Skip links

Transforming Retail Excellence: Boosting Sales Forecasting and Operational Efficiency with AI & Machine Learning Operations

Jump To Section

AI powered transformation in retail

In the dynamic landscape of retail, maintaining a competitive edge is essential. Faced with the complexities of inventory management, sales forecasting, and operational efficiency, a leading apparel company recognized the urgent need for innovation. This prompted a strategic partnership with Altimetrik, aiming to leverage AI integration for enhancing processes, refining decision-making, and ultimately elevating overall retail performance.

The company’s Merchandising & Planning team embarked on a mission to harness the potential of AI, with a clear goal: to refine decision-making processes crucial for both seasonal and in-season sales. This marked the beginning of a collaborative effort with Altimetrik, aimed at seamlessly integrating Machine Learning Models into their operations, thus addressing the complexities of retail management, and enhancing their competitive position in the market.

Before the transformation, the company faced significant hurdles. Their ML forecasting modules operated in isolation, leading to underutilization and inefficiencies. Prolonged experimentation cycles and deployment bottlenecks further hindered progress, while the lack of monitoring systems impeded effective decision-making. Undeterred by these challenges, the company set ambitious objectives: to seamlessly integrate AI into their operations, establish structured workflows, and enhance precision in decision-making processes.

With Altimetrik’s expertise, a comprehensive assessment was conducted, laying the foundation for a cutting-edge cloud-native architecture. Leveraging tools like AWS SageMaker and Snowflake, alongside advanced AI/ML libraries, they established a configuration-driven microservices architecture. This framework not only facilitated scalable training and analytics pipelines but also significantly reduced time to market, enabling swift model evaluation.

The results were transformative!

Streamlined deployments led to a 50% reduction in deployment time, enhancing operational efficiency. Effortless model management became a reality with a 90% decrease in manual tracking efforts through an automated model registry. The company witnessed a 40% efficiency boost in their model portfolio, thanks to automated monthly retraining of 20 ML models. Timely decision support was achieved with 500K daily forecasts generated across geographies, facilitating agile decision-making. Furthermore, a 25% increase in model accuracy was achieved through enhanced experimentation methodologies.

As the partnership between the premium apparel company and Altimetrik evolves, attention now shifts to future enhancements. These include the integration of advanced features such as Feature Store, LLMOPS, GenAI, and Vector Database capabilities. As they continue to leverage the unified platform provided by Altimetrik, they are poised to drive sustained growth and innovation in the dynamic athletic apparel market.

In conclusion, this journey of AI-powered transformation exemplifies the potential of technology to revolutionize retail operations. By seamlessly integrating advanced ML models, the apparel company achieved enhanced decision-making processes, improved operational efficiency, and gained a competitive edge in the dynamic retail landscape. 

Picture of Altimetrik

Altimetrik

Latest Reads

Subscribe

Suggested Reading

Ready to Unlock Your Enterprise's Full Potential?

Vikas Krishan

Chief Digital Business Officer and Head of the EMEA region

Vikas (Vik) Krishan serves as the Chief Digital Business Officer and Head of the EMEA region for Altimetrik. He is responsible for leading and growing the company’s presence across new and existing client relationships within the region.

Vik is a seasoned executive and brings over 25 years of global experience in Financial Services, Digital, Management Consulting, Pre- and Post-deal services and large/ strategic transformational programmes, gained in a variety of senior global leadership roles at firms such as Globant, HCL, Wipro, Logica and EDS and started his career within Investment Banking. He has developed significant cross industry experience across a wide variety of verticals, with a particular focus on working with and advising the C-Suite of Financial Institutions, Private Equity firms and FinTech’s on strategy and growth, operational excellence, performance improvement and digital adoption.

He has served as the engagement lead on multiple global transactions to enable the orchestration of business, technology, and operational change to drive growth and client retention.

Vik, who is based in London, serves as a trustee for the Burma Star Memorial Fund, is a keen photographer and an avid sportsman.

Megan Farrell Herrmanns

Chief Digital Officer, US Central

Megan is a senior business executive with a passion for empowering customers to reach their highest potential. She has depth and breadth of experience working across large enterprise and commercial customers, and across technical and industry domains. With a track record of driving measurable results, she develops trusted relationships with client executives to drive organizational growth, unlock business value, and internalize the use of digital business as a differentiator.

At Altimetrik, Megan is responsible for expanding client relationships and developing new business opportunities in the US Central region. Her focus is on digital business and utilizing her experience to create high growth opportunities for clients. Moreover, she leads the company’s efforts in cultivating and enhancing our partnership with Salesforce, strategically positioning our business to capitalize on new business opportunities.

Prior to Altimetrik, Megan spent 10 years leading Customer Success at Salesforce, helping customers maximize the value of their investments across their technology stack. Prior to Salesforce, Megan spent over 15 years with Accenture, leading large transformational projects for enterprise customers.

Megan earned a Bachelor of Science in Mechanical Engineering from Marquette University. Beyond work, Megan enjoys playing sand volleyball, traveling, watching her kids soccer games, and is actively involved in a philanthropy (Advisory Council for Cradles to Crayons).

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes