Designing the Warehouse Control Tower Ecosystem

Built a scalable Control Tower that gives managers live visibility into bottlenecks, SLA risks, and overall warehouse performance. Introduced a modular process map that adapts across clients and reduces engineering dependency for updates.

B2B EnterpriseService DesignDiscovery CallsProduct Design
schedule Jan 2025 - Sep 2025

Problem Statement

The warehouse setup process was fragmented and relied on manual Excel sheets, creating data silos between sales and developers that led to high error rates and redundant data entry. Additionally, clients lacked real-time visibility and control, forcing them to rely on slow support tickets for even minor dashboard adjustments or report generation.

Solution Implemented

By replacing disconnected workflows with a centralized digital repository, we enabled transparency, traceability, and real-time validation across the entire project lifecycle. Additionally, we introduced visual process map monitoring that aligned with warehouse mental models.

The Team

Design Team - 1 Product Designer (Me)

Product Team - 1 Product Manager, 1 Scrum Master

Development Team - 1 Database Architect, 4 Front-end Engineers, 5 Back-end Engineers, 3 QA Engineers

Service Ecosystem

Existing Process :

Existing Process Flow

Redesigned Process :

Redesigned Process Flow

Introducing Central Repository and Setup

Target User: Warehouse Admin Team
User Goal: To set up parameters (like enabling/disabling discount, classifications and groups for robots etc) and add exhaustive list of machines (robots, conveyor belts etc) from the sales catalogue with minute details (like cost, version, dimensions etc)
Parameter Management Overview

1. From Fixed UI to Parameter Management:

Instead of hardcoding configurations, we designed a granular settings panel where users could adjust warehouse parameters, eliminating dependency on devs for each change.

Parameter Management Interface

2. Introducing the Setup Section:

Created a central repository where users could manage devices, systems, and other parameters. This allowed new warehouses to self-onboard without manual setup.

Setup Section Interface

Designing Quotation Process for Sales Team

Target User: Sales Team
User Goal: To create Sales Quotations that include details (quantity, cost, machine specifications, etc.) about the robots, conveyors, storage, software, etc. that the client decided to purchase. This involves multiple iterations and stakeholders, with extensive data entry and exports.
Quotation Creation Process

1. The Quotation Form

The quotation form was designed to handle a highly cognitive task by using progressive disclosure, guiding users through the information one section at a time rather than presenting everything at once.

Quotation Form

2. Data Entry in the Quotation

For quotation data entry, we enabled users to fetch items from an existing repository via a pop-up. The interaction was designed to support precise, structured actions-such as selecting an item first and then explicitly defining its units-ensuring accuracy and reducing manual input errors.

Data Entry Interface

Digital Maps for Warehouse Processes

Target User: Sales Team and Warehouse Experts
User Goal: To finalize the layout and interactions between the machines, robots, storage, etc. being purchased. Warehouse experts and the sales team collaborate iteratively with client stakeholders to ensure that the deal is executable. This involves multiple iterations and feedback cycles that may update the Sales Quotation.
Process Map Creation Process

Creating a Process Map involved designing a system-level diagram that represents warehouse operations in real time. Unlike a static flowchart, the map is data-synced and reflects the live state of processes and their components, helping users quickly assess whether each part of the operation is performing as expected.

The Process Map is generated directly from the approved quotation by fetching communications, automation vendors, and robots from the existing data. This reduced manual input and ensured consistency across teams, while attaching images of robots and devices improved fault diagnosis by enabling faster visual identification during operational issues.

Process Map Design Interface

Linking Digital Map to Data

Target User: Development Team
User Goal: Once the business deal is sealed and machines are finalized, developers establish data links between the database and physical machines to ensure real-time monitoring of the machines and processes in the warehouse.
Deployment Process

Once the process maps are developed in collaboration with the client and the tech team, the focus shifts to integrating the process map with backend services, enabling live tracking capabilities. The implementation team then moves on to first create an implementation team.

The implementation team then ensured that all artefacts and files were uploaded, linked, and deployed within the same platform. Keeping everything in one system made inconsistencies and failures visible in context, significantly reducing ambiguity and making troubleshooting faster and more effective.

Database Linking Interface

Monitoring by Warehouse Managers

Target User: Client Team Warehouse Managers
User Goal: To monitor the health of the robots and ongoing processes in the warehouse and take action, analyze errors, and review logs as needed.
Dashboard Monitoring Process

1. Process Map Monitoring

Provides process-level, real-time monitoring through a diagrammatic view of all defined warehouse workflows. Each process and its components can be evaluated in context, with errors or performance issues immediately flagged in red for quick identification.

Process Map Monitoring Dashboard

2. AI Assisted Dashboard

Enables users to interact with operational data via an AI chatbot to generate reports, create custom widgets, or retrieve specific tables and data points, reducing manual effort and accelerating access to insights.

AI Assisted Dashboard - Chat Interface AI Assisted Dashboard - Widget Creation