Process manufacturers are facing a slow-building tension driven by two central factors: pressure to maintain margins amid rising costs and deteriorating operating KPIs (first pass yield, throughput, productivity, etc.) in multiple pockets of the industry.
Rising production costs have been driven by factors like elevated chlorine prices and a reliance on scarce ores like ilmenite and rutile as basic whitening and coloring agents.
Deteriorating KPIs are a particular concern for producers of basic chemicals (caustic soda, ethane, propane, benzene, ethylene oxide, nitrates, sulphates, etc.). While the basic chemicals industry has undergone significant transformation since its origins in the 18th century, many current plants are now 30-40 years old. Products like specialty polymers and resins – and the facilities that produce them – may be relatively new, but because they depend on basic chemicals as their raw materials, these inefficiencies in basic chemicals production drive up costs across the chemical manufacturing value chain.
According to the US Bureau of Labor Statistics, the US chemical manufacturing industry has experienced a significant and sustained drop in productivity since 2016. Factory-related issues such as declining output quality, rising rework rates, and increased machine downtime have all played a role.
The most straightforward and comprehensive route to boosting operating KPIs and reducing long-term costs – replacing aging factories and modernizing core manufacturing equipment – is prohibitively expensive for most manufacturers. However, aging equipment is no longer a barrier to improving operating KPIs. Process manufacturers can reduce costs and improve their most critical business KPIs by strategically implementing new Industry 4.0 (I4.0) technologies at older plants.
From Theory to Factory
I4.0 technologies such as AI/ML, IoT, AR/VR, and image analytics are already significantly impacting automotive and electronics manufacturers. To make a meaningful impact in process manufacturing, these same technologies must drive improvements in fundamental metrics like first-pass yield, throughput, productivity, rework rate, and capacity utilization.
So, what would a successful I4.0 intervention look like in practice?
Imagine a production line for a specialty thin film used to manufacture green fuel cells. The quality requirements are extremely high. Close visual and tactile inspection reveals occasional surface defects, but in the absence of a human observer, these anomalies cannot be tracked, much less mitigated. Presumably, these anomalies are caused by factors like roller speed, tension control imbalance, and the material's consistency. One solution would be to upgrade the production line with new rollers, tension controllers, and other hardware. However, with I4.0, it is possible to achieve similar results with a less expensive approach that avoids a lengthy equipment shutdown.
Leveraging light-touch digital tools to drive responsive automation, an I4.0 approach would:
- Use advanced analytics on historical process parameter data (roller speed, tension, temperature, etc.) to determine a golden set of process parameters that minimizes the formation of surface defects.
- Capture any residual defects through computer vision cameras and an analytics model that relies on AI-driven image recognition.
- Create a feedback loop for DCS/PLC controllers that responds to parameter deviations and returns the parameters to the golden set.
An I4.0 Adoption Framework for Process Manufacturers
Achieving market-leading results from I4.0 starts at the level of the individual plant. By focusing on a single production line, a manufacturer can pilot I4.0 interventions, measure them, and scale those interventions to other plants. This process can proceed in four stages:
- Discover use cases: Process manufacturers should begin with a consulting-led approach that identifies the plants and processes most ripe for digital transformation. Visits to plants by domain experts can leverage process assessments (often informed by frameworks like APQC and ASCM) to identify hotspots contributing to a disproportionate loss in operating KPIs. The discovery phase maps potential digital interventions, estimates the anticipated improvements to key business KPIs, selects the most attractive digital interventions, and aligns with plant leadership to create the transformation roadmap and begin implementation.
- Execute and improve: Regardless of the digital sophistication of the manufacturing plant, implementing a new I4.0 intervention will likely involve new sensors/instrumentation and data infrastructure. At this stage, manufacturers should be efficient in their investments and prepared to fail quickly and move on. Using an agile, iterative approach, they can quickly identify the interventions that are most likely to drive value at other plants.
- Frame a global approach: As data reveals the most impactful I4.0 interventions at individual plants, manufacturers can begin to create a global digital strategy. This strategy will explore the costs and benefits of scaling high-performing solutions to all plants and consider how more intensive investments in new instrumentation and data infrastructure could further boost KPIs like yield.
- Replicate at scale: As they advance with a global digital strategy, manufacturers must continue engaging in plant-specific assessment and road-mapping to ensure that I4.0 benefits can be replicated and surface any needs for plant-specific customization. As with all technology transformation, scaling I4.0 solutions will require building a human-centered change management strategy that can drive adoption and impact on the ground.
An iterative approach to I4.0 technologies will help manufacturers refrain from investing in solutions that are a poor fit for their particular plants. A strategic framework will focus their teams on the digital solutions most likely to improve KPIs across numerous plants and contribute to visible improvements in the bottom line.
Where I4.0 Will Take Process Manufacturers
Many factors driving up process manufacturing costs are beyond any individual enterprise’s control. To respond, process manufacturers need to find areas where they can reduce the long-term cost of production by augmenting rather than replacing most plant infrastructure. Fortunately, I4.0 technologies are already proving their worth in the context of legacy process manufacturing plants.
At one chemical plant, for example, Wipro worked with a manufacturing client to advance quality improvements by enabling a digital twin of the plant. Equipped with a unified data platform that ingested data from numerous disparate IT and OT systems, supported by additional instrumentation along the production line and new AI/ML-driven analytics models, the manufacturer achieved improvements in key quality KPIs while creating a global template for scaling I4.0 interventions.
Typically, advanced manufacturing sectors like automotive and consumer electronics have been at the leading edge of digital adoption. However, in reality, process manufacturers have a distinct advantage: For the foreseeable future, the expensive phase of building next-generation smart factories can be mitigated through digital adoption that delivers tangible improvements to key business metrics. Many legacy process manufacturing plants are triumphs of 20th century engineering, and a new digital overlay can quickly bring those plants up to a 21st century speed.
