Redefining Productivity in the Aftermarket

For military, aerospace/airline, capital equipment, process manufacturers and utilities organizations, guaranteeing lowest-cost compliance to a Service-Level Agreement (SLA) is mission-critical.

Organizations seeking to hone productivity and capture additional revenue are turning to an ongoing and in-depth examination of the intricate relationships between materials, staff, and information. However, as the volume of performance and activity-related data available for analysis from enterprise systems continues to expand exponentially, decision-making reaches new levels of complexity.

Traditional Solutions Are Limited

Traditional enterprise system modeling and analysis tools allow collection and review of performance indicators alone, attempting to identify trends in existing data only. These tools are inherently limited because they draw on information in the "closed loop" of each segment of the system, without taking into account the interrelationship between components and the significant influence of external business and operational considerations. Further, they rarely incorporate the support logistics and service-level requirements that can be unique from one piece of equipment to another, or from one location or event to another.

Gartner on Clockwork Solutions for Aftermarket Services

"...excellence in predictive maintenance."

"...unique in providing analysis and scenario simulation capabilities... as well as the methodology to calibrate the level of equipment service and customer support to match the needs of a project..."

Efficiency, Savings, Revenues

By drilling down and analyzing data from ERP, EAM, CRM, and other strategic enterprise systems, combining this knowledge with data from external sources such as manufacturer specifications, and taking into account custom-defined business goals - SPAR-based solutions help organizations develop a deeper awareness of customer needs in the aftermarket.

This awareness translates directly into revenues, as companies optimize Service Part Planning (SPP) staffing resource planning, and predictive maintenance planning strategies to suit ever-evolving, multifaceted aftermarket considerations.

SPAR for Aftermarket Intelligence

Effectively analyzing historical data from multiple sources, together with unique and specific business and operational performance goals, SPAR's Monte Carlo simulation models deliver true aftermarket intelligence.

Tightly-integrated with enterprise systems, SPAR-based models assist in evaluating and analyzing the interrelationship between operational goals, logistics, maintenance, and manpower - deploying a robust set of custom-defined predictive indicators to identify and address existing and future key business performance issues.

The results – a systematic approach, more accurate and future-proof decision-making, better understanding of aftermarket behavior, near real-time alerts of potential performance problems, and substantial savings in parts, service and maintenance overhead - ultimately resulting in higher profitability, improved availability, and greater customer satisfaction.

SPAR for SPP

SPAR offers system performance-based spare parts planning and optimization, creating a complete organizational logistical model which allows calculation and validation of inherent system performance under numerous "what-if" scenarios. The flexible SPAR platform allows prediction of system performance increase or decrease, taking into account:

Demand based on Service-Level Agreements, geographic product use, and predictions of component failure and reuse
Necessity of relocating inventory as demand (or customers) shifts
The need for multi-echelon planning, including mobile field services units
Intermittent demand
Returned items for remanufacture or resale
Disposal constraints
Budgetary constraints

SPAR calculates required spare parts quantities, and where to place them in the logistic architecture, based on system demands– not just the warehouse availability.

SPAR for Predictive Maintenance Planning

Preventive maintenance planning is central to profitable performance. When management considers the effect of outages and downtime, a value-based predictive approach answers three questions relative to their economic impact:

When to perform preventive maintenance, outages, planned downtime?
What equipment to maintain during each maintenance session?
What level of maintenance should be applied to the equipment?

SPAR is designed to support system, plant engineering, and operations - helping decide when to perform the next preventive maintenance and what to maintain during a planned shutdown. It does so by quantifying the system’s survival probability over time and estimating the accumulated damage (Age Coefficient) and probability of the system and parts to survive additional periods after the planned outage.

For more information:

SPAR Modeling Platform and SPAROpt - Spare Parts Optimization

Also download the following selected documents from our Information Center:

Total Life Cycle Management Assessment Tool-(TLCM-AT)
SPAR and Spare Parts Optimization
Optimizing the Spare Parts of a Natural Gas and Waste Incinerating System
ATLAST Deployment & Push Pack Spares Optimization Module
The Monte Carlo Method and Optimization of Spare Parts in Complex Systems

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