The Connected DC

Connect to the Power of Predictive

Publication
February, 2020

Leveraging Operational Data to Drive DC Performance and Maintenance Improvements 

Whether you call it industry 4.0, the industrial internet of things (IIoT) or digital transformation, many material handling companies are recognizing the need to make a fundamental shift in the ways they run their distribution and fulfillment (D&F) operations. In fact, 70 percent 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 experienced relatively slow adoption rates. A recent study revealed that only 2 percent of executives had identified supply chain performance as a focus of their digital strategies. This trend suggests that the importance of IIoT may be both misunderstood and/or its potential benefits difficult to measure.

This isn’t the case in the energy sector, where IIoT’s transformative operational impacts were proven a decade ago. A 2010 Department of Energy (DOE) study of operations, maintenance and energy professionals revealed that the average savings from an IIoT-driven predictive maintenance operation delivered a 1,000 percent return on investment (ROI), including the following key performance indicators (KPIs) — many of which would appeal to distribution center (DC) operators:

  • 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

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

Challenges and Opportunity Costs

Compared to these industries, D&F has its own unique complexities, barriers to adoption and opportunity costs. 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 technologies can present compatibility challenges when integrating existing assets into a connected infrastructure. And 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.

But the risks of doing nothing and not making a digital transformation may be even greater. 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 true differentiator, skilled service technicians 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. As a result, DC operators are in a race to improve training processes, recruit new candidates, and quickly get them up to speed.

All these challenges present opportunities for connected, IIoT infrastructures to succeed. 

How to Make an Effective Digital Transformation

Many early digital transformation efforts are falling short or missing the mark.

  • Over the last 12 months, 81 percent of IT decision makers have seen a digital transformation project fail, suffer a major delay, or get scaled back. 

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

  • Lack of understanding of the technology landscape and its effects on your business
  • Lack of adequate talent to effectively implement and utilize the technology
  • Lack of a clear business case to justify the investment

Steps to Succeed

1.    CREATE A STRONG BUSINESS CASE. 

  • IIoT adoption leaders were 75 percent 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.

  • 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, CAPABLE IIOT INNOVATION TEAM.

  • 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 Downtime

Up to 80 percent of businesses are unable to accurately estimate their downtime rates. Many underestimate downtime costs by 200–300 percent. 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

Utilize Existing Control System Data

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

It’s estimated that there are hundreds of thousands of data points that can be accessed from a control system, but this data is largely underutilized. Some operators pull data from programmable logic controllers (PLCs) periodically throughout a day or shift. But because PLCs are only capable of storing limited amounts of data, this information alone is transient and offers no trending information or insights. What’s more, only roughly 25 percent of this data has any real 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:

  • 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 potentially predicted — and even prevented.

Expand Your Insights

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 (AI) — can detect and track deviations from performance baselines.

Today’s machine-learning algorithms are refined to such a degree that 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 preventative maintenance (PM) interval. Upon inspection, technicians perform a series of maintenance tasks:

  • 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 these insights are available on any equipment utilizing motors, gearboxes and controller data, e.g., print and apply, palletizers and robotics.

Integrate Predictive 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 smart glasses for live troubleshooting

For example, DC operators could potentially automate the entire 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 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 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 3–5 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. This approach is applicable in operations old and new, large to small, and everything in between.

Some forward-thinking companies are building connectivity into the specification of new DCs. From the outset, this will enable predictive capabilities while protecting their new equipment investments. Existing facilities have just as much to gain, and can retrofit connectivity within control systems, equipment sensors, and smart analytics and visualization software.

Regardless of your business or operational goals, Honeywell Intelligrated has the technologies to help you build a more connected present and a more predictable future. Our Connected Assets offering is helping our customers enhance their operations today while laying the groundwork for ongoing, data-driven performance improvements. 

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