How to Assess the Business Value of Your Digital Enterprise Maintenance Investments

Author: John Soldatos
Category: Enterprise Maintenance and Reliability

The fourth industrial revolution unveils a host of opportunities for improving your industrial maintenance activities, including the possibility to deploy and fully leverage predictive maintenance (PdM). The deployment of PdM is likely to require significant investments, including investments in hardware (e.g., sensors networks for automated data collection) and software (e.g., deep learning packages and asset management).

It may also become costly to procure consultants on BigData and Industrial IoT technologies. As a result, justification for these investments is at the forefront, as a means of convincing senior management to include these investments at the top of the corporate project portfolio.

Industrial organizations are looking into ways to calculate the Return On Investment (ROI) of any maintenance project. To determine the ROI, there are a number of calculator tools. ROI calculators provide a sound basis for understanding and quantifying the merits of an enterprise maintenance project, yet managers and maintenance engineers should be aware of associated limitations.

Understanding Capital Budgeting

Chief Financial Officers (CFOs) and financial managers are usually keen on using capital budgeting methods to assess the financial benefits and the overall profitability of investments. Most of the capital budgeting methods require a thorough consideration of the costs and the benefits of an investment.

In practice, the costs are known (or can be estimated) with high accuracy, while the benefits are 'approximated' in. This can result in uncertainty about the credibility of the outcomes.

As far as the costs are concerned, it is important to make sure that all the expenses are listed, including infrastructure, software, energy, labor and downtime costs. This means that the Total Cost of Ownership (TCO) of a new system or investment should be taken into account.

This can be quite challenging when costs are either overlooked or assumed.

Likewise, all projected benefits should be considered as well, including cost savings and revenue streams. Although benefits estimations may not be very accurate, they should be based on sound assumptions for operating a new system and its monetary performance.

An important element of any capital budgeting calculation is the time frame during which costs and benefits are taken into account.

For IT systems like asset management and enterprise systems, a period of five years is considered reasonable, given that this is a commonly used depreciation period for IT equipment and services.

However, the ROI of infrastructure investments is likely to be computed over rougher time scale estimates. Regardless of time frame, both capital budgeting and ROI calculation should take into account the “time value for money”—the fact that one dollar spent today will not equal a dollar spent three or four years later, due to inflation. As a result, when computing capital budgeting metrics, it’s always important to know if the estimates account for the time value of money.

With a detailed list of costs and benefits, various capital budgeting metrics can be calculated. In principle, these calculations require a good understanding of finance. However, most of the indicators are available as built-in functions in popular spreadsheet programs.

It’s important to note the following key metrics:

Once computed, these indicators can be used to understand the value of investments, but also to compare alternative investments. For example, the NPV and the payback period provide good estimates about the monetary value of the investment in a specified time frame. At the same time, the IRR can be used to compare two or more alternative investments as a means of prioritizing them in the company’s project portfolio.

Capital Budgeting for enterprise maintenance: Costs and expected benefits

The calculation of the above-listed indicators for maintenance projects is a matter of understanding and estimating their benefits and costs. On the cost side, the following parameters should be considered:

Benefit estimations are much more challenging to capture. This is due to the multi-faceted benefits of predictive maintenance. There’s also the fact that complex calculations might be needed in order to translate maintenance parameters to monetary values.

In principle, benefits will stem from improvements in the following areas:

Mapping each of the above benefits into monetary value may involve several reasonable assumptions (e.g., regarding labor cost, purchase prices, machines utilization and more), along with complex calculations involving advanced statistics.

This is the reason why such calculations require an effective collaboration between the finance and accounting department, maintenance practitioners and engineers. Maintenance literature is full of research papers with practical formulas for quantifying the benefits of maintenance.

Limits and Challenges of using Capital Budgeting in enterprise maintenance

Calculating financial indicators such as IRR and NPV is a very useful tool for prioritizing maintenance-related investments and making educated decisions. Nevertheless, it’s very important to understand the limitations of capital budgeting methodologies:

Despite significant efforts spent on producing ROI calculators and formulas for quantifying the benefits of digital maintenance projects, the estimation of the business value of maintenance systems (such as PdM) remains challenging.

In several cases, ROI calculation is as much art as science. No matter the methodology and tools used, it’s always good to understand how these tools work behind the scenes. We hope that we were able to shed some light on this, and is of use in your next enterprise maintenance investment-related capital budgeting assignment.

Author: John Soldatos

John Soldatos holds a Phd in Electrical & Computer Engineering. He is co-founder of the open source platform OpenIoT and has had a leading role in over 15 Internet-of-Things & BigData projects in manufacturing, logistics, smart energy, smart cities and healthcare. He has published more than 150 articles in international journals, books and conference proceedings, while he has authored numerous technical articles and blogs posts in the areas of IoT, cloud computing and BigData. He has recently edited and co-authored the book “Building Blocks for IoT Analytics”.

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