The Journey to Predictability

Leveraging Data to Achieve Critical Business Outcomes in Lifecycle Management Programs

The distribution and fulfillment (D&F) sector has arrived at an operational crossroads in its approach to lifecycle management. On the familiar route, traditional methods of preventive maintenance (PM) programs and reactive responses to issues are providing adequate results and unpredictable consequences. But on the horizon awaits a new direction entirely, one where competitive pressures and profitability targets are driving the need for something well beyond the status quo. This road represents a fundamental shift in the way distribution centers (DCs) and warehouses operate: with a reliance on an abundance of operational data to inform lifecycle management programs.

The fact remains that, despite escalating e-commerce challenges, many DCs continue to run with little to no reliance on the operational data that is readily available to them. Instead, they’re content to rely solely on intuition and feel, hoping that they can meet each day’s throughput targets or business objectives. But as consumer service level agreements (SLAs) dictate ever shorter cycle times, this approach doesn’t always deliver their desired outcomes.

Unplanned equipment and system outages can create a domino effect of inefficiencies and potential profit losses — from production downtime and idle labor to delivery/shipping truck delays and disappointed customers. It’s a vicious cycle that, if perpetuated day after day, can silently erode a company’s bottom line.

For these reasons, data utilization is among the most critical issues facing modern DCs. With slim profit margins and soaring consumer expectations, operators simply cannot afford to ignore operational data and expect better outcomes.

Understanding the Value of Data

In other industries, such as the energy sector, data utilization has been built into the culture for more than a decade. From power plant output to oil platform production, these companies analyze every bit of data to squeeze even the smallest scraps of efficiency out of a process.

The value of this data is also well-known by the Department of Energy, which reported 10 years ago the potential benefits of data used in functional predictive maintenance programs, including:

  • 10X return on investment
  • 25–30 percent reduction in maintenance costs
  • 70–75 percent elimination of equipment breakdowns
  • 35–40 percent decrease in downtime
  • 20–25 percent increase in production

But DC operators have been much more skeptical about the operational impacts of data, and therefore much slower to embrace data-driven ecosystems. McKinsey reports that in the retail sector, only 30–40 percent of the potential value of data has been captured. In a related supply chain industry report, nearly two-thirds of companies confess to not utilizing any technology to monitor their supply chain performance.

However, it’s safe to assume that pure play e-tailers are among the one-third who have adopted data and digital technologies to drive productivity. For one, they’re often held to the highest standards and SLAs and can’t afford to make mistakes. But they also don’t have the luxury of excess capacity in their systems — especially during peak seasons — and are engaged in a never-ending pursuit to improve facility optimization.

For smaller operations and technology laggards alike, the notions of continually collecting data and applying automated analytics to it are new. In most cases, these stakeholders don’t understand how data-driven, actionable insights could deliver measurable results and daily operational improvements. But on closer examination, these benefits are relatively obvious:

  • Running one shift instead of two
  • Preventing downtime with the addition of machine sensors
  • Optimizing individual and networked DCs with deep operational visibility

What’s more, the idea of acting on those insights in real time, during production, is simply not an option in their playbooks. To make a culture shift toward embracing data, the D&F sector must first understand its vast potential to deliver long-term value in their operations.

Moving the Uptime Needle with Predictive Maintenance

The use of data can significantly impact DC uptime. Not only is uptime vital to meeting daily throughput targets and gauging overall operational effectiveness, but it's also an area where traditional lifecycle management strategies fall short. At issue is the way in which maintenance operations are conducted, specifically the need to transition from the strategies of “react and respond” to “analyze and predict.”

Indeed, by adopting predictive maintenance technologies, DCs can reduce the amount of unplanned operational disruptions they experience — and potentially even eliminate them. This prospect isn’t nearly as far-fetched and futuristic as one may think. By simply connecting machine-level sensors and control system data to a robust analytics software platform, operators can immediately reap 
the benefits.

Once in place, these connected infrastructures help DC operators keep their systems running at peak productivity levels — continuously accumulating data on equipment and system conditions to provide real-time statuses and insightful analytics of overall system performance. Advanced machine learning (ML) algorithms detect equipment degradation and process inefficiencies to quickly identify conditions that could inhibit productivity or cause unplanned downtime.

What’s more, DC operators don’t need to be data scientists to benefit from these insights. Information is easily accessible through live performance dashboards and alerts that operators can access from anywhere and at any time for:

  • Real-time notifications of issues affecting an asset or process 
  • Actionable insights to improve productivity during production
  • Visualizations of key system parameters and metrics along with historical data trends for optimizing maintenance and operations
  • Predicting and avoiding unplanned downtime

The Building Blocks of Operational Success

Uptime is not the only metric of success. Every business and DC has its own set of critical business outcomes upon which they’re continually measured. Whether it’s simply a matter of hitting daily throughput targets, maximizing labor productivity or increasing annual profits, each operation has defined objectives it must meet.
From a lifecycle management perspective, achieving these goals requires the ability to address the fundamental building blocks of operational success. Whether companies choose to accept these responsibilities themselves or partner with a lifecycle services provider — such as an original equipment manufacturer (OEM) — each of the following building blocks must be present to achieve critical business outcomes.

  1. DATA VISUALIZATION AND ANALYTICS — Leveraging data is essential for companies hoping to achieve continuous operational improvements. Much of this data already exists within control systems and simply needs analytics tools and visualization software to deliver real-time production insights.
  2. RESIDENT TECHNICIANS AND SUPERVISORS — Modern DCs comprise complex systems and automation technologies. An on-site staff of qualified technicians is imperative to ensure smooth, reliable operations.
  3. SPARE PARTS MANAGEMENT — A robust spare parts management program is essential for delivering efficiently planned and corrective maintenance activities.
  4. 24/7 TECHNICAL SUPPORT — Technicians need access to expert OEM support to help troubleshoot issues and accelerate equipment repairs and issue resolution. 
  5. TECHNICAL ADVISORS — Individuals with extensive facility experience and equipment expertise are needed to troubleshoot and advise the best course of action on a given piece of equipment or technical issue.
  6. FIELD ENGINEERS — A core of field engineers is helpful for bolstering an on-site staff in a variety of situations, such as accelerating preventive (planned) maintenance processes or identifying flaws in a system’s design.
  7. ASSET MANAGEMENT ASSESSMENTS — DCs must have periodic assessment programs in place to evaluate both equipment and operational performance. Assessments are essential for developing multi-year, asset management plans to allow for modifications, upgrades and obsolescence.  
  8. ENGINEERING CENTER OF EXCELLENCE — As the needs for modifications and upgrades are identified, this team of engineers can help ensure proper design and implementation of these improvements.
  9. TRAINING — With continuous industry growth, high attrition rates and a shortfall of qualified technicians, the need for training is more critical than ever. Training helps enhance your existing technician skillsets while aiding in the recruitment process.

Only with all these lifecycle management building blocks in place is it possible for companies to consistently achieve their critical business outcomes — even if the goal is 99.9 percent uptime. 

Adopting a Data-Friendly Culture

For those companies or operations that have been slow to adopt data, the good news is that they can quickly catch up to (or surpass) their competitors. But doing so will require a fundamental cultural change. The power that this data represents can be perceived as a threat to employees and stakeholders alike, many of whom are either content with the status quo or fear a disruption in their standard modes of operation.

To help make the transition to a data-friendly culture, companies should consider the following best practices:

  1. ESTABLISH TRANSPARENCY — Give everyone in the company access to the same data so they can A) gain visibility to the insights, and B) contribute to the problem-solving process.
  2. PROMOTE CROSS-FUNCTIONAL TRUST — Transparency will inevitably bring issues to the surface. Rather than focusing on recrimination and creating a culture of fear, it’s essential for all departments to build trust and work together on resolutions.
  3. ALIGN THE ORGANIZATION TO BUSINESS OUTCOMES — Once business outcomes have been clearly defined, leadership must make sure that everyone in the organization is working to achieve them. This also means everyone must understand what has value and what does not.

Like any new technology or process, introducing the use of data in a transparent and automated way will require some degree of change management in order to ingrain it into standard procedures and ultimately, leverage it effectively.

At Honeywell Intelligrated, we’re creating the tools to help companies embrace data utilization in their lifecycle management programs. Not only do we provide our customers with the building blocks to success, we’re helping them integrate our foundational connected solutions to deliver the insights needed to achieve their business outcomes.