Operation & Decision Analysis

Operational Process

This operational analysis comprises the gathering of information and research for purposes of locating and isolating problems in the proposed business model, as well as, an examination of the proposed business systems for effectiveness.  Performance measures consist of answering two questions;

  1. How capable is the studio at supporting clients and stakeholders, and
  2. how effectively is the studio managed by leadership?

To this end, data was reviewed from a variety of sources and at the end of this report a series of final recommendations focusing on for possible solutions and opportunities for enhancement to operational processes is provided.

Operations Diagram

In the below diagram, is a chart detailing the primary operations needed for the production and delivery of mixed media experiences and content creation services to clients. These operational processes facilitate the creation of value for clients and stakeholders. In the content creation sector, value is often driven by a few areas;

  • the ability to create a compelling and engaging experience – typically though rich storytelling, intuitive UIs and ease of use, and
  • the desire to create the “ultimate” immersive experience that ideally achieves the highest level of immersion and the highest level of the 6-degrees of freedom possible, based on available technology.

For a startup with limited staff and capital, it is likely that individuals and departments will overlap in the early years of the business. For instance, during year one, the only employees may be the Director, CEO and Operations Director, though it is unlikely they will use those express titles. During this year, things will be very hands on and to maintain the pace needed to grow, the studio might outsource large portions of work to vendors.

The mixed reality sector is still relatively young, and as such, there are areas that are not as easily outsourced, such as programming and development. VR and AR programming is still something of a niche and many individuals who are technologically capable tend to run their own businesses or work already for larger-scale companies. This significantly impacts outsourcing these tasks. Thus, having the capability to innovate and troubleshoot new and often times untested technology must exist in operational leadership.

Cost Cutting Diagram

In contrast to other business models, a mixed reality creation studio does not rely on a constant revenue streams, but instead much like the film or video game industry, operates on continuing investments. This often involves a parent company and/or angel investor(s) or venture capital firm(s) early on. In addition, studios might need to seek out the help of private corporate investment entities throughout the life of the business.

For many studios, the only source of profit comes directly from the experiences and the IP that they create. This underscores the need to produce a high-quality VR or AR experiences. Because the mixed reality field is in flux, during times of increased demand a studio can profit; if, its executive management team is capable of using its resources to deliver engaging immersive experiences.

Based on the original sample workflow presented in the Introduction, it is of note that commonly departments are nonexistent or underfunded when a studio is just starting out, especially in areas, such as, data capture, storage and analytics. The proposed business model includes labor increases that total $47,000 per production cycle, but are estimated to save $200,000 of loss per production cycle. The total cost for the proposed model is $449,000.

Queue Diagram

The workflow models (as presented in the Introduction), are good candidates for the use of queuing theory for quantitative evaluation. The production queue is really a queue of queues encompassing project files in use from department to department. The number of experiences that a studio might release in a year might start at just one or two during the early years of the business and may grow to 20 to 25 releases per year, as the business matures and the market demands. This is an area to explore as more data becomes available.

The measure of effectiveness using queue theory would ideally include the following quantitative factors;

  • average waiting time per project in the queue,
  • average processing time,
  • average time waiting per project in the system (average waiting time in queue plus average processing time),
  • average number of projects in the queue, and
  • the average number of clients in the system.

Let’s use the M/M/s/N queuing model to analyze the effectiveness of the two models. For the first model, let’s assume an arrival rate of 5 projects per year, a service rate of 2, a capacity of 3 and our current number of servers as 1. A quick analysis reveals the queuing intensity would be 2.5, utilization 96.059%, length in queue would be 1.478, length in system would be 2.438. Based on these figures the delay in the queue would be 0.769 and the delay to the system would be 1.269, with the probability of an idle server at 3.941%.

Based on the revised workflow proposal the arrival rate of 5 projects per year would stay the same, a service rate would increase to 3, so would capacity to 4 and our current number of servers would double, bringing us to 2. Under the newly proposed model the queueing intensity would drop to 1.667, utilization would drop to 70.322%, length in queue would drop to 0.500, length in system would be 1.906. Based on these figures the delay in the queue decrease to 0.118 and the delays to the system would fall to 0.452, with the probability of an idle server at 16.188%.

Probability of n Projects in the System

Queuing Delays

  • Queue
  • System

Queuing Intensity

  • Intensity

Probability: Original Model

  • Idle Server
  • Busy Server

Queuing Utilization: Original Model

  • Server Very Busy
  • Server Not Busy

Probability: Proposed Model

  • Idle Server
  • Busy Server

Queuing Utilization: Proposed Model

  • Server Very Busy
  • Server Not Busy

Five-Year Plan

When doing bigger, bolder things; optimism alone is not enough to ensure success. The only way to handle risk is to have enough capital to offset failures and to accommodate R&D efforts. In these times of economic and political uncertainty, investors are more particular about the companies they choose to devote time and resources to and may invest more sparingly. For these reasons, planning ahead five years in the mixed reality sector can be a daunting task.

Get smart about collecting data.

While the goal of a five-year plan is to offer succinct and pertinent information to investors in a convincing manner, the sector suffers with excessive hype, a lack of expert guidance and a general lack of capability relating to data analysis efforts. Thus, it is important for companies to illustrate their ability to cultivate insight capability from the onset as soon, as possible.

Consider the moral and societal challenges of mitigating human emotional responses and their impact on a studio. Immersive experiences commonly provoke a deep emotion response in participants and for safety and compliance these areas should be studied and careful consideration to the handling of data and privacy addressed in the early stages of any projects. Some experiences can even cause participants to become physical ill and experience motion sickness, which can have a devastating impact on the studio in the long term. Savvy investors are aware of these issues and expect studios to be able to offer ways to address them.

By testing and measuring responses to the experience, before mass release, studios can manage these side effects quickly and provide clients with a safer, more controlled experience leading to more positive outcomes for the business. Additionally by collecting data, many other areas of risk can be addressed and decisions by stakeholders reached with more certainty and positive outcomes. Let’s see what a five-year plan might entail.

Executive Summary

In an ideal world, stakeholders would recognize the need for agile, high-value analytics solutions for mixed reality content production early on. As the mixed reality industry evolves and more companies are entering the marketplace, there is a need for analytics professionals that deeply understand all of the aspects and challenges facing companies crafting the next generation of entertainment and education. This report includes research and general recommendations for mixed reality content creation startups on how they can incorporate data capture and analysis into their planning and operational strategies.

Management Team

Leadership will ideally have strong educational backgrounds, in addition to, relevant and translatable experience. Technical skills, such as; human computer interaction, real-time 3D computer animation, computer science, television and film production, engineering and dynamics are necessary to understand the intricacies of the landscape. In addition to strong technical skills, the executive management team must be equipped to handle social, political, and economic issues with an equal robustness.

Societal, political and economic issues have a huge impact of the short-term outlook for mixed reality content creators. Leadership must be competent to find the great opportunities in the marketplace, as they arise, and appreciate the need for close looped experiments and data cultivation for quick decision making by stakeholders and key decision makers.

Company Structure

The studio set up in this example might operate as a Limited Liability Company (LLC) incorporated under the laws of the state of its home state. However, these ideas could be configured to work with a corporation or sole proprietorship, just as easily. The founder of the studio, in this example, would function as the Director, along with two to three additional key management members. Further, a formal advisory board is to be formed and expert consultants sought out.

Products & Services

Following the mixed reality business model proposed in the Introduction, first year products and services will consist of all aspects surrounding the creation, delivery and support of narrative immersive experiences and immersive digital environments for the location-based entertainment (LBE), film and television industries. Throughout the second year, clients will see product enhancements relating to the 6-degrees of freedom and the level of immersion offered, as the studio shifts into incorporating positional tracking, stereoscopic 3D and surround sound acoustics. In the third year, product focus will be on delivering higher quality visual displays and tackling perception issues. In the fourth year, customers will see more product enhancements in terms of interaction, as improvements to motion tracking, interactive user-input, controls and computer vision are realized. In the fifth year, customers will see the expansion of support capability and additional services.

One of the strongest threats to studios entering the mixed reality content sector is competition from pre-existing companies with massive social media platforms. The big 4; Google, Amazon, Facebook and Apple are known for creating visionary products and have the proven methods of distribution and the market reach needed to ensure success. Each of these 4 companies have platforms with the abilities to reach billions and billions of users and are currently producing original content.

Financial Projections: Overcoming the Folly of Forecasting the Future of VR & AR

In this early stage of the mixed reality sector, businesses entering the marketplace face high levels of risk. Historical company information, in and of itself, often falls short in terms of accurate prediction. This is largely because most prediction models do not work well in the current environment given the level of volatility and the unknowns surrounding the security and market index.

The rate of mixed reality trend growth and mass adoption in the economy is one of the best predictors of success for companies in the sector. In the charts and diagrams below, is a sample of a financial projection for the 5-year strategy plan

Data Tables

Design Visualization

The following charts and diagrams are rounded to the thousands of dollars. On the various axis below, please note that figures over 1000 are millions of dollars, not thousands.

5-Year Revenue Forecast

  • Product.1
  • Product. 2
  • Product. 3
  • Product. 4

5-Year Operating Activities Forecast

  • Totals

5-Year Cost of Sales Forecast

  • Product.1
  • Product. 2
  • Product. 3
  • Product. 4

5-Year Cumulative Cashflow Forecast

  • Totals

Decision Analysis

Every content creation studio claims that they have a particularly exclusive style of storytelling, but creativity cannot stop with the storytelling aspect of the business. Creativity must be found in all aspects of the business to make it appealing for savvy investors. So how does a content studio prove, they that their approach is best? Further, how can this be achieved with limited access to capital. Let’s consider the success of Penrose Studios.

Penrose Studio’s Founder Eugene Chung, whose background in VR is well established and includes work with Oculus VR, NEA and Pixar [12]. In 2015, Penrose Studios found success in the sector when they were able to capitalize on under-utilized positional tracking capability in HMDs and had a moment of breakthrough creativity by mimicking the effect through the zoom features on the Samsung Gear VR [13]. After releasing a mobile preview of their movie, “The Rose and I” for Samsung Gear VR, viewers were dazzled by the ability to zoom in and out of the scene. Moreover, the buzz surrounding the release intensified, as people talked about the experience with renewed vigor for VR.

When the “Rose and I” premiered at the Sundance and Tribeca Film festivals, it was met with positive reviews and much praise by critics. This demonstrated Penrose Studios ability to achieve success. In 2016, Penrose Studios raised “$8.5 million in seed-round funding” [14]. Penrose Studios’ rise to success has many lessons that other mixed media startups can learn from, including; starting with;

  • a highly skilled leadership team,
  • establishing presence and name recognition in the marketplace early on,
  • being creative in methodology, as well as storytelling, and
  • being unafraid to have work vetted by the public, as well as hard-core critics.

Total Quality Control (TQC)

There are many root causes, which contribute to lowered production quality. Some of the challenges, like management can effect the entire enterprise, so it is important to address any shortages in planning and preparedness, as quickly as possible. Here are the key areas that introduce issues to production quality;

  • Suppliers
  • Coding of Application,
  • Technology,
  • Skills & Labor,
  • Management, and
  • Machines.

Consider this, does the vendors that the studio uses have the capability to handle the complexity and capacity of the project? How will programmers handle legacy code and hardware updates? Is there a process in place? Are the staff able to execute the process? Do they have the skills needed to handle the task? Many times these questions are asked too late when a studio is in the midst of a crisis and people are scrambling to find out, what went wrong. In the next section is a an Ishikawa “Fishbone” diagram that expands more of these ideas.

Ishikawa "Fishbone" Diagram


“While the U.S. is enabling technologies for the industry and Silicon Valley remains the heart of R&D, China is now leading on the market side” – Ryan Wang, Partner, Outpost Capital [15].

It is already well established that the mixed reality sector rises and falls with the mass adoption rates for supporting technologies, like HMDs or software applications. However, there are other trends that impact the sector substantially. One of the areas, where the landscape is constantly changing and that studios need to take note of, is where investment dollars are coming from.

Although the Americas region is forecast to account for 83% of the global VR market by 2019, many of the large scale investments in the sector are coming from China [16]. China has a strong primary market in the VR/AR sector with opportunities for innovative entrepreneurs [16]. This means mixed reality content creators should be prepared to do business with investment companies overseas.

Decision Tree

Let’s suppose a studio is creating a VR experience where participants ride on a virtual roller coaster. They want to give the riders as much as a sense of the 6-degrees of freedom that they can, but they have some technology issues to overcome. These issues may, or may not, be solved quickly, or may not yet be created.

After consulting with the creative team, one of the artists poses the question – how likely is the participant to look in that direction? Using a Kaggle dataset, from a from a VR driving experience, it’s possible to look at the data and begin to figure out the bounds of where a person might look in a typical VR experience [17].

This is an easy way to start formulating ideas, so for this task two different tools will be used, AnswerMiner, a web-based solution and RapidMiner, a well know program in the analytics world. First, using AnswerMiner, this is some of the information, that was able to be determined about the position for x;

  • lowest vr_pos_x value (-0.01),
  • middle vr_pos_x value (1.34),
  • average vr_pos_x value (1.61), and
  • highest vr_pos_x value (2.69).

These results could easy be repeated for the other positions for further insights. The resulting decision tree is capable of determining the correct outcome, (allowing 20% inaccuracy), in roughly 64% of the cases.

One of the things to consider, however, is that this is data specific to another experience. It can give a general idea, but if the experience in question has, for instance, action or sounds coming from an area that causes the user to turn their head quickly, the effectiveness of results might vary drastically. This will also occur in places where the two experiences diverge greatly. If a studio wants a concrete answer, gathering the data and using game play testers will reveal a much more accurate basis for analysis. This again underscores the need for a data capture system.

RapidMiner is more robust application for decision tree analysis. Below are images of the process and resulting decision tree and data from the same Kaggle dataset, as processed using different techniques and parameters in RapidMiner.

In these examples, other factors, such as the speed and throttle of the VR app the participants were experiencing were included. Although for the example, validation and performance nodes are not setup, in any work that is being used in a professional capacity, this would need to be included in the process.

Final Recommendations

Although many ideas were introduced on in this section, there is one major takeaway. The studio must begin to cultivate data and prepare it for the analytics process, as soon as possible to maximize on benefits and for presentation to potential investors. Quantitative analysis is essential for enticing savvy international investors, who may be bombarded with pitches from startups seeking capital.

Remember, investors want to how studios measure success and how well do they run their core business. Simply, being creative is not enough. Additionally, in China, where the “gold rush” for VR funding is currently taking place investors are highly aware of the market. A studio must be able to show the benefits of funding operations, able to demonstrate how costs will be cut, solve problems with production and processes and plan for the future using methods similar to the ones proposed above.