Case Studies

AI-Enabled Order-to-Cash Makeover: How a European Glass Leader Cut Customer-Service Cycle Time by 35%

Duration:

18 Weeks

Challenge

A heritage glass-manufacturing group struggled with soaring SG&A costs and sluggish customer-service performance.

Order-entry cycle time averaged 48 hours, causing back-order spirals and revenue leakage.

Escalations clogged the Customer Service Department (CSD), diverting sales reps from growth initiatives.

Fragmented processes across three ERP instances generated duplicate data, driving error-correction cost up by €2 m+ per year.

The executive committee asked Massoni Advisory to redesign the CSD operating model, inject digital intelligence, and deliver a measurable P&L impact—fast.

Our Approach

Gathered 24 months of order-level data and voice-of-customer feedback.
Deployed agentic AI bots to auto-map 48 end-to-end process variants, pinpointing bottlenecks and rework loops.
Benchmarked KPIs vs. peer manufacturers; flagged a 35% efficiency gap.

Co-designed a “future-state” CSD blueprint: unified order-capture workflow, single data model, and tiered service levels (standard vs. fast-track).
Configured Al agents to triage incoming orders, auto-validate master data, and route exceptions to specialists.
Authored playbooks, KPIs, and RACI charts; trained 42 CS reps and sales coordinators.

Piloted the new model in the flagship Italian plant; Al agents handled 68 % of orders
autonomously within three weeks.
Rolled out to two satellite plants; migrated legacy orders into the new workflow.
Embedded a real-time dashboard tracking cycle-time, error rate, and cost-to-serve;
instituted weekly performance huddles.

Impact

31h

Order-entry cycle time 35% faster than before (48h)

2.8

Manual touches per order
46% reduction than before (5.2)

96%

First-time-right rate +20pp from before (76%)

€1.7 m

Annual SG&A saving (verified by Finance)

The AI agents now auto-process ~70 % of orders, freeing 16 FTEs to focus on upselling and proactive account care.

Conclusions & Lessons Learned

Anchor on data first

The AI mapping exercise exposed hidden rework loops that would have been missed by interviews alone.

Govern for sustainability

Weekly huddles and live dashboards keep cycle-time gains from eroding as volumes grow.

Start small, scale fast

A controlled pilot de-risked the rollout and built confidence among staff and unions.

Blend tech with operating-model redesign

Automation delivered speed, but the tiered service policy ensured capacity stayed in sync with customer expectations.

Next Step

The client has green-lit Phase 2—a sourcing wave on packaging and MRO (Pillar 1) using the same data-driven, AI-enabled playbook.

AI-Enabled Order-to-Cash Makeover: How a European Glass Leader Cut Customer-Service Cycle Time by 35%

Challenge

A heritage glass-manufacturing group struggled with soaring SG&A costs and sluggish customer-service performance.

Our Approach

Impact

31h

2.8

96%

€1.7 m

Conclusions & Lessons Learned

Next Step

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