DATA INSIGHTS APPLIED TO BUSINESS CHALLENGES

Data from sales transactions was restructured to analyse customer purchasing patterns over time, rather than relying on aggregated monthly totals. By comparing order frequency, order value, and recency at a customer level, patterns began to emerge that were not visible in standard reporting. Additional segmentation was applied to group customers based on purchasing behaviour, allowing changes in engagement to be tracked more accurately across different customer types.

While overall sales remained consistent, a group of previously high-value customers had significantly reduced order frequency over a sustained period. This decline was offset by new or lower-value customers, masking the issue in standard reports. Further analysis showed that these customers had not fully churned, but had gradually reduced engagement—indicating a high risk of future churn.

The business was able to proactively engage with at-risk customers before full churn occurred. This led to improved retention, better visibility into customer behaviour, and a shift towards more proactive account management.

Production data was analysed across the full workflow, mapping each stage from raw materials to finished goods. Cycle times, wait times, and work-in-progress levels were measured, with analysis aligned to Lean principles. This enabled clear identification of delays and non-value-added activities across the process.

A consistent constraint was identified at a mid-process assembly stage, where longer cycle times caused a buildup of queued work and underutilisation downstream. The issue was driven by inefficient task sequencing and manual handling, resulting in increased waiting and motion. This bottleneck was masked in overall output, as upstream stages continued operating at full capacity.

By improving task standardisation, rebalancing workloads, and making minor layout adjustments, the business improved flow through the constrained stage. This resulted in a 15% increase in production output, reduced buildup of queued work, and more consistent throughput across the process. Improved visibility also enabled proactive management of future constraints, supporting ongoing continuous improvement.

Workforce data was analysed across multiple sources, including employee records, tenure, performance metrics, and feedback mechanisms such as engagement surveys and internal scoring frameworks. The analysis focused on identifying patterns in employee sentiment, demographics trends, changes in engagement over time, and correlations between these factors and eventual attrition. Predictive models were then developed to highlight employees at higher risk of leaving, based on historical trends and behavioural indicators. Segmentation across departments, roles, and tenure groups provided deeper insight into where risks were most concentrated.

The analysis identified consistent patterns leading up to employee turnover, including declining engagement scores, reduced participation in feedback initiatives, and changes in performance trends. These signals often appeared months before an employee exited, but were not previously tracked or connected. Additional segmentation across tenure, role type, and workforce demographics highlighted areas where attrition risk was more concentrated, particularly within specific employee groups and stages of the employee lifecycle.

By introducing predictive monitoring and consolidating key indicators into a single view, the business was able to proactively address retention risks before they resulted in employee exits. This led to improved retention in key teams and a more targeted approach to engagement initiatives. The business also gained a more forward-looking view of workforce trends, enabling better planning and a stronger, data-driven People & Culture strategy.

A detailed audit was conducted on the margin calculation logic, tracing reported figures back to source transaction data. Sales, cost, and adjustment entries were independently recalculated to validate how margins were being derived within reporting layers. Particular focus was placed on how credit notes, rebates, and cost adjustments were being treated, along with consistency of logic across different reports and dashboards.

The audit identified that credit notes and cost adjustments were not being correctly incorporated into margin calculations. In some cases, revenue was reduced without the corresponding cost adjustment, artificially inflating margin percentages. This led to an overstatement of margin performance, particularly in high-volume product lines, masking underlying profitability issues and influencing pricing decisions that further eroded margins.

By correcting the calculation logic, reported margins aligned with actual financial performance, providing a true view of profitability. This enabled the business to adjust pricing strategies and refocus on underperforming products. As a result, margin accuracy improved and contributed to a measurable uplift in profitability, while the introduction of validation checks ensured ongoing confidence in reporting and decision-making.

A detailed validation was undertaken to align CRM activity data with ERP sales transactions at a granular level. Customer interactions recorded in the CRM were mapped to corresponding accounts and compared against subsequent sales within defined time windows. The analysis focused on consistency of customer identifiers, timing differences between systems, and how data was structured across both platforms. Additional checks were introduced to identify duplicate records, missing links, and variations in customer naming or account hierarchies.

The analysis revealed a small but consistent gap between recorded CRM activity and actual sales linkage. A portion of activity was tied to duplicate or mismatched customer records, or not aligned to the correct account structure used in the ERP system. While the discrepancy was relatively minor at a total level, it diluted the accuracy of conversion metrics and understated the effectiveness of certain sales efforts. As a result, some high-performing activity was not being correctly attributed to resulting revenue.

By aligning customer mapping between systems and introducing validation rules, the business gained a clearer and more accurate view of sales performance. Conversion metrics became more reliable, allowing management to better understand which activity was genuinely driving results. This led to improved confidence in reporting and more informed decision-making. Ongoing validation checks were also implemented to ensure consistency between CRM and ERP data over time.

A centralised dashboard was developed to track sales performance against targets in real time, with clear KPI visuals highlighting variance and trends. The dashboard provided breakdowns by branch and salesperson, allowing performance to be assessed at multiple levels within the business. Visual elements such as variance indicators, trend lines, and conditional formatting were used to quickly highlight areas of concern or strong performance. The design focused on simplicity and clarity, ensuring key insights could be understood at a glance.

The visualisation revealed performance gaps that were not clearly visible in previous reports, particularly at the salesperson level. While some branches were meeting targets overall, individual performance varied significantly within teams. It also highlighted consistent underperformance in specific areas that had previously gone unnoticed due to aggregated reporting.

With clear and accessible performance insights, management was able to take a more targeted approach to improving results. Underperforming areas were addressed more quickly, and high-performing individuals and branches were identified and leveraged as benchmarks. This led to improved accountability, faster decision-making, and stronger alignment between sales activity and targets.

A visual dashboard was developed to map the full order processing pipeline, tracking orders across each stage from entry through to dispatch. Key metrics such as order volumes, ageing, and stage duration were incorporated to provide a clear view of workflow progression. The dashboard enabled filtering by department, order type, and timeframe, allowing operational performance to be analysed in detail. Visual cues were used to highlight delays and backlogs, making issues immediately visible.

The visualisation revealed that delays were not evenly distributed across the process, with specific stages consistently causing bottlenecks. Orders were building up at certain points in the workflow, while other stages had available capacity. These inefficiencies were not visible in previous reporting, which focused only on total orders processed rather than flow through each stage.

With improved visibility, the business was able to better manage workloads and address delays as they occurred. Bottlenecks were reduced through more effective resource allocation and process adjustments, leading to faster order processing and improved on-time dispatch rates. The dashboard also enabled ongoing monitoring, allowing teams to proactively manage performance and maintain efficient operations.

An automated data pipeline was developed to streamline data extraction, transformation, and reporting across systems. Data from core platforms was integrated into a centralised database, with scheduled refresh processes ensuring information was consistently up to date. Existing manual steps were reviewed and replaced with automated logic, while validation checks were introduced to maintain data accuracy throughout the process. Dashboards were then connected directly to the automated data source, removing the need for manual intervention.

The automation process highlighted inefficiencies and inconsistencies that had previously gone unnoticed, including duplicated effort across teams and variations in how data was prepared for reporting. It also revealed that a significant portion of time was being spent on low-value tasks such as data extraction and formatting, rather than analysis. Once automated, reporting became more consistent and reliable across the business.

By automating data flows, the business significantly reduced manual effort and improved the timeliness and accuracy of reporting. Reports that previously took hours to prepare were made available in near real time, allowing faster and more informed decision-making. This freed up internal resources to focus on higher-value analysis and continuous improvement, while ensuring reporting remained consistent, scalable, and reliable.

An automated reporting solution was implemented to provide real-time visibility of inventory across all locations. Data from the ERP system was integrated into a centralised data model, with scheduled refresh processes ensuring stock levels, movements, and availability were consistently up to date. Key metrics such as on-hand stock, committed quantities, and available inventory were incorporated into a visual dashboard, allowing users to quickly assess stock positions. Manual reporting processes were removed, and data was made accessible through a single, reliable source.

The automated solution revealed inconsistencies between perceived and actual stock availability, particularly where committed stock was not previously considered in reporting. It also highlighted slow-moving inventory and imbalances across locations that were not visible in manual reports. With clearer visibility, the business was able to better understand true stock positions and identify areas for improvement.

By automating inventory reporting, the business gained accurate, real-time visibility of stock across the organisation. This improved decision-making around purchasing, allocation, and fulfilment, reducing stockouts and excess inventory. The removal of manual processes also saved significant time and reduced reporting errors, enabling teams to focus on managing inventory more effectively rather than compiling data.

A detailed analysis was conducted on inventory data, focusing on product dimensions, storage requirements, and movement patterns. Products were categorised based on size, handling needs, and frequency of movement, allowing storage zones to be designed accordingly. Racking configurations were then developed to align with these requirements, ensuring appropriate use of vertical space while maintaining accessibility. The layout was structured to support efficient flow, with high-movement products positioned for easier access and slower-moving items allocated to less accessible areas.

The analysis revealed that a significant portion of warehouse space would have been underutilised using a standard racking approach, particularly for products with non-uniform sizes or lower turnover. It also highlighted the importance of aligning storage design with product movement patterns, as a small number of high-velocity items accounted for a large share of handling activity.

By designing the warehouse layout around actual product attributes and movement patterns, the business was able to optimise both storage and operational areas. Inwards and dispatch zones were strategically positioned to support efficient flow through the warehouse, reducing unnecessary movement and congestion. Storage capacity was maximised through appropriate racking configurations while maintaining easy access to high-priority inventory. This resulted in a more organised warehouse, improved picking efficiency, and a smoother end-to-end handling process. The data-driven design also provided a scalable foundation for future growth, ensuring the warehouse could adapt as product ranges and demand evolved.

A detailed analysis was conducted on product dimensions, pallet configurations, and storage requirements to assess how effectively existing racking was being used. Rack heights were reviewed against product profiles to identify unused vertical space and opportunities for reconfiguration. Innovative racking solutions were explored, including double-deep/drive-in racks and alternative storage methods better suited to varying product sizes. The layout was redesigned to optimise both vertical and horizontal space while maintaining safe and efficient access.

The analysis revealed that many rack locations were not optimised for the products stored within them, resulting in significant wasted racking space. Standardised racking configurations were limiting capacity, particularly for products with smaller or irregular dimensions. It became clear that aligning racking design more closely with product attributes could unlock substantial additional storage without increasing the warehouse footprint.

By reconfiguring racking based on product height and implementing more flexible and innovative storage solutions, the business was able to significantly increase storage capacity within the existing warehouse. Storage locations were redesigned to better align with product characteristics and movement patterns, improving product placement and enabling faster putaway and retrieval. The optimised design supported more efficient day-to-day operations and provided a scalable foundation for future growth, without the need for major capital investment.