Redesigning the end-to-end service to increase capacity, reduce lead time variability, and strengthen operational resilience
Timeline: 2024 — full year
Summary
In 2024, BestGift entered a strong growth phase, with demand increasing faster than the operation could confidently scale. Sales performance was visible, but production capacity, constraints, and delivery predictability were not. The risk was clear: growth could outpace execution and damage the customer experience.
I led a service design initiative across the full year to map the end-to-end experience, connect frontstage expectations to backstage execution, and translate insights into practical interventions. The work combined qualitative research, operational data collection, and service blueprinting to identify the constraints creating most delays and rework—then redesigned the service model to support sustainable growth.
Context
BestGift’s customer experience depended on a chain of internal and external steps: purchasing, production, engraving, packaging, and delivery coordination. Several steps relied on external suppliers and informal handoffs, while internal systems were underutilized for planning and operational visibility. As volume increased, these weaknesses surfaced as delays, inconsistent delivery estimates, and preventable rework.
Scaling required a service-level view: not only improving isolated steps, but aligning the entire experience—from promise to delivery—around reliable information, clear ownership, and measurable performance.
My Role
Service Design lead, partnering directly with purchasing, production, sales, and leadership.
The problem
Growth exposed service-level gaps across the full journey:
- Frontstage expectations (stock and delivery timelines) were set without reliable operational signals
- Backstage work was fragmented across teams and suppliers, with unclear handoffs and ownership
- A critical step (engraving) consistently introduced delay and unpredictability due to external dependency
- High error and rework rates affected timelines and increased operational cost
- The control software existed but was not being used as an operational management tool
Goals
- Increase production capacity by at least 30% without sacrificing quality or delivery reliability
- Reduce overall lead time and variability across the service
- Improve coordination and accountability across departments
- Establish operational visibility and routines to support continuous improvement
- Protect and improve customer experience as volume increased
Constraints and approach
The work needed to improve performance while operations continued running at full pace. Instead of optimizing isolated tasks, the approach was to define the service end-to-end, identify where customer expectations disconnected from operational reality, and then focus on interventions that reduced the highest-impact constraints.
Key service design decisions:
- Use service blueprinting to connect customer experience to operational capabilities
- Prioritize reliability and predictability (not only speed) to protect trust at scale
- Reduce dependency on external steps that introduced high variability
- Improve system adoption and operational routines so improvements could be sustained
Key improvements (service design highlights)
1) Research grounded in real work and real constraints
I conducted discovery across the full service ecosystem:
- Interviews with 8 staff members across purchasing, production, sales, and management
- 12+ hours of qualitative insights
- Real-time audit of the production flow to build baseline data
Key insights (as collected):
- 65% of staff cited communication gaps as a major operational friction
- 70% pointed to engraving delays, adding an average of ~3 days to delivery time
- 80% reported underutilizing the control software, limiting visibility and coordination
Baseline performance signals:
- Average production time: ~12 days per order
- ~40% of delays linked to reliance on external suppliers
- Error rate: ~15%, creating rework and occasional customer dissatisfaction
2) Service blueprint: current state → future state
I created a service blueprint to make the system visible and actionable.
Current state (key patterns):
- Frontstage: customers and sales relied on incomplete signals; delivery estimates were often based on assumptions rather than capacity and stock reality
- Backstage: silos and informal handoffs caused missed deadlines and unclear accountability
- Support systems: the control software operated mainly as a historical record instead of enabling planning, inventory accuracy, or real-time reporting
Future state (designed service model):
- Frontstage: improved alignment between customer promises and operational reality through more reliable stock and delivery signals
- Backstage: clearer ownership of steps and reduced variability in critical paths (especially engraving)
- Support systems: stronger system adoption with training and operational routines, enabling tracking, inventory management, and reporting
This blueprint was used as a decision tool to prioritize interventions and align teams around a shared service model.
3) Redesigning backstage operations to reduce variability
Interventions were designed to improve flow reliability across the service:
- Clarified ownership and handoffs across purchasing and production
- Adjusted responsibilities and expanded team capacity where constraints were systemic
- Introduced operational routines supported by the control system (tracking, inventory, reporting)
4) Internalizing a critical service constraint (engraving)
The service blueprint made clear that engraving was not simply “a step,” but a systemic reliability risk. Bringing engraving in-house reduced dependency, improved scheduling control, and removed a recurring source of delay.
Outcomes
- Production capacity increased by approximately 35–40%
- Average production time reduced from ~12 days to ~8 days
- Error rate reduced from ~15% to ~5%, lowering rework and operational cost
- Engraving internalization saved approximately R$25,000/month and reduced delivery time by ~2 business days
- Customer satisfaction improved (illustrative: ~15% uplift in NPS)
- Team engagement increased (illustrative: ~85% reporting higher satisfaction and ~20% turnover reduction)
- ROI for engraving investment reached break-even within ~6 months
What this demonstrates
- Service design leadership: mapping a complex operation end-to-end and aligning teams around a shared service model
- Ability to connect frontstage experience to backstage execution and support systems
- Translating qualitative insight and operational metrics into high-impact interventions
- Designing for scale with sustainability: capability building, routines, and system adoption
- Driving measurable outcomes across efficiency, reliability, cost, and customer satisfaction
Next steps
Introduce forecasting and capacity planning to support continued growth without service degradation
Maintain monthly KPI tracking to monitor lead time, errors, and customer feedback
Expand e-commerce and operational integration to improve stock accuracy and delivery estimates