can my manufacturing be optimized?
The answer is quite simple: up to the outer edge of the observed productivity, as this level has already been recorded and is thus achievable. If a higher value is measured during further observations, this benchmark will naturally be raised.
2. Do my KPIs (OEE), calculated values from pre- and post-calculation, and from item master data match the produced reality?
These are the basis for the planning, calculation, and organizational improvements of the Smart Factory (named above as secondary effects). In the second step, processes are thus stabilized, and replicability is ensured. After the first analyses and observations, the task is to apply what has been learned in production to reduce process fluctuations. The gained transparency is already used in initial effects to increase performance, and the effects are measurable. Due to the reduced process variance, the necessary rechecks by quality assurance often decrease, and overall defect rates are slightly improved.
Incidentally, better replicability also leads to production control being able to better depict the manufactured reality, thus reducing deviations in planning and controlling. Additionally, sales and production cost calculations can be checked, and if necessary, countermeasures can be implemented.
Only in the third step is productivity raised to an even higher level. Once the processes are stabilized and performance is achieved replicably, process performance can be brought to the limits of achievable productivity levels without jeopardizing defect rates.
In addition to increasing performance, alternative optimization goals can also be explored, allowing for economically sensible decisions regarding the optimization dimension. For instance, it is conceivable to operate tolerances more tightly, leading to the necessity of using less material, reducing resource and water consumption, operating the system as gently as possible, or minimizing carbon footprints.
Measurability is key
For the digitalization of production, many arguments and added values are cited. It is important not to forget that money must be made with the introduction of these technologies. And amidst all the sensible side effects, the investment in digitalization must also pay off. Users, decision-makers, and providers must ask themselves where exactly the added value arises and how a ROI can be represented. They should not rely on an always attackable and thin argumentative basis. Because: a clearly and simply articulable ROI helps significantly in bringing the participants of the digital transformation along in a story about the desired improvement and focusing on a goal. As soon as progress becomes measurable, it also helps to maintain motivation and drive momentum in the change process. Only then can the introduction of digital solutions be successful.
