Generating future estimates from historical data can be very useful for enterprise planning across areas like sales or revenue management, operating expenses and costs management, infrastructure management etc. A major challenge in the area of forecasting is that different units, for instance product SKUs may exhibit different historical patterns, and hence not allow for a one-size-fits-all modelling approach. Mphasis AI for Forecasting overcomes this problem through a combination of ensemble learning and auto model selection to provide better and more consistent forecasts. The service allows users to perform the following tasks:

1. Automatically identify the set of optimum algorithms for a given pattern of historical time series data
2. Combine the results through ensemble learning to provide best forecast for future as the output
3. Generate more consistent results for future predictions using automatic model selection

Mphasis AI for Forecasting leverages several customizable machine learning models which have the ability to generate the ensemble of algorithms to best fit the provided input data. It leverages a large repository of proprietary processing modules to solve enterprise use cases that require time series forecasting such as retail sales, and product demand forecasting, operating expenses forecasting, cloud database and compute cost forecasting, passenger and web traffic forecasting, ticket forecasting etc.

Assessment & Workshops

Our Assessment and Workshops help clients get started with AI for Forecasting. This engagement enables clients to plan and identify the relevant business problems which could be candidates for solving using AI for Forecasting. Assessments are a fast way of identifying if AI for Forecasting is the relevant technology to leverage for solving a business problem. In the workshops, the Mphasis AI/ML experts, SMEs, architects, and data scientists work closely with the client stakeholders to understand the system and data. The engagement includes activities such as stakeholder interviews, assessment and user journey workshop, identification of frameworks and tooling, gap analysis, understanding and assumptions, recommendation of solution, and report-out. The typical duration of the assessment and workshop exercise is spread across 3-4 weeks and the outcome of this exercise is a prioritized set of use cases along with recommended infrastructure to solve the problems, recommendations on frameworks and tooling, along with reference architecture.

Algorithm Development and Implementation

Algorithms Development and Implementation is a 2–3 month long engagement, where we bring our AI for Forecasting IP as well as customizations to solve the specific client problems. Mphasis AI for Forecasting is used in multiple verticals like pharma, retail and manufacturing. Our services are powered by our patented AI/Ml platforms and frameworks such as DeepInsights, PACE-ML, HyperGraf and InfraGraf. The Mphasis IP consists of 200+ pre-built and customizable Machine Learning, Deep Learning and Natural Language Processing algorithms. Our IP helps clients arrive at actionable recommendations faster and provide measurable results across scenarios. Our experience in AI/ML algorithm development and implementation are spread across several industries as well as use cases such as improved customer experience, recommendation of right products at the right time for the right customer, profit margin maximization, faster checkout processes, improved productivity, reduced disputes, reduced false positives, anomaly detection, predictive analytics recommendations, text categorization on large document corpus, natural language generators etc.