Quantifying the Bathtub Curve
Measuring Your Capex Reduction and Delayed IT Expenditures from Sourcing Hardware from OSI is Easy – But Don’t Overlook the Time and Cost Savings Gained by Avoiding the Higher Failure Rates of New Equipment Burn-In
For organizations accustomed to purchasing pre-owned IT hardware, the advantages of stepping away from an OEM-only approach are obvious. Guaranteed compatibility, easy parts replacement, next-day delivery, faster implementation, and reduced maintenance costs are all part of the value proposition of buying thoroughly inspected, tested, and Lifetime Warrantied gear from OSI Hardware. Couple these operational benefits with savings of 40% to 80% off the most aggressive discount pricing offered by manufactures, and its obvious why the third-party hardware market has matured and expanded so rapidly.
Most infrastructure and operations managers are acquainted with the bottom line benefits achieved through the avoidance of End Of Life and End Of Support policies propagated by Cisco and other manufacturers. However, when championing their reliance on secondary market equipment to their senior managers, they understandably may overlook the other less obvious but equally impressive cost- and time-savings from stepping off the 3-to-5-year upgrade cycle propagated by Cisco and other OEMs.
Specifically, failure rates are lower for pre-owned hardware than new equipment, resulting in fewer unexpected outages and a reduced level of costly downtime. The fact is, the overall reliability of fully vetted, used hardware is significantly better than new products shipped to distributors and VARs directly from the factory. While the idea of secondary market hardware offering measurably lower failure rates than OEM may seem counter-intuitive, the reason stems from the well-known ‘Bathtub Curve’, a central principle of reliability engineering.
OEM Customers Used to Taking a Bath?
New components shipped by OEMs are generally expected to experience a 2% to 5% failure rate during their useful life. Due to several factors, the risks for failure are highest upon installation of new components and remain elevated during the “burn in” period. The Bathtub Curve reproduced below is to used in reliability engineering to define the levels of risk for hazard function (failure) during the lifecycle of a given component.
Universally accepted by systems engineers conducting lifecycle management in products in all industries, this visual representation describes failure probability across three phases:
Infant Mortality: Higher probability for early failures upon first use and burn-in period
Random: Flat or constant failure rate due to arbitrary causes of failure
Wear Out: Probability for failure increases due to expiration of design lifetime