Demand Forecasting Accelerator: 12-Wks Implementation

Demand Forecasting is an area of priority for companies. A broken forecasting system leads to overstocking and understocking issues, which in turn leads to financial losses and loss of brand equity. Factoring in different forecasting horizons for different product types, ranging from raw materials to finished goods, absence of a robust demand forecasting solution leads to problems at all levels of the supply chain, from the supply side to the demand side.

Sigmoid's Demand Forecasting Accelerator consists of a host of ML models for sensing different demand patterns to improve forecast accuracy and enhance visibility in the supply chain by analyzing the impact of a range of variables that affect demand—from historical demand patterns to internal business decisions and even external factors beyond trend and seasonality, solving the fundamental problem of understocking /overstocking. Consisting of a feature store with an exhaustive list of internal and external datasets, simulation tools are also provided to the business teams for running scenarios catering to specific events to aid in the planning process. With a quick onboarding and speed to market, it also consists of inbuilt model performance and monitoring tools help to gauge issues of data drift, concept drift, pipeline failures, etc.

The steps for Implementation:

1: Assessment & Feasibility Analysis -Understanding the client's business goals, challenges, gap analysis and available datasets. -Conducting a thorough assessment of the client's current data infrastructure, including data quality, availability, and accessibility. 2: Pilot -Deciding on specific SKUs, Channels in a defined region within a geography, to build the model for. Defining the KPIs and success metrics, viz, WAPE, MAPE, Demand Fulfillment parameters, OTIF, Trend capturing etc -Evaluating the performance of the Pilot against defined success criteria making adjustments based on feedback 3: Scaling to other SKUs, Products, Brands or Geos: -Expanding the scope of the pilot. Developing a detailed implementation and scaling plan -Fine Tuning data models, algorithms, or predictive analytics techniques -Integrating with relevant systems like SAP, Demand Sensing/management systems and Data visualization tools to enable seamless data flow and decision-making, ensuring compatibility and scalability 4: Adoption and Feedback -Providing training and documentation to empower the stakeholders to interpret and utilize the insights generated by the solution effectively 5: Monitoring, Optimization and Maintenance: -Implementing monitoring and performance tracking mechanisms to evaluate the ongoing effectiveness of the solution, monitoring model performance, model/data drifts, data quality, and business impact metrics 6: Review and Handover: -Conducting a post-implementation review to assess the overall success of the solution and its alignment with business objectives. -Documenting lessons learned, best practices, and recommendations -Establishing a support and maintenance plan to address any post-implementation issues and ensure the long-term success of the solution

Being developed in accordance to the Well Architected Framework of Azure, the following Azure workloads are used:

1. Azure Data Factory (ADF) for orchestrating data integration pipelines to integrate data from diverse sources
2. Azure Data Lake Storage, serving as the underlying storage layer
3. Microsoft Purview for a Unified Data Governance
4. Azure Machine Learning
5. Integrating and analysing large data sets
6. Power BI for Business Intelligence