October 2023
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Mixed reality (MR) technologies enable modern workforces and are a key pillar for organizations who are embracing the fourth industrial revolution (Industry 4.0).1 Hands-free, immersive 3D overlays and visualizations provide manufacturing organizations with numerous opportunities to drive value with more effective training and enable remote collaboration to solve complex manufacturing issues. Mixed reality contains elements of virtual and augmented reality but focuses on holography. The user is sufficiently immersed to clearly see and interact with digital models and data, but not so immersed that they cannot do their work in the field. The transparent nature of the device allows the user to continue doing their work while also having a relatively realistic experience of the data, information, or models.2 MR has become an important tool in the toolbox for manufacturing organizations to deliver value and solve critical problems when they arise.
Microsoft commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying HoloLens 2 with mixed reality applications.3 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of HoloLens 2 with first-party mixed reality applications on their organizations. It should be noted that the mixed reality applications discussed in this study are specifically Microsoft Dynamics 365 Guides (Guides) and Microsoft Dynamics 365 Remote Assist (RA), and not any commercial third-party or internally developed applications. While HoloLens 2 with mixed reality applications is applicable to numerous sectors, the focus of this study is on the ROI and benefits to manufacturing organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed eight representatives in manufacturing companies with experience using Microsoft HoloLens 2 with Dynamics 365 Guides and Dynamics 365 Remote Assist. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is a global manufacturing corporation with an Industry 4.0 focus, generating $18 billion in annual revenues with 120,000 total employees and operating 20 advanced factories. The composite organization for a TEI is a hypothetical organization that intentionally does not reflect any specific customer but encompasses the experiences of all the interviewed customers. The financial model for the TEI is tailored for a company that has the attributes of the composite organization.
Interviewees noted that, prior to using Microsoft HoloLens 2 with mixed reality applications (HoloLens 2 with MRApps), more employees had to travel to solve any problem that on-site teams could not fix, and that their organizations wanted to increase training efficiency and create better documentation that improved manufacturing and training processes.
After the investment in HoloLens 2 with MRApps, the interviewees’ organizations enabled users to receive hands-free instruction on the manufacturing line; provided Guides to create virtual instructions that technicians could use to solve problems and complete common repairs that would have otherwise required help from a specialist; and enabled expert technicians to remotely assist on-site work with complex tasks with Remote Assist.
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include the following:
For , this benefit could be worth over three years.
For , this benefit could be worth over three years.
For , this benefit could be worth over three years.
For , this benefit could be worth over three years.
For , this benefit could be worth over three years.
For , this benefit could be worth over three years.
For , this benefit could be worth over three years.
For , this benefit could be worth over three years.
Qualitative benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
For , with devices in Year 3, this cost could be over three years.
For , with licensed users in Year 3, this cost could be over three years.
For , this cost could be over three years.
For , this cost could be over three years.
Results. The representative interviews and financial analysis found that a composite organization experiences benefits of $21.39 million over three years versus costs of $6.76 million, adding up to a net present value (NPV) of $14.62 million and an ROI of 216%.
could experience benefits of over three years versus costs of , adding up to an NPV of and an ROI of .
“We have a digital initiative for Industry 4.0. There’s an industry trend for that. And so, I see new technology and being innovative is key to our survival as a company. And there are four main technologies right now on the roadmap and XR is one of them.”
— Senior manager, engineer services support process systems development, transportation manufacturing
Return on investment (ROI):
Benefits PV:
Net present value (NPV):
Payback (months):
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment HoloLens 2 with MRApps.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that HoloLens 2 with MRApps can have on an organization.
Interviewed Microsoft stakeholders and Forrester analysts to gather data relative to HoloLens 2 with MRApps.
Interviewed eight representatives at organizations using HoloLens 2 with MRApps to obtain data about costs, benefits, and risks.
The composite organization for a TEI is a hypothetical organization that intentionally does not reflect any specific customer but encompasses the experiences of all the interviewed customers. The financial model for the TEI is tailored for a company that has the attributes of the composite organization.
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewed organizations.
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Readers should be aware of the following:
This study is commissioned by Microsoft and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in HoloLens 2 with MRApps.
Microsoft reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Project Leads: Roger Nauth, Erach Desai
Consulting Team: Otto Leichliter
Role | Industry | Revenue | Employees | Number Of HL 2 Devices |
---|---|---|---|---|
Director of augmented engineering services | Pharmaceuticals manufacturing | >$35 billion | >300,000 | >500 |
Department manager, field tech services | Automotive manufacturing | >$20 billion | >125,000 | >400 |
Lead solutions engineer intelligent automation and AIOps infrastructure services | Power management manufacturing | >$15 billion | >175,000 | >250 |
Senior manager, engineer services support and process systems development | Transportation manufacturing | >$25 billion | >225,000 | 50 |
Senior training program manager | Semiconductor supplier | >$8 billion | >800,000 | 500 |
Engineering project lead | Semiconductor manufacturing | >$10 billion | >90,000 | >200 |
Tech fellow | Aeronautics manufacturing | >$15 billion | >125,000 | >250 |
Senior advanced mechanical design engineer | Manufacturing conglomerate | >$35 billion | >350,000 | 1 |
The interviewees noted how their organizations struggled with common challenges, including the following:
“HoloLens 2 allows you to see through the eyes of another person … So that’s why we decided to use the telepresence device only for gamba recordings and HoloLens 2 for the actual production phase of the product.”
Senior advanced mechanical design engineer, manufacturing conglomerate
“We’re teaching people how to maintain the tools and we’re teaching them somewhat in the classroom and somewhat on the tools. So, in the classroom, it’s PowerPoint like it is everywhere. And he’s like, maybe we need to make that better.”
Engineering project lead, semiconductor manufacturing
The interviewees’ organizations searched for a solution that could:
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the eight interviewees at manufacturing companies, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The global manufacturing corporation generates $18 billion in revenues and has 120,000 total employees, and 1,000 field technicians. It also operates 20 factories with approximately 60,000 factory employees, including 6,000 skilled technicians.
Description of . In Year 1, generates in revenues and has total employees, including field technicians. also operates factories with approximately factory employees, including skilled technicians. might grow modestly in Years 2 and 3.
Deployment characteristics. The composite organization enhances the training of technicians, enables field troubleshooting, and brings new factories online, among several other value-creating activities and tasks. Due to the relative novelty, training, and expense of the HoloLens 2 for MRApps solution, the composite organization has a deployment ramp for rolling out the technology:
For , an optimal configuration of HoloLens 2 devices might be devices in Year 1, devices in Year 2, and devices in Year 3.
The modeled configuration of HoloLens 2 devices at is devices deployed in Year 1, in Year 2, and in Year 3. In addition, licenses active users in Year 1, in Year 2, and in Year 3.
Key modeling assumptions. To quantify the economic and productivity benefits that the composite organization incurs with the deployment of the HoloLens 2 MRApps solution, Forrester uses the following set of assumptions in the financial model (that are shown and calculated in the accompanying reference tables R and S):
has fully operational factories in Year 1.
Assuming modest growth, might have factories in Year 2 and in Year 3.
At , each factory operates per week.
In Year 1, employs people overall with factory employees and skilled technicians. might growth modestly in Years 2 and 3.
For , factory production might generate in revenue in Year 1 (with each factory generating in revenue). might growth modestly in Years 2 and 3.
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
---|---|---|---|---|---|
R1 | Fully operational factories | ||||
R2 | Shifts per week | ||||
R3 | Weeks of production time per year | ||||
R4 | Length of each shift (hours per week) | ||||
R5 | Annual hours of factory operation | R2*R3*R4 | |||
R6 | Production lines per factory | Composite | |||
R7 | Manually operated machine stations per production line | Composite | |||
R8 | Field service locations | ||||
HoloLens 2 devices deployment (including compatible licensing of Guides and Remote Assist MR software) | |||||
R9 | HL2 devices deployed — optimal configuration | ||||
R10 | HL2 devices deployed — actual configuration | ||||
R11 | Deployment ramp of Microsoft’s HL2 MR solution — for operations | ||||
R12 | Deployment ramp of Microsoft’s HL2 MR solution — for training | R10/R10Y3 | |||
R13 | Licensed RA and Guides users |
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
---|---|---|---|---|---|
S1 | Total employees | ||||
S2 | Factory employees | ||||
S3 | Skilled factory technicians | ||||
S4 | Field technicians | ||||
S5 | Manufacturing SMEs | ||||
S6 | Annual revenue | ||||
S7 | Revenue from factory production | S6*80% | |||
S8 | Revenue per factory | S7/R1 | |||
S9 | Revenue per factory per hour | S8/R4 | |||
S10 | Revenue per production line per hour | S9/R5 | |||
S11 | Revenue per factory employee | S7/S2 | |||
S12 | Revenue per factory employee per hour | S10/R3/R4 | |||
S13 | Revenue per skilled factory technician | S7/S3 | |||
S14 | Revenue per skilled factory technician per hour | S12/R3/R4 |
Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
---|---|---|---|---|---|---|
Atr | Training productivity improvement and materials cost savings | |||||
Btr | Improved revenues from training effectiveness | |||||
Ctr | Manufacturing operations productivity improvement | |||||
Dtr | Revenue assurance from avoided production downtime | |||||
Etr | Field technician productivity improvement | |||||
Ftr | Manufacturing SME productivity improvement | |||||
Gtr | Improved time to revenue for new factories ramping online | |||||
Htr | Overall travel and incidentals savings | |||||
Total benefits (risk-adjusted) |
Evidence and data. Prior to the deployment of Microsoft’s HoloLens 2 mixed reality (MR) solution, interviewees’ organizations trained technicians — especially skilled manufacturing technicians — with traditional approaches, such as in-class PowerPoint presentations, training videos, and on-hands instruction on expensive and limited machines. Most interviewees cited how Microsoft’s MR solution enabled self-guided mixed reality instructions, leveraging 3D models, simulations, and real-world overlays to enable factory technicians to learn faster, understand content better, and practice machine-specific skills. Technicians could also observe and participate in remote demonstrations and be evaluated for certification, saving time and expense. Some interviewees described how creating digital twins of these sophisticated machines enabled them to reduce virtual, hands-on training without disrupting production time. It should be noted that training of field technicians (an important cohort, but not critical to manufacturing operations) is not included here.
As the expenses associated with developing training materials for MR technology are included in the Cost section, the corresponding cost savings of not having to produce traditional training materials is the second component of this benefit.
This specific training benefit speaks to the amount of training effort or cost that was reduced when the interviewees’ organizations deployed Microsoft’s MR solution. The key metric described by most interviewees was the reduced overall training time for skilled manufacturing technicians, also referred to as the door-to-floor reduction in time compared to their prior states. Interviewees shared many examples of training productivity improvement and materials cost savings benefits, along with data insights, including the following:
“They have their time with the trainer and they’re able to kind of use the HoloLens like a crutch. So, maybe they forgot the second step of the process. They can use the HoloLens to be like, ‘OK, this is exactly what I’m doing.’ And then they kind of go back and forth between it and it creates that muscle memory for them, and they do a really good job in terms of catching on quicker.”
Lead solution engineer, power management manufacturing
24%
Improvement in training productivity in Year 3
35%
Reduction in training materials expenses in Year 3
Modeling and assumptions. The focus of this benefit is training productivity improvement enabled with Microsoft’s MR solution with the key metric being the reduction in training time for skilled manufacturing technicians — both new hires, and existing technicians being trained on new equipment. Also included is the saving in materials costs compared to the prior state of training. Forrester modeled the impact for the composite organization assuming the following:
For , of existing manufacturing technicians may need incremental training in Year 1.
Risks. The expected financial impact is subject to risks and variation based on several factors, including the following:
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.91 million.
For , this benefit might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
A1 | Existing skilled factory technicians | S3-S3*2.5% | ||||
A2 | Training hours per existing skilled factory technician | Interviews | ||||
A3 | New skilled factory technicians hired | S3-S3PY+S3PY*5% | ||||
A4 | Training hours per new skilled factory technician | Interviews | ||||
A5 | Total technician training hours before Microsoft HL2 MR solution | A1*A2+A3*A4 | ||||
A6 | Reduction in training time with Microsoft HL2 MR solution | Interviews and survey | ||||
A7 | Total technician training hours saved with Microsoft HL2 MR solution deployed | A5*A6 | ||||
A8 | Fully burdened hourly salary of average manufacturing technician | TEI standard | ||||
A9 | Deployment ramp of Microsoft HL2 MR solution for training | R12 | ||||
A10 | Subtotal: Training productivity improvement | A7*A8*A9 | ||||
A11 | Annual trainees | A1+A3 | ||||
A12 | Cost of training materials and consumables per trainee | Interviews | ||||
A13 | Avoided training materials expenses with Microsoft HL2 MR solution | Survey | ||||
A14 | Subtotal: Training materials cost savings due to Microsoft HL2 MR solution | A11*A12*A13 | ||||
At | Training productivity improvement and materials cost savings | A10+A14 | ||||
Risk adjustment | ↓15% | |||||
Atr | Training productivity improvement and materials cost savings (risk-adjusted) | |||||
Three-year total: | Three-year present value: |
Base: 306 decision-makers working as operations professionals, frontline workers, or training/teaching/research employees in the architecture and engineering construction (AEC), education, healthcare, or manufacturing industries who use mixed reality headsets for their organizations, which have at least 1,000 employees. Varies from total base of 312 due to some respondents opting to not respond to this question.
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, August 2023
Evidence and data. As noted in the previous benefit, prior to the deployment of Microsoft’s HoloLens 2 MR solution, interviewees’ organizations trained skilled manufacturing technicians with traditional approaches. With Microsoft’s MR solution, several interviewees described how creating digital twins of these sophisticated machines enabled them to achieve faster door-to-floor time specifically for new technicians basically, cutting down overall training time compared to how training was conducted previously. Doing so resulted in not only improved training productivity that we already discussed, but also improved time to revenue for existing factories bringing on a steady stream of new hires.
In addition to the faster ramp for new trainees, interviewees emphasized the improvement in the quality of training with Microsoft’s MR solution. Hands-on training with a digital twin would be akin to flight simulator training for a pilot. Some interviewees described this improvement in quality in terms of reduced scrap materials, anecdotal observations, and aspirational goals. This improved quality benefit applied to all skilled technicians delivering higher-quality products (improved yield) by being trained on the Microsoft MR solution.
This benefit discusses the amount of training effort or cost that was reduced when the interviewees’ organizations deployed Microsoft’s MR solution. The key metric described by most interviewees was the reduced overall training time for these skilled manufacturing technicians or the door-to-floor reduction in time compared to the prior state. As the expenses associated with developing training materials for MR technology are included in the cost section, the corresponding cost savings of not having to produce traditional training materials is the second component of this benefit. Interviewees shared examples of door-to-floor productivity improvement, along with data insights, including the following:
24%
Reduction in door-to-floor time for new technicians trained with HoloLens 2 with MR Apps by Year 3
2.5%
Minimum improvement in production quality enabled with the Microsoft MR solution
“We build out digital twins, a replica at scale of a piece of equipment. The trainees are introduced to HoloLens’s understanding of what the equipment is. I’m a huge proponent of the what and why prior to going into something.”
Director of augment engineering services, pharmaceuticals manufacturing
Modeling and assumptions. The focus of this benefit is faster time to revenue enabled with Microsoft’s MR solution, with the key metric being the reduction in door-to-floor training time for newly hired skilled manufacturing technicians. The second component of this benefit is the improvement in revenues due to the production of higher-quality products as a function of higher-quality training with MR technology. Forrester modeled the impact for the composite organization assuming the following:
has new technicians who require training in Year 1.
Based on the information provided, the revenue generated per skilled technician at is per hour in Year 1.
Based on the information provided, the annual revenue per factory employee at is computed to be in Year 1.
Risks. The expected financial impact is subject to risks and variation based on several factors, including the following:
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $4.5 million.
For , this benefit might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
B1 | New skilled factory technicians hired | A3 | ||||
B2 | Training hours per new skilled factory technician | A4 | ||||
B3 | Reduction in training time with Microsoft HL2 MR solution | A6 | ||||
B4 | Additional production hours for new technicians from faster door-to-floor | B1*B2*B3 | ||||
B5 | Revenue per skilled factory technician per hour | S14 | ||||
B6 | Adjusted revenue contribution for skilled factory technicians | TEI standard | ||||
B7 | Deployment ramp of Microsoft HL2 MR solution for training | R12 | ||||
B8 | Subtotal: Faster time to revenue for new technicians | B4*B5*B6*B7 | ||||
B9 | Annual trainees | A11 | ||||
B10 | Improved quality due to more effective training with Microsoft HL2 MR solution | Interviews | ||||
B11 | Annual revenue per factory employee | S11 | ||||
B12 | Deployment ramp of Microsoft HL2 MR solution for operations | R11 | ||||
B13 | Subtotal: Improved revenue from higher quality based on more effective training | B9*B10*B11*B12 | ||||
B14 | Operating margin | |||||
Bt | Improved revenues from training effectiveness | (B8+B13)*B14 | ||||
Risk adjustment | ↓15% | |||||
Btr | Improved revenues from training effectiveness (risk-adjusted) | |||||
Three-year total: | Three-year present value: |
Evidence and data. Prior to the deployment of Microsoft’s HoloLens 2 MR solution, manufacturing technicians typically had to refer to instruction manuals and reference charts during their day-to-day tasks of operating machines during production time. Most interviewees discussed the productivity benefits enjoyed by these technicians when having access to HoloLens 2 lenses and Guides software to see and manipulate advanced instructions, schematics, and HoloLens vital information that is overlaid on the real world, while remaining head-up and hands-free. Again, the value of Microsoft’s MR solution was most impactful for the skilled technicians working with advanced and sophisticated manufacturing equipment, who were higher-compensated workers compared to regular factory workers.
Interviewees noted that technicians also used remote collaboration applications, such as Remote Assist, to quickly get support from peers or experts, especially for troubleshooting. While there was an aspect of easier access to subject matter experts (SMEs) on sophisticated manufacturing equipment for these skilled technicians, the bulk of that benefit accrued to the manufacturing SMEs and is modeled in benefit F.
This benefit addresses the manufacturing productivity improvement for skilled technicians deploying Microsoft’s MR solution. The key metric extracted from the survey was the increase in task efficiency for these skilled manufacturing technicians, as it applies to tasks that could be enhanced with MR technology.
The director of augmented engineering services working in pharmaceuticals manufacturing said: “If you think about it, you’re investing in the operator. You give them the information they need when they need it. You’re giving them the ability to call for help even if they still have questions about whatever that task is. It’s been extremely beneficial.”
20%
Increase in task efficiency for skilled technicians during manufacturing operations by utilizing HoloLens 2 with MRApps
“You can appreciate the potential cost savings there is by making the right decision instead of the wrong decision when it comes to manufacturing that. They’re now all over the possibilities that they have now that they can build anything they want virtually.”
Engineering project lead, semiconductor manufacturing
Modeling and assumptions. The focus of this benefit is manufacturing productivity improvement enabled with Microsoft’s MR solution with the key metric being increased task efficiency for skilled manufacturing technicians for ongoing operations enabled with MR technology. This benefit does not attempt to capture any productivity benefit for faster resolution of downtime or troubleshooting incidents, as that value is calculated in benefit D. Forrester modeled the impact for the composite organization assuming the following:
Based on the information provided, starts with skilled technicians in Year 1. might have skilled technicians in Year 2 and skilled technicians in Year 3.
Based on the information provided, the deployment ramp for the Microsoft MR technology at is as described in row C2.
Risks. The expected financial impact is subject to risks and variation based on several factors, including the following:
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.21 million.
For , this benefit might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
C1 | Skilled factory technicians | S3 | ||||
C2 | Deployment ramp of Microsoft HL2 MR solution for operations | R11 | ||||
C3 | Number of technicians with access to HL2 MR solution | C1*C2 | ||||
C4 | Percentage of tasks enhanced with MR | Interviews | ||||
C5 | Increase in task efficiency with MR | Survey | ||||
C6 | Working hours per skilled factory technician | R3*R4 | ||||
C7 | Fully burdened hourly salary of average manufacturing technician | A8 | ||||
Ct | Manufacturing operations productivity improvement | C3*C4*C5*C6*C7 | ||||
Risk adjustment | ↓15% | |||||
Ctr | Manufacturing operations productivity improvement (risk-adjusted) | |||||
Three-year total: | Three-year present value: |
Evidence and data. Irrespective of products produced or manufacturing technology, interviewees noted that the process of manufacturing complex products with sophisticated equipment was fraught with risks of downtime. Planned downtime generally refers to time scheduled for equipment maintenance and process upgrades, etc. Unplanned downtime could be especially pernicious and potentially crippling for the interviewees’ organizations. Sophisticated machinery could fail, break down, or sometimes need to be recalibrated during operational runtime. This was above and beyond any power failure or network performance degradation. Before the adoption of Microsoft’s HoloLens 2 MR solution, when interviewees’ organizations experienced equipment-related downtime, the manufacturing SME on the machine (an in-house expert) needed to be called and — more than 50% of the time — had to come in to evaluate the situation. The SME would then have to get on the phone with an expert from the vendor, who produced the specific equipment and diagnose the problem. In the most extreme of cases, the vendor’s expert might have to travel in or the equipment would have to be swapped out. Needless to say, countless hours could be spent merely diagnosing the root cause of the problem.
With the introduction of Microsoft’s MR solution, expert technicians were able to use remote collaboration applications, such as Remote Assist, to quickly get support from manufacturing SMEs for equipment troubleshooting. Faster troubleshooting for nonmaintenance machine downtime enabled faster time to revenue for existing factories. Clearly, MR technology could not avoid or fix the problem, but it enabled the interviewees’ organizations to communicate efficiently and cost-effectively to get the issue resolved.
This benefit speaks to the faster troubleshooting for nonmaintenance machine downtime resulting in faster back to production time when the interviewees’ organizations deployed Microsoft’s MR solution. The key metric described by most interviewees was the reduced downtime for factory equipment compared to the prior state. Interviewees shared examples of faster back to production from equipment troubleshooting, including the following:
23.5%
Reduction in downtime caused by manufacturing equipment issues due to remote SME intervention using HoloLens 2 with Remote Assist
“I would say on average prior to HoloLens two and half to three days per incident versus how I can connect at any given time from any given place and potentially solve this within 30 minutes to an hour.”
Senior manager, engineer services support process systems development, transportation manufacturing
Modeling and assumptions. This benefit focuses on the faster back-to-production timeframe for troubleshooting of manufacturing operations enabled with Microsoft’s MR solution. The key metric is the reduction in factory downtime caused by equipment malfunction or failure. Forrester modeled the impact for the composite organization assuming the following:
Based on the information provided, might experience downtime incidents in Year 1, downtime incidents in Year 2, and downtime incidents in Year 3.
Based on the information provided, the deployment ramp for the Microsoft MR technology at is as described in row D5.
Based on the information provided, the single factory revenue per hour for is computed to be in Year 1.
Risks. The expected financial impact is subject to risks and variation based on several factors, including the following:
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.04 million.
For , this benefit might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
D1 | Critical incidents impacting factories | |||||
D2 | Hours of downtime per incident before Microsoft HL2 MR solution deployed | Interviews | ||||
D3 | Reduced downtime for factory equipment with use of Microsoft HL2 MR solution | Interviews and survey | ||||
D4 | Hours of downtime per incident after Microsoft HL2 MR solution deployed | D2*(100%-D3) | ||||
D5 | Deployment ramp of Microsoft HL2 MR solution for operations | R11 | ||||
D6 | Revenue per factory per hour | S9 | ||||
D7 | Percent of individual factory production potentially disrupted per incident | |||||
D8 | Fully operational factories | R1 | ||||
D9 | Operating margin | B14 | ||||
Dt | Revenue assurance from avoided production downtime | D1*(D2-D4)*D5 *D6*D7*D8*D9 | ||||
Risk adjustment | ↓15% | |||||
Dtr | Revenue assurance from avoided production downtime (risk-adjusted) | |||||
Three-year total: | Three-year present value: |
Evidence and data. Interviewees noted that prior to the deployment of Microsoft’s HoloLens 2 MR solution, field technicians typically had to refer to instruction manuals and reference charts during the course of their day-to-day tasks of installing and servicing manufactured products in the field. Interviewees discussed the productivity benefits enjoyed by these field technicians when having access to Microsoft’s MR solution using head-up, hands-free instructions enhanced by detailed visualizations overlaid on the real world. Real-time remote collaboration enabled “see what I see” assistance from and with manufacturing SMEs to complete work or resolve issues beyond the workers’ expertise without requiring extra trips. Field workers consequently saved time, prevented errors and rework, avoided excess trips, and increased their capacity.
This benefit addresses the productivity improvement for field technicians deploying Microsoft’s MR solution. The key metric extracted from the survey was the increase in task efficiency for these field technicians, as it applies to the tasks that can be enhanced with MR technology.
The department manager of field services in automotive manufacturing said that “Ultimately, it comes down to time to resolution of the customer. If he can cut it from four or six days down to one day or two days, it’s a benefit for all, but ultimately, it’s a benefit of customer experience.”
25%
Increase in task efficiency for field technicians utilizing MR solution for tasks that can be enhanced with MR technology
“The idea is there if you have a less experienced technician, maybe didn’t have a chance to go to a training session or we have certain repairs, they’re more difficult than others. We can quickly create a guide and an individual user can use it and again, fix the car, and get it back to the customer.”
Department manager, field tech services, automotive manufacturing
Modeling and assumptions. The focus of this benefit is productivity improvement for field workers servicing or upgrading manufactured products with the key metric being increased task efficiency for field technicians enabled by MR technology. Forrester modeled the impact for the composite organization assuming the following:
Based on the information provided, starts with field technicians in Year 1. might have field technicians in Year 2 and field technicians in Year 3.
Based on the information provided, the deployment ramp for the Microsoft MR technology at is as described in row E2.
“We can use technology to see what the technician sees [and] in a certain percentage of those cases, we can resolve without physically going there and obviously fix the car and get it back to the customer. There’s our initial number-one use case one.”
Department manager, field tech services, automotive manufacturing
Risks. The expected financial impact is subject to risks and variation based on several factors, including the following:
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.76 million.
For , this benefit might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
E1 | Field technicians | S4 | ||||
E2 | Deployment ramp of Microsoft HL2 MR solution for operations | R11 | ||||
E3 | Field technicians with access to HL2 MR solution | E1*E2*120% | ||||
E4 | Typical field technician available working hours per year | |||||
E5 | Percentage of tasks enhanced with MR | Interviews | ||||
E6 | Increase in task efficiency with MR | Survey | ||||
E7 | Annual hours saved per field technician by HL2 MR solution | E4*E5*E6 | ||||
E8 | Fully burdened hourly salary of skilled field technician | TEI standard | ||||
Et | Field technician productivity improvement | E3*E7*E8 | ||||
Risk adjustment | ↓15% | |||||
Etr | Field technician productivity improvement (risk-adjusted) | |||||
Three-year total: | Three-year present value: |
Base: 292 decision-makers working as operations professionals, frontline workers, or training/teaching/research employees in the architecture and engineering construction (AEC), education, healthcare, or manufacturing industries who use mixed reality headsets for their organizations, which have at least 1,000 employees. Varies from total base of 312 due to some respondents opting to not respond to this question.
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, August 2023
Evidence and data. Interviewees reported having specialized experts, or SMEs, that were employees with machine/tool-specific expertise, providing business-critical work and playing key roles in: 1) assisting with manufacturing operations problem-solving and 2) assisting field technicians with product servicing and upgrading. Prior to the deployment of Microsoft’s HoloLens 2 MR solution, interviewees noted their skilled manufacturing and field technicians typically had to refer to instruction manuals and reference charts throughout the course of their day-to-day tasks as noted in benefits C and E. With the introduction of Microsoft’s MR solution, expert manufacturing technicians and field technicians used remote collaboration applications, such as Remote Assist, to quickly get support from manufacturing SMEs for equipment troubleshooting and dealing with product issues in the field. These experts used MR remote support for critical needs to avoid travel, boost capacity, and address needs more efficiently. Interviewees cited a varying range of travel time required for these SMEs in responding to factory downtime incidents — sometimes even days.
This benefit addresses the productivity improvement for SMEs deploying Microsoft’s MR solution to assist both skilled manufacturing and field technicians. The key metric extracted from the survey was the increase in task efficiency for these SMEs, as it applies to the tasks that could be enhanced with MR technology.
The lead solutions engineer of intelligent automation and AIOps infrastructure services in the power management manufacturing industry said: “A lot of time was getting consumed [with the] subject matter expert telling them [what to do]. We wanted to have this onboarding [happen quickly]. We started with digital training. We had the animation of each and every step — how to fix the screw and all the fun stuff.”
30%
Increase in overall task efficiency for manufacturing SMEs with HoloLens 2 with MRApps
“If there’s an issue on the shop floor, you call an engineer. The engineer has to put on his gowns, go into the clean room, whatever. With Remote Assist, he just picks up the phone and launches his laptop and gives assistance remotely from his desk. And then the final use case that we’re starting to do more and more is design reviews. This is something we can build a 3D virtual tool and the design engineers can review their designs in real size and walk around and explore them together.”
Engineering project lead, semiconductor manufacturing
Survey respondents estimate that nearly 30% of specialized expert tasks could be supported by MR, saving each expert an average of 64 hours per task per month.
Modeling and assumptions. There are two primary components of this benefit as it relates to the productivity of SMEs. The first is the time saved in training expert manufacturing technicians, which is the other side of benefit A that accrues to these experts. The second is the collective time saved by avoiding travel to factories (to assist expert technicians with manufacturing equipment issues) and to field locations (to assist field technicians with product-related issues). In addition to the key metric of increased task efficiency for these SMEs, the reduction in training time metric from benefit A also comes into play. Forrester modeled the impact for the composite organization assuming the following:
employs manufacturing SMEs.
Based on the information provided, the deployment ramp for the Microsoft MR technology at is as described in row F2.
Risks. The expected financial impact is subject to risks and variation based on several factors:
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.2 million.
For , this benefit might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
F1 | Manufacturing SMEs | S5 | ||||
F2 | Deployment ramp of Microsoft HL2 MR solution for training | R12 | ||||
F3 | Manufacturing SMEs with access to Microsoft HL2 MR solution | F1*F2 | ||||
F4 | Hours spent on training and instruction per SME | Interviews | ||||
F5 | Reduction in training time with Microsoft HL2 MR solution | A6 | ||||
F6 | Subtotal: Hours saved on training and instruction with HL2 MR solution per SME | F4*F5 | ||||
F7 | Critical incidents impacting factories | D1 | ||||
F8 | Average SME travel hours per critical incident | Interviews | ||||
F9 | Percentage of critical incidents resolved by HL2 MR solution | Composite | ||||
F10 | Increase in task efficiency with MR | Survey | ||||
F11 | Subtotal: Hours saved by avoided time travel due to HL2 MR solution per SME | F7*F8*F9*F10 | ||||
F12 | Total hours saved per SME for training, instruction, and manufacturing operations | F6+F11 | ||||
F13 | Fully burdened hourly salary of manufacturing SME | TEI standard | ||||
Ft | Manufacturing SME productivity improvement | F3*F12*F13 | ||||
Risk adjustment | ↓15% | |||||
Ftr | Manufacturing SME productivity improvement (risk-adjusted) | |||||
Three-year total: | Three-year present value: |
Evidence and data. For the interviewees, building and equipping an Industry 4.0 factory for complex products was a multiyear journey. The construction of the building and infrastructure (e.g., electrical wiring, cabling, water and cooling, etc.) was beyond the scope of this study. Planning the production lines with the sophisticated machinery that has been discussed — the equipping component — came up for discussion during several interviews. In the prior state, such planning required working closely with equipment vendors, estimating size and form-factors, creating 3D models, etc. But the ultimate test came when the equipment arrived at the factory and was physically installed. Interviewees spoke of variances in specifications and human errors in estimating functional flow that led to equipment not fitting, requiring rework (with each engineering change order being upwards of $10,000). Inevitably, this led to delays in getting new factory lines and new factories ramping up production.
With the introduction of Microsoft’s MR solution, creating digital twins of equipment during the factory/line-planning process with HoloLens 2 and RA ensured that machines would fit and would not require engineering change (and thus more cost). With up-front planning, interviewees cited a significant reduction in the effort required to install equipment when building out a new factory line. Plus, the more significant impact on the business was that up-front planning allowed for faster time to revenue for new production lines.
There are two components of this benefit: the reduction in effort required to install machinery for each incremental production line and faster time to revenue for each incremental line. The key metric computed in the model, based on data provided by the interviewees, was more than an 80% productivity boost for manufacturing technicians installing equipment for a new production line (savings in FTEs and number of hours). The faster time to revenues for a new production line was also based on data provided by interviewees. Interviewees shared examples of door-to-floor productivity improvement, along with data insights, including the following:
>85%
Productivity improvement for equipping, installing, and calibrating each new production line by utilizing HoloLens 2 with MRApps for virtual factory planning
“Vendor would initially build their equipment. It will arrive on-site. And then, the plant would discover, ‘Oh, hey, this wasn’t built quite the way we would like it. We want to change these features of this equipment and have that changed.’ Well, the vendor would charge us a change request to be able to make those changes. And so, by being able to utilize the HoloLens, we essentially were able to eliminate some of those potential change request that would have happened.”
Lead solution engineer, intelligent automation and AIOps infrastructure services, power management manufacturing
“Based off of the 3D models that the vendor provided to the plant … after performing that analysis, the plant was able to get back with the vendor and say, ‘Hey, we need to make a few adjustments here and there for a safety issue or for a better ergonomic issue or material flow.’ And so, we were able to make those changes to the equipment before any equipment was actually built so that when they go to build it, everything is built in the way that works well for the plant.”
Lead solution engineer, intelligent automation and AIOps infrastructure services, power management manufacturing
“The first step was to start doing some virtual layouts to be able to see how all the equipment fits together. Is there going to be enough space on the floor where the plant plans to put all the equipment? And things like that. And be able to kind of move things around and make sure that as soon as the equipment arrives, they know exactly what the equipment looks like, how large it is, where it needs to fit, and the configuration of everything.”
Lead solution engineer, intelligent automation and AIOps infrastructure services, power management manufacturing
Modeling and assumptions. The focus of this benefit is faster time to revenue enabled with Microsoft’s MR solution for ramping up new production lines with the key metric being improved productivity for factory technicians equipping each new production line. The second component of this benefit is the revenues derived by ramping each new production line faster than before as a function of planning each line with digital twins by deploying MR technology. Forrester modeled the impact for the composite organization assuming the following:
Based on the information provided, might bring on new production lines each year.
Based on the information provided, might accelerate the time to ramp a new production line by hours.
Based on the information provided, the annual revenue per production line per hour for is computed to be in Year 1.
Based on the information provided, the deployment ramp for the Microsoft MR technology at is as described in row G11.
Risks. The expected financial impact is subject to risks and variation based on several factors, including the following:
Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.68 million.
For , this benefit might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
G1 | Incremental production lines added | (R1-R1PY)*R6 | ||||
G2 | Production lines per factory | R6 | ||||
G3 | Factory employees needed per machine for new factory layout before Microsoft HL2 MR solution | Interviews | ||||
G4 | Factory employees needed per machine for new factory layout after Microsoft HL2 MR solution | Interviews | ||||
G5 | Hours to install each machine for a new production line before Microsoft HL2 MR solution | Interviews | ||||
G6 | Hours to install each machine for a new production line after Microsoft HL2 MR solution | Interviews | ||||
G7 | Fully burdened hourly salary of average manufacturing technician | A8 | ||||
G8 | Subtotal: Cost savings derived by utilizing MR solution for new production lines layout | G1*G2*(G3-G4)*(G5-G6)*G7 | ||||
G9 | Reduction in hours required to ramp each incremental production line | Interviews | ||||
G10 | Revenue per production line per hour | S10 | ||||
G11 | Deployment ramp of Microsoft HL2 MR solution for training | R12 | ||||
G12 | Subtotal: Faster time to revenue for new production lines | G1*G9*G10*G11 | ||||
G13 | Operating margin | B14 | ||||
Gt | Improved time to revenue for new factories ramping online | G8+G12*G13 | ||||
Risk adjustment | ↓20% | |||||
Gtr | Improved time to revenue for new factories ramping online (risk-adjusted) | |||||
Three-year total: | Three-year present value: |
Base: 312 decision-makers working as operations professionals, frontline workers, or training/teaching/research employees in the architecture and engineering construction (AEC), education, healthcare, or manufacturing industries who use mixed reality headsets for their organizations, which have at least 1,000 employees.
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, August 2023
Evidence and data. Replacing expert travel with remote expertise and self-guided task worker instruction saved the interviewees’ organizations significant travel and incidentals costs (e.g., flights, cars, hotels, food and beverage, etc.) in addition to labor saved (which is captured separately, under productivity benefits). Interviewees noted costs saved were typically between $2,500 to $3,500 per trip. Avoided regional field worker trips for rework and follow-up visits also generated minor cost savings for fuel and incidentals. Training travel cost savings for new technicians were also experienced. Field technicians and manufacturing SMEs reduced overall travel due to MR technology.
Interviewees shared many examples of overall travel and incidentals savings benefits, including the following:
45%
Reduction in number of training-related trips
20%
Reduction in number of field technician trips
“The beauty of HoloLens is that it allows you to have flexibility. You can pull that digital twin up anywhere. I can pull it up in my office, I could be in a conference room, wherever, you could pull this digital twin up.”
Director of augmented engineering services, pharmaceuticals manufacturing
Modeling and assumptions. Forrester modeled the impact for the composite organization assuming the following:
“It’s great for my team, we can go on to the next car and, if the dealer can fix the car in one or two days, it allows them to use this technical capacity now somewhere else.”
Department manager, field tech services, automotive manufacturing
Risks. The expected financial impact is subject to risks and variation based on several factors, including the following:
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 $2.09 million.
For , this benefit might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|
H1 | New skilled factory technicians hired | A3 | ||||
H2 | Manufacturing SMEs | F1 | ||||
H3 | Total average number of training-related trips required annually before HL2 | H1*1+H2*2 | ||||
H4 | Reduction in number of trips due to Microsoft HL2 MR solution | Interviews and survey | ||||
H5 | Avoided trips per trainer/trainee after Microsoft HL2 MR solution | H3*H4 | ||||
H6 | Average cost savings per trainer/trainee trip | Interviews and survey | ||||
H7 | Subtotal: Total travel cost savings for training | H5*H6 | ||||
H8 | Field technicians with access to HL2 MR solution | E3 | ||||
H9 | Percentage of field technician tasks enhanced with MR | E5 | ||||
H10 | Typical field technician hours spent on travel before MR | E4*25% | ||||
H11 | Reduction in number of trips due to Microsoft HL2 MR solution | Interviews and survey | ||||
H12 | Average travel and incidentals hourly cost per skilled field technician | E8+$10 | ||||
H13 | Subtotal: Total travel cost savings for field technicians | H8*H9*H10*H11*H12 | ||||
H14 | Critical incidents impacting factories | F7 | ||||
H15 | Reduction in number of trips due to Microsoft HL2 MR solution | F9 | ||||
H16 | Average cost savings per manufacturing SME trip | Interviews and survey | ||||
H17 | Subtotal: Total travel cost savings for manufacturing SMEs | H14*H15*H16 | ||||
Ht | Overall travel and incidentals savings | H7+H13+H17 | ||||
Risk adjustment | ↓10% | |||||
Htr | Overall travel and incidentals savings (risk-adjusted) | |||||
Three-year total: | Three-year present value: |
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify include:
“The benefit we see in HoloLens when a technician works, you have the device and you’re hands-free — you can work.”
Department manager, field tech services, automotive manufacturing
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement HoloLens 2 with MRApps and later realize additional uses and business opportunities, including:
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
“Our primary use cases for the device are remote expert assist, technical training and to a lesser extent and BIM for tool install. And we’ve been using the HoloLens, as I said kicked off in 2020 and now in 2023, we have a couple of hundred headsets, and we’ve trained a few thousand people.”
Engineering project lead, semiconductor manufacturer
Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
---|---|---|---|---|---|---|---|
Itr | HoloLens 2 devices | ||||||
Jtr | Subscriptions and consumption | ||||||
Ktr | Planning, implementation, and management | ||||||
Ltr | Training | ||||||
Total costs (risk-adjusted) |
Evidence and data. Interviewees noted that their organization could purchase HoloLens 2 devices directly from Microsoft or through a partner. Devices could be dedicated to a user or shared by multiple users. Some interviewees noted their organizations required specialized devices, such as the Industrial Edition or third-party hard hat options.
Modeling and assumptions. Forrester modeled the cost for the composite organization assuming:
purchases devices initially, devices in Year 1, and devices in Year 2.
Risks. Forrester uncovered low risks that may impact device costs, including the selected use cases, the number of sites and users, travel and site damage risk, network integration needs, device availability, existing usage of Microsoft services, and any needs for specialized Industrial Edition or hard hat devices.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV of $646,308
For , this cost might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|---|
I1 | HoloLens 2 devices | R10NY | |||||
I2 | Cost per HoloLens 2 device | Composite | |||||
I3 | Subtotal: Initial device purchase costs | (I1-I1PY)*I2 | |||||
I4 | Overhead for device replacements | Interviews | |||||
I5 | Device replacement costs | I1*I2*I4 | |||||
I6 | Mobile device management subscription per device, per month | Composite | |||||
I7 | Mobile device management (MDM) subscriptions | I1*I6*12 | |||||
It | HoloLens 2 devices | I3+I5+I7 | |||||
Risk adjustment | ↑5% | ||||||
Itr | HoloLens 2 devices (risk-adjusted) | ||||||
Three-year total: | Three-year present value: |
Evidence and data. Interviewees noted that most mixed-reality apps from Microsoft and ISV partners were priced using a per-user subscription fee with additional costs incurred for Azure services consumption. Device-based licensing was an option for only some applications. The cost of custom-developed apps was instead based on internal and system integrator (SI) labor rather than subscriptions.
Modeling and assumptions. Forrester modeled the cost for the composite organization assuming:
assigns subscriptions to end users in Year 1, in Year 2, and in Year 3.
might incur Azure costs of up to by Year 3.
might incur up to in additional costs to provide network connectivity for sites or field users without dependable access.
Risks. Costs will vary based on the selected applications and number of users. Interviewees noted that their largest inhibitor to scaling MR was user-based licensing models that became prohibitively expensive and difficult to manage for large numbers of infrequent users, such as trainees. Readers are strongly advised to: 1) carefully select the use cases and end users for which subscriptions are assigned, 2) conduct regular and diligent user profile management to control costs, and 3) monitor usage of Azure minutes.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV of $2.03 million.
For , this cost might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|---|
J1 | Licensed RA and Guides users | R13 | |||||
J2 | Average annual subscription cost per user for Guides and Remote Assist | Composite | |||||
J3 | Subtotal: Application subscriptions | J1*J2 | |||||
J4 | Azure cloud services for access and consumption | ||||||
J5 | Internet accessibility | ||||||
Jt | Subscriptions and consumption | J3+J4+J5 | |||||
Risk adjustment | ↑10% | ||||||
Jtr | Subscriptions and consumption (risk-adjusted) | ||||||
Three-year total: | Three-year present value: |
Evidence and data. Interviewees noted that implementation costs and labor for mixed reality continued to fall every year as more expertise and apps became available and as use cases became better documented and defined.
However, mixed reality remained at the leading edge. Successful implementations required significant work to build, test, and evangelize. The time, cost, expertise, and stakeholder buy-in needed to deploy mixed reality and ensure successful adoption must not be underestimated. Mixed reality success required much more than deploying software; entire processes (e.g., training methods) must be changed with stakeholders driving change forward.
While remote collaboration could often be deployed quickly and easily with minimal change management, significant effort must be dedicated to work instructions and visualization. Teams must document processes, map out process changes, gather or create 3D assets, build instructions, and test and refine iteratively until the visualizations and instructions provide consistent value to users. Frontline workers and their managers must trust the materials and find the experience to be both relevant and high quality.
Modeling and assumptions. Forrester modeled the cost for the composite organization assuming:
Based on the information provided, might have a team of people running key innovation efforts, including the mixed reality deployment. This team might include XR leader(s) and developer(s), technical project manager(s), 3D designer(s), and ITOps personnel. The team size and composition might vary depending on the scope of the deployment.
For , the time dedicated to the initial deployment might include: 1) total hours from the XR leader(s) and developer(s); 2) total hours from the technical project manager(s); 3) total hours from the 3D designer(s); and 4) total hours from the ITOps FTE(s).
For , the time that might be dedicated to developing and maintaining the solution in Year 1, Year 2, and Year 3 is shown in the table.
Based on the information provided, might have tester(s) who collectively might provide hours of labor prior to deployment. In addition, the tester(s) might provide total hours of labor in Year 1, total hours of labor in Year 2, and total hours of labor in Year 3.
might incur in initial professional costs, plus an additional in Year 1.
The innovation team at might hold a retainer expense budget of per year.
Risks. Costs will vary significantly per organization depending on a range of factors, including:
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV of $3.84 million.
For , this cost might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|---|
K1 | Manufacturing SMEs with access to Microsoft HL2 MR solution | F3 | |||||
K2 | Annual hours spent on MR-related training materials for training and operations per SME | ||||||
K3 | Annual hours spent on MR-related new factory planning per SME | Interviews | |||||
K4 | Fully burdened hourly salary of manufacturing SME | F13 | |||||
K5 | Subtotal: Manufacturing SME costs related to MR | K1*(K2+K3)*K4 | |||||
K6 | 3D designer hours | ||||||
K7 | Fully burdened hourly salary of 3D designer | TEI standard | |||||
K8 | Subtotal: 3D designer costs | K6*K7 | |||||
K9 | Developer hours | ||||||
K10 | Fully burdened hourly salary of developer | TEI standard | |||||
K11 | Subtotal: Developer costs | K9*K10 | |||||
K12 | ITOps hours | ||||||
K13 | Fully burdened hourly salary of ITOps FTE | TEI standard | |||||
K14 | Subtotal: IT admin costs | K12*K13 | |||||
K15 | Project management hours | ||||||
K16 | Fully burdened hourly salary of project management FTE | TEI standard | |||||
K17 | Subtotal: Project management costs | K15*K16 | |||||
K18 | Test user hours | ||||||
K19 | Fully burdened hourly salary of test user | TEI standard | |||||
K20 | Subtotal: Test user costs | K18*K19 | |||||
K21 | Professional services costs | ||||||
K22 | Expense budget for shipping, travel, and other hardware/software needs | ||||||
Kt | Planning, implementation, and management | K5+K8+K11+K14+ K17+K20+K21+K22 |
|||||
Risk adjustment | ↑10% | ||||||
Ktr | Planning, implementation, and management (risk-adjusted) | ||||||
Three-year total: | Three-year present value: |
Evidence and data. Interviewees noted that their users had to learn to use HoloLens 2 and the apps running on it before they could benefit from their new mixed reality work experiences. They also had to be convinced to use it in their day-to-day work. Investing in user training was critical to drive real adoption of mixed reality and achieve business results. Underinvesting in training led to disappointing results; compared to the potential benefit, the costs of a high-quality and well-supported mixed reality training program were easily recouped.
Aside from learning to use the HoloLens 2 itself, training was particularly important for addressing two adjacent needs commonly experienced by interviewees when deploying mixed reality: 1) the need to conduct additional safety training to ensure that users followed rigid safety protocols even after donning the headset and 2) the need to conduct change management training around process redesign that often accompanied the development of MR instructions.
Modeling and assumptions. Forrester conservatively modeled user training costs for the composite organization. In most cases, users at the composite organization require anywhere from 2 hours to 8 hours of training to gain comfort and proficiency with the HoloLens 2 device and MR applications. Given the scale of the composite’s deployment, Forrester has modeled 8 hours of training for all 800 users who interact with mixed reality, including those being trained for their work tasks with HoloLens 2.
Risks. Costs will vary significantly per organization depending on a range of factors, including:
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV of $247,000.
For , this cost might have a three-year, risk-adjusted total PV of .
Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|---|
L1 | Total MR users including trainees and device sharing | J1 | |||||
L2 | Percent of users that are new to MR | (L1-L1PY)/L1 | |||||
L3 | Training and set up hours per new MR user | Interviews | |||||
L4 | Fully burdened hourly salary across all MR users | Assumption | |||||
Lt | Training | L1*L2*L3*L4 | |||||
Risk adjustment | ↑10% | ||||||
Ltr | Training (risk-adjusted) | ||||||
Three-year total: | Three-year present value: |
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
---|---|---|---|---|---|---|
Total costs | ||||||
Total benefits | ||||||
Net benefits | ||||||
ROI | ||||||
Payback |
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Benefits represent the value delivered to the business by the product. The TEI methodology places equal weight on the measure of benefits and the measure of costs, allowing for a full examination of the effect of the technology on the entire organization.
Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The cost category within TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PV of costs and benefits feed into the total NPV of cash flows.
The present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
Base: 312 decision-makers working as operations professionals, frontline workers, or training/teaching/research employees in the architecture and engineering construction (AEC), education, healthcare, or manufacturing industries who use mixed reality headsets for their organizations, which have at least 1,000 employees.
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, August 2023
“Using your best estimate, how many employees work for your firm/organization worldwide?”
Base: 312 decision-makers working as operations professionals, frontline workers, or training/teaching/research employees in the architecture and engineering construction (AEC), education, healthcare, or manufacturing industries who use mixed reality headsets for their organizations, which have at least 1,000 employees.
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, August 2023
“Using your best estimate, what is your organization’s annual revenue(USD)?”
Base: 240 decision-makers working as operations professionals, frontline workers, or training/teaching/research employees in the architecture and engineering construction (AEC), education, healthcare, or manufacturing industries who use mixed reality headsets for their organizations, which have at least 1,000 employees. Varies from total base of 312 due to some respondents opting to not respond to this question.
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, August 2023
Related Forrester Research
“A Reality Check For Enterprise Extended Reality And Metaverse,” Forrester Research, Inc., April 27, 2023.
“Building The Beginnings Of The Metaverse,” Forrester Research, Inc., January 24, 2023.
“Smart Manufacturing: Don’t Forget The People,” Forrester Research, Inc., October 4, 2022.
“The Forrester Wave™: Digital Operations Platforms For Manufacturing And Distribution, Q3 2022,” Forrester Research, Inc., September 26, 2022.
“The Future Of Manufacturing,” Forrester Research, Inc., September 9, 2022.
“How To Successfully Scale Your Augmented Reality Device Rollout,” Forrester Research, Inc., August 3, 2021.
Online Resources
Laia Tremosa, “Beyond AR vs. VR: What is the Difference between AR vs. MR vs. VR vs. XR?,” Interaction Design Foundation, August 2023.
1 Source: “What are Industry 4.0, the Fourth Industrial Revolution, and 4IR?,” McKinsey & Company, August 17, 2022.
2 Source: “The Total Economic Impact™ Of Mixed Reality Using Microsoft HoloLens 2,” a commissioned study conducted by Forrester Consulting on behalf of Microsoft, November 2021.
3 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s
technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Forrester provides independent and objective research-based consulting to help leaders deliver key transformation outcomes. Fueled by our customer-obsessed research, Forrester’s seasoned consultants partner with leaders to execute on their priorities using a unique engagement model that tailors to diverse needs and ensures lasting impact. For more information, visit forrester.com/consulting.
© Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester®, Technographics®, Forrester Wave, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies.
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