Lindt & Sprungli: Smart Factory Maintenance for Chocolate Production

The Challenge

Lindt & Sprungli’s Aachen factory runs around the clock. Conching machines, tempering units, and moulding lines operate in tight sequence — if one machine stops, the entire production line stalls. Downtime is not just inconvenient. It costs product, time, and money.

Before Spadoom got involved, the maintenance team worked with paper-based work orders and a shared spreadsheet. When a tempering unit flagged an error, a supervisor would walk to the maintenance office, fill out a form, and hope the right technician was available. Spare parts? Someone had to physically check the stockroom.

The problems were concrete:

• Slow dispatch: It took an average of 45 minutes from machine fault to technician arrival. Most of that was coordination, not walking.
• No machine history: Technicians arrived without context. They didn’t know what had been replaced last month or which components were nearing end-of-life.
• Blind spare parts management: The factory held over 3,200 unique parts across two stockrooms. Nobody had a real-time view of what was in stock. Parts got ordered twice — or not at all.
• Reactive maintenance only: The team fixed things when they broke. There was no structured preventive maintenance calendar. Critical machines like the ball mills and shell moulding stations had no scheduled service intervals.

Lindt needed a system that matched the precision of their chocolate-making process: structured, fast, and reliable.

The Approach

We deployed SAP Field Service Management (FSM) in three phases over fourteen weeks, working alongside Lindt’s maintenance leads and factory IT team.

Phase 1: Digital Work Orders and Mobile App (Weeks 1–5)

We replaced paper work orders entirely. Every machine in the factory — 187 assets total — was registered in SAP FSM with its full technical specification, location, and maintenance history imported from existing records.

Technicians received ruggedized tablets with the SAP FSM mobile app. When a fault occurs, the system creates a work order automatically. The technician sees the machine ID, fault description, location on the factory floor map, and the complete service history — all before they leave the workshop.

We configured role-based views: line supervisors see their area’s open tickets and priorities; the maintenance manager sees the full factory dashboard with SLA tracking.

Phase 2: Real-Time Spare Parts Integration (Weeks 6–9)

We connected SAP FSM to Lindt’s existing inventory system. When a technician opens a work order, they see which parts are needed and whether those parts are in stock — in real time.

If a part is unavailable, the system flags it immediately and suggests alternatives from the approved parts catalogue. Reorder thresholds trigger automatic purchase requests for high-consumption items like seals, bearings, and heating elements.

This eliminated the guesswork. Technicians stopped walking to the stockroom only to discover that the part they needed was out of stock.

Phase 3: Preventive Maintenance Scheduling (Weeks 10–14)

We built a structured preventive maintenance calendar covering all critical production assets. Each machine type has its own service interval based on operating hours, not calendar days — a conching machine running 20 hours a day gets serviced more frequently than one running 8.

The system tracks cumulative operating hours via sensor data feeds from the factory’s existing SCADA system. When a machine approaches its service threshold, SAP FSM auto-generates a preventive work order and assigns it to the next available qualified technician.

We defined three priority tiers:

• Tier 1 — Safety-critical: Emergency shutoff systems, pressure relief valves. Serviced within 24 hours of threshold.
• Tier 2 — Production-critical: Tempering units, enrobing machines, moulding lines. Scheduled during planned production pauses.
• Tier 3 — Support equipment: Conveyor belts, packaging stations. Batched into weekly maintenance windows.

The Results

Six months after go-live, the numbers tell the story.

• Unplanned downtime dropped 40%. Preventive maintenance catches issues before they cause line stoppages. The Aachen factory went from an average of 12 unplanned stops per month to 7.
• Machine uptime reached 99.2%. Across all 187 tracked assets, the factory maintains near-continuous operation during production shifts.
• Repair dispatch time cut in half. From fault detection to technician on-site, the average dropped from 45 minutes to under 20. The mobile app and automatic assignment removed the coordination bottleneck.
• Spare parts accuracy improved to 97%. Real-time inventory visibility eliminated most stockouts. Emergency orders dropped by over 60%.
• Full machine history on every tablet. Technicians arrive informed. They know what was replaced, when, and by whom. No more guessing, no more repeat diagnostics.

The maintenance team now plans their week around data, not gut feeling. Supervisors use the dashboard to spot patterns — which machines need attention most often, which parts wear out fastest, where the bottlenecks are.

Lindt’s Aachen factory runs tighter, with less waste and fewer surprises. The chocolate keeps flowing.