Benefits & Results

Enhanced accuracy and completeness of product data leading to improved demand forecasting and planning.
Streamlined data management processes, reduced manual effort, and better alignment of data across systems.
Optimized inventory, improved sales and profitability, and a more robust demand planning function for Speedo North America.

Immediate Benefits: Enhanced accuracy and completeness of product data leading to improved demand forecasting and planning.
Long-Term Impact: Streamlined data management processes, reduced manual effort, and better alignment of data across systems. This will result in more efficient operations, improved sales and profitability, and a more robust demand planning function for Speedo North America.

Background

Pentland Group is a multi-brand organization investing in retail and wholesale businesses within the sports, outdoor, and sports fashion sectors. Their brand Speedo North America (SNA) required a data cleanse initiative to improve demand planning and forecasting.

Challenges

Issue Identification: Pentland Brands faced several challenges with their current data management:

Absence of clean and appropriate data.
Restricted view of data issues; Inadequate knowledge of data estate.
Challenges in generating timely, accurate, and efficient demand planning and forecasting reports.

Issue Impact: These issues led to poor demand forecasting, inaccurate product attribute values, lack of systemic data governance, and increased manual effort for data validation. This resulted in inventory chaos, misled sales trends, and inefficiencies across sales margin, profitability, and workforce

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Solution

NowVertical conducted a comprehensive assessment and provided a data cleanse solution for Pentland Brands. This included identifying key challenges, mapping leakages in the product journey, and recommending a future state with clean data for Blue Yonder implementation.

Implementation

Data Collection and Analysis: Gathered data from Centric (PLM), SAP (ERP), and Blue Yonder (Demand Planning & Forecasting). Conducted workshops, interviews, and documentation reviews with business and technical stakeholders. Performed data analysis to identify completeness and conflicting patterns.
Data Cleanse Initiatives: Developed a systematic approach to cleanse data. Created a product data health card and revalidated data anomalies. Generated and implemented business rules for data validation
Recommendations and Roadmap: Proposed a future state with a streamlined data flow and automated checks. Provided a detailed roadmap for data cleanse and ongoing data management.