Pick System Modernization: Documenting Legacy Industrial Part Catalogs for the Modern Web

The $3.6 trillion global technical debt crisis isn't found in modern microservices; it’s buried in the "green screens" of terminal emulators. For industrial manufacturers and distributors, the "Pick" system—a MultiValue database architecture dating back to the 1960s—remains the backbone of part catalogs, inventory management, and order fulfillment. But as the engineers who built these systems reach retirement age, companies are left with a "black box" problem: business logic that is critical to operations but impossible to document or migrate without risking a total system collapse.

70% of legacy rewrites fail or exceed their timelines because they attempt a "Big Bang" migration without understanding the source material. When it comes to Pick system modernization, the challenge isn't just the data; it's the 40 years of undocumented business rules hidden within DataBasic subroutines.

TL;DR: Successful Pick system modernization requires moving away from manual "code archaeology" toward Visual Reverse Engineering, reducing extraction time from 40 hours per screen to just 4 hours using Replay.

The Archaeology Problem: Why Pick System Modernization Stalls#

Most enterprise architects approach Pick system modernization by attempting to read the source code. This is a mistake. In a Pick environment (whether it’s Rocket UniVerse, UniData, D3, or jBASE), the "source of truth" isn't just the code—it's the way users interact with the multi-valued data structures.

67% of legacy systems lack any form of up-to-date documentation. In the industrial sector, this means the logic for calculating "available-to-promise" (ATP) inventory or complex tiered pricing for part catalogs is effectively a trade secret held by a terminal screen.

Traditional modernization strategies usually fall into three high-risk categories:

The "Big Bang" fails because the requirements are gathered from interviews, not reality. When you ask a user how they process a part return in a Pick system, they give you the "happy path." They forget the three "workarounds" they use to bypass 30-year-old bugs. Manual documentation attempts to capture this, but at 40 hours of manual effort per screen, the project is underwater before the first React component is even scaffolded.

From Green Screens to React: The Replay Methodology#

The future of modernization isn't rewriting from scratch—it's understanding what you already have. Replay changes the paradigm by using Visual Reverse Engineering. Instead of digging through obfuscated DataBasic files, you record a real user performing a workflow. Replay’s engine then analyzes the state changes, network calls, and UI transitions to generate documented React components and API contracts.

Step 1: Workflow Capture#

Instead of technical interviews, we record the "expert users" in the warehouse or the front office. As they navigate the Pick system’s terminal emulator to look up industrial parts, Replay captures every field, validation rule, and hidden state change.

Step 2: Component Extraction#

Replay’s Blueprints (Editor) takes the recorded session and identifies patterns. It recognizes that a specific sequence of terminal inputs represents a "Part Search Result" grid. It then generates a clean, modern React component that mirrors that logic.

typescript
// Example: Generated React Component from Pick System Extraction
// This component preserves the legacy multi-value logic while using modern hooks.

import React, { useState, useEffect } from 'react';
import { PartDetails, InventoryStatus } from '@/types/catalog';

interface PartLookupProps {
  partNumber: string;
  onSelect: (part: PartDetails) => void;
}

export const LegacyPartCatalogBridge: React.FC<PartLookupProps> = ({ partNumber, onSelect }) => {
  const [loading, setLoading] = useState(false);
  const [inventory, setInventory] = useState<InventoryStatus | null>(null);

  // Business logic preserved from legacy 'GET.INV.STATUS' subroutine
  const fetchInventoryData = async (id: string) => {
    setLoading(true);
    try {
      // Replay generates the API contract based on observed terminal behavior
      const response = await fetch(`/api/v1/pick/inventory/${id}`);
      const data = await response.json();
      setInventory(data);
    } finally {
      setLoading(false);
    }
  };

  return (
    <div className="p-4 border rounded-lg shadow-sm bg-white">
      <h3 className="text-lg font-bold">Part: {partNumber}</h3>
      {loading ? <span>Querying Pick System...</span> : (
        <div className="mt-2">
          <p>Warehouse Location: {inventory?.location}</p>
          <p>Qty on Hand: {inventory?.qty}</p>
          <button 
            onClick={() => onSelect(inventory as any)}
            className="mt-4 px-4 py-2 bg-blue-600 text-white rounded"
          >
            Update Catalog
          </button>
        </div>
      )}
    </div>
  );
};

💰 ROI Insight: Manual extraction of a complex industrial part catalog screen typically takes a senior dev 40-50 hours. Replay reduces this to 4 hours, representing a 90% reduction in labor costs for the discovery phase.

Documenting the "Undocumentable"#

The greatest risk in pick system modernization is the loss of business logic during the transition. In industrial manufacturing, a part isn't just a row in a database; it has complex associations (MultiValue attributes) that traditional relational databases struggle to map.

Replay’s Flows (Architecture) feature maps these relationships visually. By recording how data moves from a part lookup to a purchase order, Replay generates a visual map of the system architecture. This transforms the "black box" into a documented codebase that any modern full-stack developer can understand.

Preserving Data Integrity in Regulated Environments#

For industries like Healthcare and Government, "moving fast and breaking things" isn't an option. Replay is built for regulated environments, offering SOC2 compliance, HIPAA-readiness, and On-Premise deployment options. When modernizing a Pick-based system used for medical device tracking or government inventory, you cannot afford a documentation gap.

⚠️ Warning: Never attempt to migrate a Pick system without first generating an API contract. Direct database "lifts" usually fail because the application logic (the "how") is more important than the raw data (the "what").

The AI Automation Suite: Accelerating the "Strangler" Pattern#

The most effective way to handle pick system modernization is the Strangler Fig pattern: gradually replacing legacy functionality with new microservices until the old system can be decommissioned.

Replay’s AI Automation Suite accelerates this by automatically generating:

1. •API Contracts: Defining how the new React frontend will talk to the legacy backend.
2. •E2E Tests: Ensuring the new system behaves exactly like the old one.
3. •Technical Debt Audit: Identifying which parts of the Pick system are actually used and which can be retired.

Step 3: Generating the Modern Library#

Once the workflows are captured, Replay populates your Library (Design System). This ensures that every modernized screen across your industrial catalog uses consistent components, even if the underlying Pick subroutines vary.

typescript
// Example: Generated API Contract for Pick System Integration
// This ensures the modern frontend speaks the same language as the legacy MultiValue DB.

export interface PickPartRecord {
  id: string;          // Attribute 0
  description: string; // Attribute 1
  price: number;       // Attribute 2 (Converted from internal format)
  attributes: {
    color: string[];   // Multi-value Attribute 3
    size: string[];    // Multi-value Attribute 4
  };
  warehouseLogs: Array<{
    date: string;
    action: string;
  }>; // Sub-valued Attribute 5
}

/**
 * @generated By Replay Visual Reverse Engineering
 * Legacy Source: PART.MASTER.FILE
 * Validation Logic: Captured from screen 'INV-101'
 */
export const validatePartUpdate = (data: PickPartRecord): boolean => {
  // Logic extracted from observed user error states in terminal
  if (data.price <= 0) return false;
  if (data.attributes.color.length === 0) return false;
  return true;
};

Bridging the Talent Gap#

The median age of a Pick developer is rising every year. Within a decade, the "tribal knowledge" required to maintain these systems will be gone. Pick system modernization isn't just a technical upgrade; it's a knowledge preservation project.

By using Replay, you are essentially "video-recording" the logic of your most senior engineers and converting it into TypeScript. This allows you to hire modern React developers to manage your industrial catalogs without requiring them to learn 1970s syntax or terminal navigation.

💡 Pro Tip: Focus your first modernization sprint on the "Search" and "View" workflows. These provide the highest immediate value to users and are the easiest to extract using visual reverse engineering.

Frequently Asked Questions#

How does Replay handle MultiValue data structures during pick system modernization?#

Replay doesn't just look at the database; it looks at how the application interprets the data. By observing how a terminal screen displays multi-valued attributes (like multiple locations for a single part), Replay identifies the data structure and generates a corresponding JSON schema and React state model.

Can Replay work with "Green Screen" terminal emulators?#

Yes. Replay is designed to record and analyze any web-based or desktop-based interface, including common terminal emulators used to access Pick systems. It captures the visual changes and keyboard inputs to map out the underlying business logic.

We have no documentation for our 30-year-old system. Where do we start?#

Start with the workflows that have the highest support ticket volume. By recording a user performing these tasks, Replay generates the documentation for you. You move from "archaeology" to "extraction" in a matter of days, not months.

Is Replay a "no-code" tool?#

No. Replay is a Visual Reverse Engineering platform for engineers. It generates high-quality, editable React code and TypeScript definitions. It gives your developers a massive head start (saving 70% of the time), but keeps the power in the hands of your engineering team.

How long does a typical extraction take?#

While a manual rewrite of a complex industrial catalog might take 18-24 months, a Replay-led modernization typically moves from recording to a functional modern prototype in 2-8 weeks.

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Ready to modernize without rewriting? Book a pilot with Replay - see your legacy screen extracted live during the call.