Data is the Key to Lifecycle Management Predictability

Succeeding in a hypercompetitive e-commerce market requires a new data-driven approach to lifecycle management. From labor challenges and technician shortages to the digital transformation from manual to automated operations, distribution center (DC) operators and supply chain leaders find themselves navigating a new era of distribution and fulfillment (D&F). These rapidly changing market dynamics are driving companies to make a fundamental shift in the way they approach lifecycle management — from traditional strategies of “react and respond” to a new paradigm of “analyze and predict.” 

Proper utilization of operational data is the fuel on this journey to predictability, not only to improve reliability but also to achieve newfound productivity and maximize uptime.

Slow adoption of the industrial internet of things (IIoT)

Compared to other industries, we know that the D&F sector has been slow to adopt IIoT technologies that monitor, record and analyze operational data. Traditional methods of preventive maintenance (PM) and reactive responses to issues arising in DCs are providing only adequate results and can lead to unpredictable consequences. Consider the following industry statistics:

• Only 30 to 40% of the potential value of DC operational data has been captured.ⁱ  
• Nearly two-thirds of supply chain companies confess to not utilizing any technology to monitor the performance of their operations.ⁱⁱ 
• Department of Energy (DOE) study cites 70–75% elimination of equipment breakdowns using IIoT-based predictive maintenance.ⁱⁱⁱ  

For more than a decade, the energy sector has built data utilization into their operations. From power plant output to oil platform production, these companies analyze every bit of data to gain just the smallest amounts of efficiency out of a process. But even with escalating e-commerce challenges, many D&F companies continue to run DCs with little to no reliance on the operational data that they already own. Instead, they rely on intuition and feel alone, hoping to meet throughput targets or business objectives.

Increase operational reliability throughout the lifecycle

Unplanned downtime can have extremely detrimental effects in DC operations — where uptime is vital to meeting daily throughput targets and gauging overall operational effectiveness. But achieving the desired levels of operational reliability is one area where traditional lifecycle management strategies fall short. As a result, many companies are prioritizing technology investments that can deliver needed reliability improvements:

• 90% of companies say durability, reliability and uptime are the top priorities when evaluating any DC automation technology investment.^iv 
• 81% closely consider the total cost of ownership, speed to ROI and maintenance throughout the lifecycle of their investments.^v

Predictive maintenance technologies can help DC operators reduce — and potentially eliminate — unplanned operational disruptions. IIoT infrastructures are connecting critical assets and systems to enable proactive, predictive maintenance capabilities, utilizing machine control data, sensors, software, cloud storage and data analytics. 

Building blocks of lifecycle success

Every business defines its own measures of success — whether that’s hitting daily throughput targets, maximizing labor productivity or increasing annual profits. Realizing these goals means addressing the fundamental building blocks of success — on your own or in collaboration with a lifecycle services provider. 

As DCs become more automated, traditional lifecycle management programs are proving inadequate for reducing unplanned disruptions and increasing reliability. To protect system investments and achieve up to 99.9% uptime, DC operators need to capitalize on the wealth of operational data that is readily available to them.

Download our Lifecycle Services infographic, Make the Journey to Lifecycle Management Predictability, to learn how the proper utilization of operational data can improve reliability, achieve newfound productivity, and maximize uptime.

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Sources

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ⁱ Nicolaus Henke, Jacques Bughin, Michael Chui, James Manyika, Tamim Saleh, Bill Wiseman and Guru Sethupathy, “The Age of Analytics: Competing In A Data-Driven World,” McKinsey & Company, December 2016, https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world (accessed February 19, 2020).

ⁱⁱ Chris Farkas, “15 top supply chain statistics — and the 5 related trends that will impact your business,” October 11, 2018, eAlchemy Labs, https://ealchemylabs.com/blog/15-top-supply-chain-statistics-and-the-5-related-trends-that-will-impact-your-business/ (accessed February 19, 2020).

ⁱⁱⁱ http://www.eeperformance.org/uploads/8/6/5/0/8650231/o_m_guide_2004.pdf

^iv Bridget McCrea, “Annual Warehouse and Distribution Center Automation Survey: More automation, please,” Modern Materials Handling, May 15, 2019, https://bt.e-ditionsbyfry.com/publication/?m=24831&i=588109&p=56 (accessed February 20, 2019).

^v Bridget McCrea, “Annual Warehouse and Distribution Center Automation Survey: More automation, please,” Modern Materials Handling, May 15, 2019, https://bt.e-ditionsbyfry.com/publication/?m=24831&i=588109&p=56 (accessed February 20, 2019).