Roche

Abstract:

In this interview, Rutika Pethe reveals how pharmaceutical packaging plants can cut energy costs without compromising compliance. She identifies hidden losses in HVAC and idle equipment, offers low-capex interventions, and discusses the role of AI and Pharma 5.0 in driving predictive optimization on the path to net-zero.

1. Energy efficiency in pharmaceutical packaging is often overlooked compared to product innovation. What are the biggest structural inefficiencies you identified in modern packaging plants?

Inefficiencies are often seen in the combination of legacy infrastructure and complex production layouts. Many facilities operate with equipment that was not originally designed with energy efficiency in mind. In addition, utilities such as HVAC and compressed air are often managed at system level rather than being closely aligned with actual production demand, which can lead to avoidable baseline consumption. These inefficiencies accumulate over time and create significant hidden energy costs.

2. In your presentation on energy management, what are the most impactful “low-capex, high-impact” interventions companies can implement immediately?

Starting with energy transparency, sub-metering critical packaging assets quickly isolates peak consumers. Based on my presentation and recent projects, some effective measures include:

• Compressed air leak detection and repair programs 
• HVAC scheduling aligned with actual production requirements
• Automatic shutdown of idle equipment
• Optimization of machine startup and shutdown sequences

3. How do you balance strict pharmaceutical compliance requirements with the need to optimize energy consumption in packaging operations?

Compliance always remains non-negotiable. An approach for legacy plants is to treat validated operating ranges as a design space identifying current operating limits and recover gaps as energy savings without touching limits themselves. Considering energy efficiency from the very beginning while planning new plants is most impactful in terms of energy efficiency. The objective is not to compromise compliance but to eliminate waste that exists outside of compliance requirements.

4. Where do you see the highest hidden energy losses—compressed air systems, HVAC, machine idle time, or packaging line design?

HVACs represent the largest single utility energy loss in pharmaceutical packaging plants. As these HVAC systems often run on full capacity and fixed-schedule programs even during night shifts, weekends and during production gaps.

Machine idle time is another important hidden loss: A blister line consuming a baseload during idle times for a few hours per shift accumulates significant waste that manufacturing leadership can rarely track because cycle time, not energy consumed, is the recorded KPI.

5. What role does Pharma 5.0 play in transforming energy management from reactive reporting to predictive optimization?

Pharma 5.0 will shift energy management from reactive reporting to predictive optimization by using AI and human-centric Digital Twins by anticipating production energy spikes and maintain equipment efficiency before costly failures occur. However, this transition requires careful planning due to the initial costs of upgrading legacy systems, the need to train teams on new digital tools and the ongoing work to ensure predictive algorithms perfectly align with strict validation standards.

6. Can energy optimization ever conflict with product safety or packaging integrity—and how should companies resolve such trade-offs?

In my view, energy optimization does not have to conflict with product safety or packaging integrity when it is implemented correctly. As discussed earlier, all optimization initiatives must remain within regulatory requirements and validated process parameters. Modern monitoring, automation and alerting systems should allow manufacturers to continuously track critical quality and environmental conditions ensuring that any deviations are detected and addressed immediately. 

7. How can AI and automation be leveraged to dynamically reduce energy consumption during low-demand production cycles?

AI could play a significant role in reducing energy consumption during low-demand production cycles when integrated with monitoring, automation, alerting, and energy management systems. By continuously analyzing operational data, AI could possibly identify inefficiencies, detect deviations in real time and support faster corrective actions.

AI could evaluate production scenarios and recommend energy-saving measures, such as adjusting machine operating modes. These recommendations however need and should be reviewed as well as verified by on-site engineers to ensure they align with operational requirements and regulatory standards.

8. In your experience, what organizational barriers most often prevent energy management initiatives from scaling across sites?

The biggest challenge is often organizational alignment rather than technology. In my experience, the biggest barrier to scaling energy management initiatives across sites are the variations across both digital and operational infrastructures. Sites often differ in their levels of digitization, metering systems, machinery, and software setups, which makes it difficult to directly replicate solutions. 

To drive meaningful improvements, energy efficiency should become a mandatory KPI that management reviews regularly alongside key metrics such as productivity and quality. What gets measured and tracked at the leadership level ultimately drives organizational focus and results. 

9. How critical is workforce training in achieving measurable improvements in plant-level energy efficiency?

Workforce training is absolutely essential. Technology can identify opportunities but operators and technicians ultimately influence daily energy consumption. 

Providing transparency on key energy consumers is the first step as it helps operators clearly identify energy consumption areas and how actions affect consumption. With visible data forms like dashboards, compressed air leaks, machines in idle patterns etc. can be addressed and corrective actions can be undertaken towards energy efficiency.

10. How do regulatory expectations in Europe influence energy reporting and sustainability KPIs in packaging plants?

The European regulatory landscape has become substantially more demanding in the last few years, with direct implications for pharmaceutical packaging operations. Regulations now require large companies to report energy consumption as well as Scope 1 and Scope 2 emissions. For packaging specifically, this means that site-level energy data must be auditable and traceable, not just estimated from utility bills. Usage of alternative packaging materials and its recyclability will also shortly be required. Overall, the mandated regulations call for more granular energy reporting and the beginning of sustainability KPIs in the packaging plant. 

11. Looking ahead, what will define a “net-zero pharmaceutical packaging plant” in the next 5–10 years?

I expect a net-zero pharmaceutical packaging plant to be based on three key elements. Firstly, renewable energy integration including onsite generation and green energy sourcing. Secondly, data driven optimization measures based on energy and sustainability KPIs. And lastly, a higher efficiency in operations through automation and AI.