Environmental Remediation companies provide services spanning environmental compliance and emergency response, with personnel including operations managers, health and safety officers, accounting officers, equipment operators and trained hazmat technicians. As part of an effort to better service and expand clientele, and optimize day-to-day operations, environmental remediation companies needed to rethink their organization and develop operations research analysis to go along with a complete in-house streamlined software that leveraged cutting-edge machine learning technologies.
A full top-to-bottom approach was necessary to creating a highly technical and specialized organizational system that would provide Environmental Remediation companies with robust organizational tools to increase efficiency in workflow processes and increase revenues. Overall assessments were necessary to gain a holistic understanding of any Environmental Remediation company’s organization and processes. After conducting an internal audit in order to design a custom in-house software, an end product was imagined. This product was a complete internal program including:
- Data management platform
- Web based portal
- Mobile App with permission architecture and custom UX based on roles within the company
- Custom CRM
- Custom dispatch system that leverages AI/Machine Learning with mathematical optimization in order to make routing and decisions.
- Analytics platform
- Marketing management platform
Environmental Remediation companies were built to include an easy-to-use, unified and cost effective platform, ensuring all areas of their internal and external processes were interconnected.
In the medical field, there is an increasing need to not only reduce cost but also increase the efficiency of their systems and communication of patient needs. One area of improvement was in radiology reports and specifically how they were being recorded. The challenge was to transform the system where radiologists were forced to write their own evaluations of patient data and then transcribe them into formalized summaries without sacrificing important subjective notes.
Using data from a bounty of previous reporting, machine learning technology was able to understand the varying degrees of data and prioritize the information needed for patient summaries. By utilizing speech recognition software, radiologists would vocalize their findings and automatically have their details relayed into the processing of said medical reports. This resulted in a greater cohesion and standard for radiological reporting and much greater efficiency in the processing of patients.
Traditionally, determining factors are referenced for indications and their trends analyzed to make predictions in the world of finance. There was increasing difficulty in mimicking the successes and failures of a wide variety of industries and commodities using traditional methods. In order to properly quantify these predictions, the world of investing and private equity has come to be revolutionized with the advent of Big Data, and the potential role of artificial intelligence. Using cutting edge machine learning technology, artificial intelligence bridged traditional investing with a modern analytical approach.
Through advanced AI models, new programs are able to accurately predict the successes and failures of various stocks and investments and make informed decisions as a result. Importantly, the AI models successfully model the risk of various investments so that private equity firms can boldly invest with confidence. These models are the basis on which decisions are made, and are the foundation on which these investments succeed and capital grows. Quants dutifully modeled financial markets and systematically built AI models referencing traditional models as well as determining factors for indications of what to include. Some of the models leverage deep learning technology and adapt to the environment as the algorithm essentially learns on its own.
Technology companies have previously adapted poorly to changes in business environments globally and locally. These companies relied on annual budgeting forecasts and had difficulties responding to individual markets. Financial forecasting was not particularly fluid and would not efficiently react to external factors or changes made by the company.
In order to better optimize financial models on a global scale, technology companies incorporate financial forecasting through machine learning. The financial platform that was assembled would allow them to test spending levels regarding company-specific goals from sustainable growth to profitability. The developed software has significantly increased tech companies’ capabilities with regards to choosing their budgets for specific markets and to their marketing strategies. For example, now they can examine the profitability for a given market as a result of their marketing spend during a period of time. The models can handle a variety of data inputs and different prioritizations to yield various results. The various models have allowed companies to minimize spending and maximize product usage. Lastly, tech companies have recently introduced the use of deep learning technology to better predict financial models with reliance upon user-level metrics.
Pharmaceutical companies previously created their production schedule manually and thus inefficiently produced their drugs, often resulting in conflicts on assembly. In creating these schedules, companies keep track of historical usage of what drugs were experiencing a larger demand during specific periods and would accordingly adjust production. The tracking of changeover was necessary to better produce the optimal levels of medicine. Lastly, carrying cost was a great concern as those with greater cost were to be produced as late as possible so as to keep costs down.
Pharmaceutical companies now utilize artificial intelligence in order to maximize efficiency in their pharmaceutical production, specifically utilizing intelligent scheduling. With the implementation of AI, efficiency soared and the production was heavily fluid and optimal. In addition, the company was better able to respond to changes in the marketplace as well as limit carrying cost. With a 12 month scheduling creation, some pharmaceutical companies saw a 16 percent overall efficiency creation and significantly reduced strain on managing their production capabilities as a result of artificial intelligence and autonomous planning and scheduling.
When constructing new commercial aircraft, extreme efficiency is necessary to minimize costs and increase speed. In addition, the complexity of such a project necessitates intense organization. Previously, teams would set out a plan in advance of such construction, mapping out in their minds the most efficient means of building the planes. However, there are bound to be inefficiencies and unforeseen delays, causing bottlenecks along the lines of manufacture.
In order to increase efficiency, airline manufacturers utilize autonomous scheduling software when constructing new commercial aircraft.The autonomous planning would prioritize certain tasks as well as create diagrams to demonstrate the reasoning for the various construction processes. In addition, the AI would be able to adapt to the addition of new tasks and priorities. The autonomous planning has successfully adapted to the real-time production variability and the building has become dynamic. Ultimately, the software has been able to resolve complex conflicts in scheduling to ensure the most efficient assembly of the aircraft.
The recruiting industry was able to effectively transform their operations with the inclusion of speech recognition software. Companies within the industry are hired to effectively find new employees for their clients’ companies according to what the client is seeking in future hires. The recruiting companies would be forced to codify their interviews and relay that information to their clients. Unfortunately, such a process was incredibly time consuming and delayed from their intended work of conducting such interviews and effectively evaluating all prospects.
Using speech recognition software, recruiting companies are now able to delve into their interviews and provide their clients with concrete analysis of all candidates. The conversations are transcribed and then summarized in reports, highlighting qualities that their future employers are looking for. These reports are based on the reports previously created by recruiting companies, and allowed the machine learning software to have a foundation on which to build upon. The ability to show compliance with clients assuages any fears they may have and confirms the clients’ hopes of finding the best possible future employees.
Restaurants often operate largely on a paper-based system and struggle to maintain efficiency as they grow to operate many different locations. In addition to the management difficulties, there can be difficulties in organizing accounting and operating payment.
Restaurants that often struggle to maintain maximum efficiency in their scheduling and budget can implement autonomous planning to solve this issue. When implemented, autonomous software provides the most optimal scheduling as a result of the employees entering in their availability. The software ensures no overscheduling as well as ensuring that properly trained employees were available at all locations when needed. Lastly, the system allows for seamless accountability for employees logging their work for overtime or whether they are repeatedly late. Importantly, this software allows for the location managers to oversee projected wages for each week and how to decrease costs overtime and maximize profits.