Overcome Common IoT Implementation Challenges and Threats to Scalability
IoT implementation is quickly growing more popular across industrial markets as a way of realizing digital transformation. So, what is slowing organizations down from developing smart, connected factories?
Many companies have realized that the industrial Internet of Things (IIoT) presents wide-spread benefits, from reduced machine downtime and greater asset utilization to improved field service and more. As manufacturers face more pressure to maximize throughput at the lowest cost—without sacrificing quality—digital transformation (DX) initiatives are critical to remaining competitive. It is no longer a question of if, but when and how. Of the 400 industrial decision-makers surveyed in a study by Forrester Consulting commissioned by PTC, Microsoft, and Rockwell Automation, 87% of decision-makers know it is important to scale applications across all factories. Despite their interest in achieving a digital transformation, their programs struggle to gain traction and lack enterprise-wide scalability.
Drawn out pilot programs often stall out, ultimately never prove organizational value, and lose executive support—this phenomenon is better known as pilot purgatory. Even if the proof-of-concept (PoC) is successful, companies can struggle to repeat value across sites—resulting in scale purgatory. Hidden roadblocks derail pilot programs and prevent companies from experiencing the benefits from their IoT solution while failing to accelerate impact across the enterprise. This post will help your organization take the path to value and meaningful business outcomes while tackling common IoT implementation challenges.
How can your pilot program go awry?
Despite rising IoT deployments as a means of digital transformation, industrial firms get caught in pilot and scale purgatory for several reasons.
Focusing on technology rather than business impact
Leading manufacturers have seen a double-digit improvement in performance because of their digital transformation initiatives; however, organizations can get caught up in the technology required to get there. The outdated rip and replace model can create major disruptions and stall progress. By adopting a wrap and extend model, Industry 4.0 technologies can be applied to the data and investments already in place and lead to value in weeks and months instead of years.
Lack of organizational alignment
A lack of planning for enterprise-wide expansion can quickly lead to pilot pitfalls. Although executive buy-in is critical to help digital manufacturing initiatives spread from one site to the next, there are many functional groups that need to be aligned on objectives, including data governance, security requirements, and pilot strategy that accounts for rapid, multi-site scaling. If these needs are not met, the length of time to achieve scalability can increase and ultimately lead to a loss of executive sponsorship to drive initiatives across the business.
Bespoke implementation
Most firms accumulate a patchwork of various point solutions and end up with an expensive, over-complex cluster of IT, OT, and customer-developed, DIY solutions over time as opposed to a single, integrated system. These increase industrial firms’ likelihood to stall out as it slows time-to-value, doesn’t scale, and doesn’t upgrade.
Poor access to data
Given the massive investments in hardware and software, manufacturers are often tasked with carrying out digital transformation initiatives within their existing systems. The varied data connection methods make information difficult to access and contextualize.
Complex legacy environments
The traditional cloud networks used in manufacturing collect and process data gathered at the network edge of on-prem environments and submit the data to a central cloud server. This involves a significant amount data in motion and can result in delays between the network edge and server. Incorporating an edge computing framework can reduce the complexity of interconnected systems. Rather than transmitting all the data gathered on the edge of the network, devices leveraging an edge computing framework can process data locally and only submit relevant information to a cloud server.
Key considerations to capture impact at scale
The following options can address some of the identified IoT implementation challenges and will not only help avoid pilot and purgatory but accelerate your pilot program to drive long-term results.
1. Start small
Focus on repeatable, high-value use cases to help capture transformational outcomes. The C-Suite will take notice of impactful use cases that carry out value with speed and scale including improved quality and throughput, overall equipment effectiveness, and increased revenue.
2. Develop a project plan and governance
Create a program plan that will establish infrastructure requirements and cross-organizational alignment. Establish a timeline that will start in one factory, plant, or facility targeting a repeatable use case and establish a roll-out program over the next two years. Include data governance to address the management and storage of data clarifying the rules for who can access the data and how.
3. Embrace a hybrid cloud architecture and edge computing
Not all deployment models are suitable for all workloads. By adopting both a digital manufacturing platform and a hybrid cloud and edge architecture, manufacturers can gain more flexibility, speed and security to deploy IoT use cases across the edge to cloud.
4. Leverage device connectivity
Rather than rip and replace current equipment, manufacturers need to retrofit their current IT and OT systems with industrial IoT sensors. With an integrated digital manufacturing platform, the data across the plant can be standardized faster to unlock insights from existing investments while improving scalability and time-to-value.
Achieve results from enterprise-scale solutions
Successfully deployed industrial IoT initiatives empower manufacturers to realize a double-digital impact and lays the foundation for a digital transformation. Companies can accelerate digital transformation value at scale by creating insights to performance, assets, and people. Visibility into production performance opens opportunities around throughput, quality, and efficiency.
No matter what stage your IoT project is in, organizations can realize substantial business benefits immediately. Once a successful implementation process has been established and initial success with a small use case, companies can rapidly scale initiatives to full production lines.
The Griffin’s Food Company drives impact at scale
One of New Zealand’s largest packaged foods manufacturers faced razor thin margins and steep competition. To accelerate production and improve their bottom line all while maintaining high standards of quality, Griffin’s Food Company embraced 4.0 technology to break through pilot purgatory. Their approach was to start small, maintain a strong vision and a clear business case.
Beginning with a single chocolate mixer on the plant floor, Griffin’s quickly proved the value and impact of monitoring machine performance by improving quality, efficiency, and throughput. After this initial success, the benefits of enhanced visibility were clear, and Griffin’s began to scale from one to four full production lines. With the right strategy that focused on impact, speed, and scale, Griffin’s set themselves up for a successful digital transformation journey.
Drive a digital transformation at your organization
While many manufacturers have recognized the importance of digital transformation initiatives, organizations often slow to a halt in pilot purgatory or scale purgatory—struggling to achieve an enterprise-wide transformation. Armed with a focus on impactful use cases, and the patience to stick with IoT initiatives—even if they are slow to see benefits—organizations can move past the pilot phase.
To gain more insight and recommendations on how industrial decision-makers are embarking on their digital transformation journeys, dive into to Forrester Consulting’s study, Drive Transformational Outcomes At Scale.
