Harsh Jani
Case Study / Retail, e-commerce and fulfilment

SunShoppers UK

Improving operational visibility across inventory, fulfilment and team workflows.

Independent consulting engagementJanuary 2025 – January 2026
AI-generated illustrative image of e-commerce fulfilment operations

Illustrative artwork — not a photograph of the client’s premises.

Project overview

An independent consulting engagement supporting an online retail operation spanning grocery, general retail and jewellery. The work centred on connecting stock records, sales channels, warehouse activity and staff routines so day-to-day operations became easier to monitor and manage.

The business challenge

The business needed better visibility from inbound goods to dispatched orders. Inventory information, marketplace activity and warehouse routines had to stay aligned; inconsistent checks and fragmented workflows could create errors, unnecessary rework and delays.

My role & scope

Independent consultant supporting the operating team on inventory controls, fulfilment processes, data visibility and staff adoption. Implementation was collaborative; the reported results reflect the wider team and business, not the work of one person in isolation.

How the work was approached

01 / Understand the flow

Reviewed how goods moved from suppliers through receiving, storage, picking, packing and customer dispatch; identified points where records or responsibilities could become disconnected.

02 / Improve stock controls

Supported barcode-based SKU identification, structured stock checks and clearer reporting to compare warehouse records with the operational system.

03 / Connect sales and fulfilment

Worked across Shopify, Amazon and eBay-related workflows to improve order visibility, status tracking and coordination between platforms and fulfilment activity.

04 / Strengthen daily execution

Helped standardise routines, staff coaching and dashboards to make exceptions easier to identify and recurring work more consistent.

05 / Support replenishment

Contributed to demand-forecasting and stock-replenishment processes so purchasing discussions could better reflect actual inventory movement.

Workstreams & practical outputs

Outcomes & highlights

70% → 90%Reported on-time delivery rate
81% → 98%Reported inventory accuracy
50+ hrsReported weekly labour time saved
98.3%Reported forecast accuracy in final four months

Client-reported movement from 70% to 90% on-time delivery represents a 20 percentage-point change; inventory accuracy from 81% to 98% represents a 17 percentage-point change. The business also reported 50 labour hours saved weekly (client-reported) and 98.3% forecast accuracy for the final four months. These measures come from client/project reporting with differing source comparison periods and labour baselines, so they are not presented as independently verified causal effects.

Supporting dashboard evidence

Selected cropped performance screenshots are included for context. They are records from the project, not AI-generated visuals.

Cropped SunShoppers delivery performance dashboard
Delivery performance dashboard
SunShoppers inventory and labour dashboard
Inventory and labour efficiency dashboard

What this project demonstrates

The biggest operating gains often come from getting people, process and system records to agree. A dashboard becomes useful only when receiving, picking and replenishment decisions are backed by consistent daily routines.

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FINANCIAL-IMPACT SCENARIO · FOR REVIEW

How labour time can translate into capacity value

Using a proposed baseline of 50 hours saved per week × 52 weeks = 2,600 hours of annual staff capacity released. At an assumed £15 hourly rate, this represents an illustrative labour-capacity value of £39,000 per year. This is not necessarily a reduction in payroll costs or independently confirmed financial savings. The weekly baseline and yearly consistency should be checked against client records.

Separately, estimates of £500,000 annual overstock exposure and £2 million additional revenue remain unverified and unattributed; they should not be treated as demonstrated outcomes without source records and agreement on how they were calculated.