The Total Economic Impact™ Of UMH

Energy Cost Savings

Evidence and data. Interviewees said UMH-enabled energy monitoring and transparency surfaced avoidable consumption that led to cutting unnecessary energy consumption and reducing costs.

• One interviewee reported that one of their organization’s plants detected high weekend standby draw on a line and an investigation showed that a cleaning/heating routine was running when production was not. The organization shifted the routine to during the work week, leading to a significant reduction in the machine’s energy use.

• Another interviewee reported that within weeks of deployment, UMH exposed abnormal consumption patterns (e.g., leaks, pressure inefficiencies, unexpected equipment loads) and enabled corrective measures that reduced their organization’s overall energy waste.

• Beyond single fixes, interviewees noted there’s value in continuous visibility dashboards that show when consumption deviates from normal to enable targeted action.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• Each of the composite’s site consumes a baseline of 10,000 MWh of energy per year.

• The composite realizes a 5% energy reduction per site after implementing monitoring and basic interventions.

• At sites in Europe, the US, and Asia, the composite pays an average energy rate of €135 per MWh.

• The composite scales UMH across five sites in Year 1, 10 in Year 2 and 15 in Year 3.

Risks. Realized energy savings may vary due to:

• Deployment focus and maturity.

• The organization’s prioritization of energy visualization/actions.

• Local energy prices, seasons, and grid charges, which may alter consumption.

• Limited metering/alerting or weak follow‑through on schedules and shutdown routines.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of €1.5 million.

Labor Cost Savings

Evidence and data. Interviewees described substantial labor savings from eliminating manual machine inspections and manual meter readings that previously required significant recurring time investments.

• Multiple interviewees stated that prior to UMH, their organizations manually collected energy and machine data, often by physically walking the shop floor with paper forms to document meter values or machine status. The organizations replaced this manual process with automated data ingestion.

• One interviewee highlighted that UMH automatically captures meter readings that previously required weekly physical rounds and said the automation eliminated extensive recurring effort, replacing what used to be a regular, time‑consuming task.

• Another interviewee reported that before UMH, their organization needed one person-day per week for manual data acquisition alone because teams collected, entered, and prepared consumption and machine data. But they said with UMH automation, employees only need a few clicks to view the data on the dashboard.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• Two employees at each site perform weekly inspection cycles, amounting to 52 machine inspections at each site per year.

• UMH automation reduces the effort required per inspection by 10 hours.

• Previously at each site, manual meter readings consumed four days per month, amounting to 384 labor hours per site per year.

• The average fully burdened hourly rate for an employee involved in meter readings is €50.

• The composite scales UMH across five sites in Year 1, 10 in Year 2, and 15 in Year 3.

Risks. Labor savings may vary across organizations due to:

• Whether the plant already uses partial automation, which may lead to smaller incremental savings.

• Hourly employee rates.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of €1.5 million.

Operational Efficiency Improvements

Evidence and data. Interviewees said UMH improved operational transparency by enabling faster detection of machine issues, clearer visibility into downtime drivers, and more effective performance management.

• Interviewees explained that UMH introduced real‑time dashboards that allowed teams at plants that previously had no visibility into machine stoppages to see exactly when and why machines had been at a standstill. One interviewee noted that before UMH, downtime was a “black box,” with no information about when machines had stopped or for how long.

• Interviewees said shop‑floor leaders reported faster situational awareness. Morning production meetings now start with immediate insight into the previous shift’s performance, including issues that occurred overnight. This shortened response cycles and elevated the importance of resolving recurring issues.

• One interviewee said their organization now tracks previously undocumented stoppages and uses them for regular reporting to leadership. They explained that visibility alone has driven operators and team leaders to pay much closer attention to how machines are utilized, which reduces avoidable nonproductive time.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• Each site experiences 400 hours of unplanned downtime per year.

• Improved diagnostic visibility, root‑cause transparency, and shop‑floor responsiveness reduces the composite’s downtime by 14%.

• The composite experiences €50,000 of lost revenue for each hour of unplanned downtime.

• The composite’s operating margin is 12%.

• The composite scales UMH across five sites in Year 1, 10 in Year 2, and 15 in Year 3.

• The composite attributes 50% of the improvement to UMH.

Risks. Operational improvements and downtime reductions may vary due to:

• The organization’s industry and maturity of operations, which may affect the amount of unplanned downtime.

• Whether operators and team leads conduct consistent root‑cause analysis and corrective follow‑up.

• Whether the organization has plants with mature MES/supervisory control and data acquisition (SCADA) solutions, which may lead to smaller incremental gains.

Results. To account for these risks, Forrester adjusted this benefit downward by 2%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of €3 million.

Unquantified Benefits

Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:

• Strong vendor partnership, rapid implementation, and learning support. Multiple interviewees said UMH’s implementation speed is “unparalleled” and noted UMH experts were live at first sites at a time when they believe other vendors would not have even started projects of similar scope. They said UMH experts have an engaged and flexible working style and support a wide variety of topics including data modeling, containerization, network segmentation, unified namespace (UNS) concepts, and architectural best practices. Interviewees shared that the experts helped surface topics they hadn’t even thought about. One interviewee said: “[UMH elevated internal understanding of the UNS concept] from baseline up to the CEO level.”

• Reduced lock‑in risk and strengthened long‑term technology freedom. Interviewees stressed that UMH’s open-source technology stack allows their organizations to avoid the vendor lock‑in often associated with proprietary manufacturing platforms. One interviewee expressed they are very happy their organization is not exposed to what they described as “subscription‑model cost traps” associated with some closed ecosystems, and they also said the architecture is “future‑proof” and adaptable as new tools emerge. Interviewees also highlighted they believe there’s value in the open and collaborative nature of the UMH ecosystem, noting that regular exchange with the UMH team and broader community enabled the sharing of best practices, discussion of real‑world challenges, and more.

• Improvements to data integrity and security. Interviewees explained that before UMH, teams at their organizations heavily relied on paper-based logs, manual meter rounds, and locally stored spreadsheets, which are practices they described as fragmented, error‑prone, and lacking transparency. They also said UMH centralized data capture, removed manual data transfers, and replaced opaque processes with a unified data platform, and that consolidation reduced sources of failure and created more secure, consistent data environments.

• Increased autonomy for business and shop floor teams. Interviewees reported that team leads, operators, and digitalization managers gained the ability to independently access real‑time production information (e.g., machine states, downtime events, shift performance) without waiting for IT-generated reports. Morning shop‑floor meetings became faster and more informed, and plants previously lacking any MES‑like visibility gained immediate operational insight. Some interviewees also noted that UMH shortened training time to as little as 30 minutes for operators, further reducing IT dependency and increasing workforce enablement.

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement UMH and later realize additional uses and business opportunities, including:

• Expanding to multiple use cases. Several interviewees stated their organization implemented UMH for a single, clearly defined use case (e.g., energy monitoring driven by regulatory requirements, basic OEE transparency) and then quickly discovered broader applicability. But those interviewees said their organizations have since expanded or plan to expand use of UMH to additional areas (e.g., maintenance insights, downtime transparency, logistics signals). They described UMH evolving into a central data platform for production data, with new use cases added incrementally as needs emerged.

• Using UMH as a scalable foundation for automation and AI. Interviewees consistently referenced UMH as an enabling data foundation rather than a fixed-point solution. Several explicitly discussed their organization’s future plans for predictive maintenance, machine‑learning models, or advanced analytics they said are not yet live but are now feasible because UMH standardized and contextualized production data. One interviewee noted that UMH created the clean data foundation that was required before their organization could realistically apply AI, and this allowed the company to defer specific advanced use cases while still future‑proofing its architecture.

• Replacing or consolidating legacy systems. Interviewees reported their organizations plan to replace some legacy systems and tools with UMH. One interviewee stated their company plans to replace an aging MES system, and another said UMH effectively became for production what their ERP represents on the commercial side. Interviewees said they value having the flexibility to gradually decommission legacy systems instead of executing an MES replacement.

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).