Connect automation to the power of predictive maintenance

Leveraging operational data already in control systems can drive distribution center (DC) performance and maintenance improvements.

By Eric Rice August 6, 2020


Learning Objectives

  • U.S. Department of Energy said IIoT-driven predictive maintenance operation delivered a 1,000% return on investment (ROI).
  • Automated devices and systems help distribution and fulfillment.
  • Smart glasses allow a technician to share live audio/video in real time with original equipment manufacturer (OEM) experts.

Industry 4.0, Industrial Internet of Things (IIoT) and digital transformation efforts are helping material handling companies recognize the need to make a fundamental shift in how distribution and fulfillment (D&F) operations are run. In fact, 70% of material handling executives consider industry 4.0 a top priority.

While the potential benefits of IIoT technologies are well-known in other industries, the D&F sector has relatively slow adoption rates. A recent study revealed only 2% of executives identified supply chain performance as a focus of their digital strategies. This trend suggests IIoT’s importance may be both misunderstood and/or its potential benefits difficult to measure.

In the energy sector, IIoT’s transformative operational impacts were proven a decade ago. A 2010 Department of Energy study of operations, maintenance and energy professionals revealed the average savings from an IIoT-driven predictive maintenance operation delivered a 1,000% return on investment (ROI), including the following key performance indicators (KPIs):

  • 25to 30% reduction in maintenance costs
  • 70to 75% elimination of equipment breakdowns
  • 35to 40% decrease in downtime
  • 20to 25% increase in production.

The building automation sector has been deploying IIoT best practices for more than a decade. Other industrial sectors, like the oil and gas industry, also have willingly embraced the power of operational data to help predictive maintenance. Despite the differences between these sectors and D&F, they share similar business objectives and KPIs – and the same potentially transformative benefits.

Automated devices and systems for distribution and fulfillment

The D&F industry has its own unique complexities, barriers to adoption and opportunity costs for automation devices and systems. Because no two operations are alike, it can be difficult to approach implementation from a “standard” perspective. The ever-present risk of disrupting operations in a hyper-competitive, e-commerce fulfillment sector also may be a deterrent.

The proliferation of warehouse software, management systems and automation technologies can present compatibility challenges when integrating existing assets into a connected infrastructure. For most companies, the absence of a viable change management strategy prevents them from achieving fundamental progress and affecting the required organizational shift needed to embrace a data-driven enterprise.

The risks of doing nothing and not making a digital transformation, however, may be even greater, given the acceleration automation can provide. The increasing digitization of operations, business transactions and customer interactions dictate that retailers implement IIoT infrastructures to:

  • Address e-commerce pressures
  • Ensure customer service level agreements (SLAs) are met
  • Shorten order cycle times and delivery windows.

In a world where reliable, consistent uptime is a differentiator, skilled service technicians for automation, devices and systems are vital to an operation’s success. But as a generation of service veterans nears retirement, there are few qualified technicians poised to replace them. This trend is creating a significant knowledge and service gap that presents a long-term threat to many operations. DC operators are now in a race to improve training processes, recruit new candidates and get them up to speed on increasingly integrated automated systems.

How to make an effective digital transformation

The most common barriers to successful IIoT adoption can be traced to three primary causes:

  1. Lack of understanding of the technology landscape and its effects on your business
  2. Lack of adequate talent to effectively implement and use the technology
  3. Lack of a clear business case to justify the investment.

Three steps to succeed in digital transformation

1. Create a strong business case for digital transformation. 

IIoT adoption leaders were 75% more likely than IIoT laggards to cite the preparation of a strong business case or clearly articulated vision for value creation as key factors in their IIoT programs’ success.

2. Start small and clearly define the scope for digital transformation. 

  • Digital transformation is an iterative process.
  • Choose a key area of operations (about which many stakeholders care) and establish time-bound parameters.
  • Create an actionable business plan with goals for achieving a specific financial outcome.

3. Build a competent and capable IIoT innovation team for digital transformation. 

  • Visionary: Establishes the vision and provides clear direction
  • Motivator: Engages the team with a common goal and coaches others along the way
  • Executor: Brings the necessary resources and capabilities to drive change through your organization.

The true cost of facility downtime

Up to 80% of businesses are unable to accurately estimate their downtime rates. Many underestimate downtime costs by 200 to 300%. The following factors are often ignored when calculating downtime:

  • Lost production
  • Recovery costs
  • Wasted labor/productivity
  • Missed customer SLAs
  • Depleted inventories
  • Mechanical equipment/system stress
  • Disruption to innovation
  • Loss of brand loyalty/customer trust.

Use existing control system data

Leading retailers are beginning to test the IIoT waters via pilot programs. For those who are new to using their data, a good place to start is tapping into the vast amounts of available data from their machine control systems.

It’s estimated there are hundreds of thousands of data points that can be accessed from a control system, but this data is often underutilized. Some operators pull data from programmable logic controllers (PLCs) periodically throughout a day or shift. However, PLCs are only capable of storing limited amounts of data, which means this information alone is transient and offers no trending information or insights for predictive maintenance. About a quarter of the extracted data has value.

To extract value from control system data, operators need software and analytics tools to make sense of it. By continually aggregating and interpreting this data, these tools filter out the noise to deliver historical trends and actionable insights that provide tremendous operational value. Analysis of conveyor run statuses can help DC operators evaluate KPIs such as read rates, throughput, flow balance through merges and conveyor jams.

Armed with this data, operators can address a variety of issues that impact performance such as:

  • Resolving conveyor faults that create repetitive jams
  • Uncovering scanner timing and read rate issues to prevent unnecessary recirculation and manual handling
  • Automatically logging the duration of downtime in pick stations, merges, transfers and recirculation loops.

By connecting control system data to software with alarm management capabilities, operators can access real-time dashboards and receive email, text and mobile app notifications when key issues impact operations. These tools can help companies formalize issue escalation processes and uncover repetitive issues that can be predicted – and even prevented.

Expand insights for condition systems, equipment motors

The addition of condition sensors on equipment motors and gearboxes provides even deeper insights into system performance, including the ability to predict equipment and system failures before they occur. Data extracted from vibration and temperature sensors – combined with smart analytics software, machine-learning algorithms and artificial intelligence – can detect and track deviations from performance baselines.

Today’s machine-learning algorithms are refined to such a degree they only generate alarms when parameters exceed defined temperature and vibration thresholds. For example, consider these insights gleaned from a sensor on a sortation system gearbox. Trending data from analytics software indicates an incremental, steady increase in gearbox vibration. The alarm management system sends a notification that corrective action is needed before the next scheduled preventive maintenance (PM) interval. Upon inspection, technicians perform a series of maintenance tasks including:

  • Grease floating sprocket and idler shaft bearings
  • Inspect and adjust all timing belt pulleys
  • Ensure alignment and evenly torqued components.

After servicing the gearbox, the analytics software indicated that vibration had returned to normal levels. If maintenance teams waited until the next PM interval, there’s a high probability that the vibration could have escalated to equipment failure – shutting down the conveyor and causing a domino effect of performance issues and missed SLAs.

It’s important to realize that that these insights are available on any equipment using motors, gearboxes and controller data, such as print and apply, palletizers and robotics.

Integrate predictive capabilities into maintenance processes

By connecting analytics insights to other fulfillment technologies, operators can automate the creation of service tasks and make the transition to a true predictive maintenance model. Doing so requires integrating one or more of these key enabling technologies with a connected DC infrastructure:

  • Computerized maintenance management system (CMMS)
  • Voice-directed maintenance and inspection technology
  • Augmented reality (AR) smart glasses for live troubleshooting.

For example, DC operators could potentially automate the find-and-fix process from issue detection to resolution:

  1. Analytics software detects when a KPI is out of range.
  2. Work order request is triggered to an on-site maintenance technician.
  3. Technician receives an alert, then initiates a voice-guided inspection workflow.
  4. Smart glasses allow the technician to share live audio/video in real time with original equipment manufacturer (OEM) experts.
  5. Technician completes voice-guided work, records the fix for future reference, and automatically generates a CMMS report/issue resolution status.

Preparing for a more connected automation future

Based on current market trends, the abilities to predict equipment failure and achieve visibility into operations will become even more important in the next 3 to 5 years. Market growth, paired with a declining technician workforce, will dictate the need for more predictive automation. As a result, enterprise and DC operators will need the insights to implement smarter processes and achieve more reliable equipment operation.

Connected infrastructures help relieve these operational burdens while delivering the business intelligence to drive continuous bottom line improvements, including with predictive maintenance. This approach is applicable in operations old and new, large to small, and everything in between.

Eric Rice is principal product marketing manager at Honeywell Intelligrated. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media,

KEYWORDS: Digital transformation, predictive maintenance, automation


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Original content can be found at Control Engineering.

Author Bio: Eric Rice is principal product marketing manager at Honeywell Intelligrated.