Numerous organizations are realizing that their systems contain vast amounts of data that can be utilized for deeper insights, increased efficiency, and enabling the customer experience and business optimization of the future. The revolution in scheduling optimization and automation is well underway. According to a report published in June, 2021 revenue for decision support software is predicted to grow from USD 62.35 billion in 2021, up to USD 997.77 billion in 2028, at a compound annual growth rate (CAGR) of 40.2%. This is because businesses are constantly on the lookout for new insights into their operations that can help them gain a competitive edge.
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Numerous organizations are realizing that their systems contain vast amounts of data that can be utilized for deeper insights, increased efficiency, and enabling the customer experience and business optimization of the future. The revolution in scheduling optimization and automation is well underway. According to a report published in June, 2021 revenue for decision support software is predicted to grow from USD 62.35 billion in 2021, up to USD 997.77 billion in 2028, at a compound annual growth rate (CAGR) of 40.2%. This is because businesses are constantly on the lookout for new insights into their operations that can help them gain a competitive edge.
However, implementing advanced decision support solutions that are tailored to a business's specific requirements is complex. As a result, despite the fact that the vast majority of businesses recognize the value of algorithms, 75% of businesses in the United States, the European Union, and China are exploring these technologies, but only 34% of businesses have adopted advanced decision support capabilities. This discrepancy between intended and actual implementation rates should not be overlooked due to the complexity of such projects. To ensure that technologies such as mathematical optimization and machine learning deliver on their promises, data scientists must understand what data and predictions they are seeking and why. It is neither feasible nor cost-effective for the majority of organizations, particularly those that have not emerged and matured in the technology ecosystem, to acquire sufficient domain knowledge and expertise in-house to build solutions.
Mathematical optimization has not yet been written as a how-to manual for business applications. To commercialize algorithms, a company must develop complex, use-case-driven systems. Developing and customizing mathematical models is only one aspect of the puzzle. To adopt mathematical optimization and increase its success rate, a significant investment in numerous fundamental elements is required. Numerous factors influence the ideal and optimal algorithmic components of a solution. Monitoring, customizing compliance, cloud architecture, business thresholds, measurements, validation, trust, and feedback are just a few of these. In comparison to previous technological pivots in recent years, mathematical optimization implementation is still in its early stages.
Each company’s scheduling requirements are unique, necessitating small customization. As a result, most verticalized or use case-based solutions are out of reach for the majority of businesses and use cases.
Businesses can hire a data science team to build algorithms in-house, incurring significant costs and a lengthy time to value on their projects.
Adoption of mathematical optimization is currently failing at an astonishing rate. The root causes of adoption challenges and failures, regardless of industry, are business-related issues, technical issues, and their intersection. For instance, a solution must be adaptable in order to meet the requirements of a particular environment, which may include unique data, constraints, or requirements. Additionally, stability and robustness must be achieved. Read the following article to learn more about how robustness can be accomplished using two criteria: Algorithm selection using machine learning, predicting the best algorithm
Businesses can implement and scale algorithms using mathematical optimization as a service at a fraction of the cost of hiring and retaining a full-time data science team in-house. The vendor's participation in the process is also referred to as "Service." As an integrated platform, companies that provide optimization as a service provide everything from problem definition to maintaining the algorithms and expanding it to new use cases, to building the model, deploying it in production, and maintaining it in real-world conditions.
Optimization as a service is a new model that grew out of the recent wave of software-as-a-service, or SaaS, adoption (Software as a Service). In the late 1990s and early 2000s, an industry-wide shift toward web-based application delivery, initially for external customer use but increasingly for internal enterprise delivery, gave birth to SaaS as a business model. During this time period, the demand for rapid software delivery, including initial deployments and feature additions, increased.
Cloud-based solutions were only a matter of time before they were adopted to enable the deployment of mathematical optimization solutions.
It enables businesses to implement customized optimization solutions while investing minimally in domain expertise in data science, e.g. operations research. Vendors of optimization-as-a-service are familiar with vertical industries and have developed sophisticated mathematical models to efficiently address their unique use cases. Providers can now offer their optimization as a service solution via a cloud-based service that can be accessed, refined, and expanded in previously unthinkable ways.
A growing number of businesses are adopting the optimization as a service model, primarily because these solutions are applicable to a broad range of verticals. Because providers of optimization as a service maintain their infrastructure, businesses can benefit from these services at a reduced cost. Companies were unable to adopt mathematical optimization due to a lack of commercially available or in-house optimization challenges and operations research expertise. The model enables them to rapidly adopt, deploy, and operate effective and customized solutions tailored to their unique use case, data, and business requirements.
For instance, businesses can deploy advanced mathematical optimization solutions via a service rather than developing and maintaining their own model in production, resulting in annual savings of tens of thousands of dollars. Additionally, these solutions are more adaptable, simple to use, and scalable. With optimization as a service, businesses can implement highly customized, cutting-edge algorithmic solutions while remaining focused on their core business and avoiding the diversion of valuable resources and attention to complex new areas of development.
Each use case is distinct. Even within the same vertical or operational area, each manufacturer utilizes unique data to accomplish unique goals based on a unique business logic. All of these specifications must be translated and built into the model in order for the AI solution to be a perfect fit for the data, usability, and business needs. The customized solution is not an end in and of itself, but rather a means to an end of generating value through AI. ROI is only possible with a customized solution, as standard solutions do not address the unique needs and constraints of the business, and thus are ineffective.
While achieving production can be extremely challenging, it is only half the battle. The second half of the battle is to maintain production in order to prevent the model from deviating as data and circumstances change. Maintaining AI in production requires version control of data and models, model updates, optimization of human-machine interaction, monitoring of the model's robustness and generalization, and continuous input noise detection and correlation. Maintenance can be a particularly difficult and costly aspect of supporting AI solutions.
Companies that adopt innovation in stages find better value sooner than those that implement algorithms across the board. To avoid the formation of a patchwork of isolated use-case-based solutions that are incapable of operating holistically, it is vital to maintain consistency in the deployment approach.
Solvice has developed the industry's first uniform and configurable optimization platform. We provide hyper-customized, production-ready algorithms as a service, enabling businesses to rapidly scale their innovation and generate revenue from algorithms. While many other solutions take months or even years to achieve production-grade optimization, Solvice APIs enable businesses to achieve production-grade optimization in weeks.
The Solvice Optimization Platform is provided as a service to ensure ongoing improvement and value growth over time. Solvice's partnership with organizations enables them to rapidly build, deploy, run, and maintain their algorithms. There is no requirement for data scientists to build, train, or maintain the model. Solvice is committed to keeping solutions on track, even as data and requirements change. Complex architecture and maintenance considerations and costs are mitigated and streamlined, which frequently burden projects post-implementation. Solvice collaborates with organizations to align their needs, use cases, business rules, and logics, resulting in synergetic rather than siloed mathematical models.
As mentioned previously, each use case is unique: even within the same vertical or operational area, each company uses unique data, has unique objectives, and is guided by its own business logic. All of these specifications are meticulously incorporated into the solution, ensuring that it meets all usability and business requirements.
The Solvice platform is designed to be a robust, end-to-end state-of-the-art optimization platform that encompasses much more than a simple solver. The platform incorporates cutting-edge technologies such as monitoring, security, and compliance, intelligent feedback, and explainability. Among these integrated technologies are robustness and machine learning technology that can iterate on itself and improve its results with each solve.
The intuitive and user-friendly dashboard of the Solvice Optimization Platform enables a variety of stakeholders, not just data scientists, to manage, monitor, and maintain the solution. By enabling more employees to take control of algorithms through data visualization and simple, actionable insights, the platform helps eliminate bottlenecks in the process, allowing for faster generation of value. Additionally, the interface's ability to manage multiple use cases concurrently contributes to exponentially faster value creation.
While the Solvice Optimization platform enables enterprises in resolving specific and unique use cases, it also enables a company-wide transformation of operations through the use of algorithms for automation and optimization. Its modular technology stack enables enterprises to efficiently expand their goals and address each and every use case within the organization on a single platform; additionally, by utilizing a single platform, enterprises can ensure data and process continuity across their organizations.
Solvice's mission is to form partnerships that enable the most advanced enterprises to reap the benefits of mathematical optimization quickly and sustainably, while absolving them of the risks and costs associated with its development and maintenance.
