Please answer the following questions using the content provided below as reference.

While the customer is the key stakeholder there are many stakeholders who form the designed ecosystem:

1. How will the insights from data and AI generate unique value for each category of stakeholder?

2. How are stakeholders better off for being on the ecosystem as designed than not being a part of it?

Comprehensive Business Plan

1. Workstations

Trucking companies are under constant pressure to deliver their goods on time and on budget. As a result, they often overlook needed repairs to keep their vehicles on the road. This practice can put drivers and the public at risk. When trucking companies fail to properly maintain their vehicles, it can lead to breakdowns, accidents, and even fatalities which can increase companies' expenses. Furthermore, due to COVID 19's impact there are additional hurdles trucking company faces such as inflation; rise in fuel, equipment, and labor costs the focus on preventive maintenance becomes clearer as fleets are adding alternative suppliers and providers, and stockpiling inventory to help control costs and reduce downtime.

Predictive maintenance platform is a proactive upkeep strategy that tracks vehicle health and uses data science and analytics to forecast when vehicles will need maintenance so they can be addressed before breakdowns occur. Time is the most valuable asset when it comes to detecting potential issues with fleets. It is pointless to detect a problem minutes before it causes a road incident or cargo damage. More time is needed to act to minimize costs and hazards, therefore predictive maintenance becomes important. It aims to boost the efficiency of a fleet and minimize downtime and operational expenses over time. It also gives the company a realistic assessment of how the fleet is performing compared to the plan.

Fleet managers can leverage telematics, artificial intelligence, machine learning and cloud technologies to gather real-time data and formulate accurate predictions. In this process, fleet managers look at the condition of the vehicle and then repair or swap out an old part for a new one when necessary.If a vehicle must be pulled for repair, the downtime is shorter and more targeted, because it is based on real-time data. The technologies used in the platform also help provide better visibility of the entire logistic network; fleet service shops, vehicle parts wholesalers and retailers for procurement can share a view of all assets and resources in the field. The platform also provides built-in chat functionality that facilitates communication between vendors, drivers, and fleet managers.

Technology Platform

Artificial Intelligence plays a vital role on our platform. Artificial Intelligence is aconstellation of many different technologies working together to enable machines to sense, comprehend, and act.

The primary means of sensing digital traces will be by sensor processing.The platform will use telematics devices which is plugged into each vehicle, they capture and transmit real-time data regarding the vehicle's performance such as location, speed, idling time, fuel consumption, tire pressure, temperature, battery condition, brake condition and other sensor data.

The platform will make use of inference engines, expert system and machine learning capabilities likeanalytics and predictive algorithms to analyze the data captured from telematics devices and formulate accurate predictions such as to determine which components should be replaced before they break down or cause an accident. These tools can look for patterns in data that indicate failure modes for specific components or generate more accurate predictions of the lifespan for a component given environmental conditions and usage. When specific failure signals are observed, or component aging criteria are met, the components can then be replaced during scheduled maintenance windows. These systems can even alert drivers and fleet managers that components may fail soon, so that they can take proactive measures to change vehicles to keep scheduled appointments. These machine learning and artificial intelligence-based solutions help to scale the process, enabling accurate and timely analysis of billions of data points, with no risk of human mistakes.

Additionally, we can have future enhancements such as making use of cameras and other sensors to capture in-cabin events. Any additional future enhancements will be independent of IoT (Internet of Things) devices.

Business Model

There is rapid growth of the fleet management solution market due to major factors such as growing adoption of wireless technology, optimizing fleet operating expenses, the rapid expansion of 5G availability and an increasing number of fleet accidents. The global fleet management market is valued at $22 billion and is projected to reach $50 billion by 2030[1]. The North American fleet management solution market was valued at USD 2.2 billion in 2020, and it is expected to reach USD 6.72 billion by 2026, registering a Compound Annual Growth Rate CAGR of 20.21% during the forecast period (2021-2026) [2]. Based on this total addressable market, we aim for 1% of this market to adopt our technology.

Our platform is accessible for fleet management solutions with at least 5 vehicles. Fleet management refers to all actions that need to take place to keep a fleet running efficiently on time, and within budget. It is the process used by fleet managers to monitor fleet activities and make decisions about proper asset management, dispatch and routing, and vehicle acquisition and disposal. Fleet management helps to ensure that a fleet is meeting compliance requirements, continuously improving efficiencies, and reducing costs.Some of our target customers are Fleetio, Route4Me, Samsara based in the Americas region.

Our platform is available to fleet management companies on a contract basis with monthly subscription model with the option of white labeling the solution. We are estimating selling software licenses for $200 per year per vehicle in the fleet. Initially, this will be an investment for the fleet management companies. For years 1 and year 2 they would not see any returns but from year 3 our clients will start to see a return on their investment over a 5-year period and their IRR is estimated to be about 30% on average. The number of quantities of our product that will be purchased by our clients will depend on the number of vehicles in the fleet.

The service cost associated with our platform will be very economical compared to the rest of the industry. We have an initial training fee of $3750 i.e., $150 per hour for 25 hours and a support fee of $4000.

Our business model is very lucrative for investors. Our IRR to develop the platform over 5 years is 166% and our net present value is $111 million at a discount rate of 8%. With these numbers, from a future scope standpoint, there is a possibility of expanding the business to a global scale where the industry is valued at $50 billion.

From a company perspective, our PdM (Predictive maintenance) application can help fleet management companies to improve revenue generation with data-informed decisions around fleet deployment, time chartering in and out, and fleet equipment booking. For example, the data can be leveraged to optimize fleet positioning, analyze the drivers for rate/return variability, and short-term market dynamics.

Costs can also be optimized by collecting and analyzing data within the PdM applications to inform decision making. The PdM application can help to identify trends, optimize driver spend and the overall procurement envelope (spares, stores, provisions), and streamline repair and maintenance. Some areas where cost improvement can be attained within fleet management are:

Crewing: Fleet management companies could leverage the analytical tools within the PdM application to improve communications from loading to unloading at warehouses and to revamp safety- and quality-assurance procedures. Data analytic tools could aid in optimizing factors such as driver remuneration, number of drivers onboard, insurance claims, and travel scheduling.

Procurement: The spend-intelligence dashboard could provide real-time insights and transparency. Further, digital tools within our PdM application could standardize the procurement process, define supplier-specific strategy, consolidate purchases, and reduce unplanned spend. Fleet management companies that rely on manual processes often struggle to track their expenditure by supplier, location, or even product or service type. This can cause a lack of comprehensive planning of purchases such as spare parts. Suppliers cannot be selected in a systematic way, and companies miss volume discounts and delivery-pool savings. They may also incur higher operating expenses with emergency procurement and repairs. Such companies could invest in the PdM technology that provides digital dashboards. Here, real-time insights and analysis across key metrics can be accessed easily. Spend details can be sorted by type, category, and details made instantly available by clicking on a specific deep-dive area.

AI-driven predictive engines can make time-to-failure predictions, detect degradations, and run degradation simulations thus maintenance efforts would then focus on continuous monitoring to avoid critical outages. Analytics will also be useful for determining how to optimize the work order activities to increase efficiency and reduce costs. The sensors and algorithm scan the data in real-time to develop a summary view of the health and criticality of fleet. Using this analysis, a work order dispatcher automatically determines how the backlog of activities should be prioritized, considering the maintenance strategy of fleet and any capacity restrictions, such as the size of the maintenance team. Sensor data- temperature, tire pressure and other hyperlocal dataweather, wind, currents, and waves, combined with real-time views of traffic, congestion, and warehouse terminal activitiescould be deployed to optimize routes, fuel consumption, and turnaround time, as well as manage fleet delays and just-in-time arrivals.

Also worth mentioning is that our PdM application has the potential to shape the future of fleet mobility and business dynamics of insurance carriers: autonomous driving, connectivity and embedded telematics, and vehicle electrification. PdM in fleet maintenance means original equipment manufacturers (OEMs) will have more access to the customer and vehicle data than ever. It also means OEMs will be in an advantageous position to disintermediate insurers. Insurers may be able to take advantage of the technology and fleet mobility trends to capitalize on insurance that is based on vehicle usage in real time, combined with an automated-claims processcreating opportunities to improve both loss and expense ratios.

5. Financial Analysis

Cost Estimation

The important aspect of getting started on any project is identifying the initial capital required to get started and where this capital will be sourced from. For the development of any digital platform, this starts from what problem we are going to solve and what application platforms/ecosystems do we need to build in support of the identified problem. We have identified upfront costs related to the development as follows;

1. Software Licenses (Development, off-the-shelf, security, project management, etc.)
2. Cloud Costs (Initial Year & separate from operational costs)
3. Hardware Design
4. Platform Development

All software platforms should go through a software development life cycle and as such will require capabilities for design, development, quality assurance, security, DevOps, Project Management etc. We have utilized a combined aggregate of $250 per hour as the labor charges for all the capabilities combined and an estimate of 2000 hours to develop the platform ecosystem accounting for $1,500,000 towards platform development.

Once the platform ecosystem has been developed, there will be ongoing maintenance on the platform, but the costs are negligible and will be accounted for in the payroll expenses of direct hire software engineers. We anticipate the code to last up to 7 years before a major overhaul of the platform becomes necessary. We have also allocated $50,000 towards procuring software licenses required to support the development of the platform in the initial year. These include development software, project management, security, and ancillary software packages. An equal amount of $50,000 has been allocated to procure workstations which include computers, monitors, printers, etc.

Since the entire platform will be hosted in a public cloud environment, we need multiple environments for development, testing, staging and production. The amount of $10,000 has been allocated for the initial year's spend on cloud infrastructure. In addition to the above capital requirements related to the design and development of the platform ecosystem, we have also allocated $1,500,000 towards the initial years' payroll expenses.

Detailed Estimates of Market Development Costs

These costs need to be amortized annually

Operating Costs

The operating costs are annual costs required to operate the business. We have identified the following operating costs for the platform ecosystem.

1. Software Licenses
2. Public Cloud
3. Hardware Manufacturing
4. Wholesale 4G/5G Bandwidth
5. Marketing
6. Payroll

The platform will require ongoing annual software licenses to manage updates to the platform developed, project management software, and off-the-shelf supporting software. We have allocated $50,0000 towards the ongoing software licensing requirements. The costs for public cloud infrastructure will increase each year as the business scales. This is attributable to the increase in compute and storage costs. We start year 1 with an allocation of $20,000, for year 2 we have $40,000, and we continue to increase this proportionately as the business activities scale, ending year 5 with an allocation of $400,000. There is an outsourced manufacturing cost associated with the vehicle telematics devices that is a key component of the predictive fleet maintenance platform. We have not considered economies of scale and have kept the manufacturing costs constant through the years at $10 per unit. The manufacturing costs change every year based on the projected number of units we expect to go into production. We start year 1 with 25,000 units, resulting in a manufacturing cost of $250,000. For year 2 we are projecting 100,000 units, resulting in manufacturing costs of $1,000,000. For year 5, we are projecting 600,000 units resulting in manufacturing costs of $6,000,000. We will be purchasing bandwidth for data transmission in the wholesale markets to drive our capital costs down. Bandwidth usage will increase year-over-year as the number of devices deployed in production increases. We start year 1 at $250,000 for bandwidth purchase and continue to increase our capital allocation to end year 5 with $1,200,000. While we have a significant market development expense in the initial year to drum up demand for the platform, we still need to continue the marketing efforts in future years. For this reason, we have allocated $300,000 towards marketing for each of the five years projected. As the business continues to scale, there will be a need for more employees to manage customers, onboarding, management etc. We remain conscious of this fact and have an increasing allocation towards payroll to account for salaries, wages, benefits for employees and contractors. Being a cloud-basedSaaS (Software as a Service) application, we do not have any upfront infrastructure costs. In a world enabled by technology, to keep our operating expenses lean, we have adopted a remote first approach for all employees and contractors and will not have any expenditure on office spaces.