Carlton & United Breweries

Digital twins are increasingly espoused as an effective means of improving the efficiency of manufacturing plants and minimizing their associated carbon footprint. Indeed, the link between the digitization paradigm and its impact (on both business and the environment) is sometimes regarded as self-evident or obvious. This sentiment is often fortified by the promise of things like high-fidelity measurement, online monitoring, and scenario analysis, all to yield performance-improving insights. However, simply having access to more data, metrics, and analytical capability does not in and of itself guarantee an improvement, let alone support a business case for the implementation of such a platform.

The global digital twin market is projected to grow substantially over the next decade. Some reports have valued the market at around USD 8.6 billion in 2022, with projected growth through USD 11.51 billion in 2023 and up to over USD 137 billion by 2030. However, the p

1. rojected spending of others is not evidence of a return on investment for you. Indeed, history has no shortage of marketled IT-related paradigms that have resulted in financially unfavorable outcomes for some companies. Arguably, in many cases, the absence of a quality realistic business case (with ROI determination) is a likely common factor.

So the question then becomes, ‘What specifically could you target with a digital twin that could increase your likelihood of identifying opportunities for improvement and for making an impact?’

New Insights into the Capacity for Waste Reduction

An established, ever-present, but appealing pathway is the identification and reduction of waste. As the reduction of waste and the improvement of yield and efficient improvement of yield and efficiency all have clearly established links to profitability, they have long been a focus of the manufacturing industry. These concepts and their subsequent emerging frameworks are far from novel. The essence of the Lean Manufacturing paradigm is the ‘systematic elimination of Swaste

So, what specifically could digital twins offer in this space that could justify the required investment?

The answer to this question depends in part on the initial level of the ‘maturity’ of the manufacturing plant (in terms of informatics, automation, and energy management programs). Plants of lower maturity have typically (and by definition) not yet identified and exhausted the easy wins (low-hanging

Infection Cfruit), especially in the context of energy efficiency and emissions reduction. In such cases, the offering and potential impact of digital twins are more likely to be broader, greater, and more obvious.

“No one wins if end users do not realize a return on their digitization investment”

So, for the more mature plants (that are by definition pro

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Patient Ebably already efficient), how can the remaining opportunities (higher hanging fruit) be identified? Monitoring Non-productive Consumption One way of contextualizing the (wastereduction) opportunities is through the lens of non-productive consumption (NPC). This can be defined as the portion of the consumed energy/utilities that could be reduced without reducing the quality or quantity of the saleable product. By extension – non-productive greenhouse gas Emission

Medication s (NGE )would be the portion of emissions resulting from NPC.

Contemporary manufacturing plants typically monitor energy and utility consumption with KPI metrics normalized against the quantity of product produced. Normalized metrics attempt to present figures somewhat independent of variations in production output to en

Reporting and Learning Sable performance comparisons between lines/plants over time. However, they cannot provide any real indication of the opportunity for improvement (i.e., ‘waste’ identification and Quality and Performance Iquantification). Whereas the purpose of the NPC metric is to directly measure the actual waste (opportunity for improvement). In a simplified example in the context of a manufacturing plant in the food & beverage industry, the NPC could span three aspects of production and could include:

 1.Packaging Materials:

The portion of packaging materials that don’t make it to a finished saleable product (including finished but non-saleable products)

 2. Product:

This includes the loss of both not-yet-packaged products during the production process, as well as within finished non-saleable packaged product

3.Energy & Utilities

oIdle runs (periods in between actual production runs where the production line is on/powered up, temperatures are maintained, and

Physical Security compressed air systems are at pressure) o Efficiency gap (loss): The energy consumption of a pro Data Managementduction run as compared to past best performance for a similar volume of the same product on the same production line These aspects are not trivial to incorporate into an automated, sustainable NPC metric (especially the efficiency gap). They require integrating accurate plant floor data (segregated by production run or batch) with higher-level database content on material and products.

There is no real way to objectively measure the upper limit for ‘maximum efficiency’ as it will change over time as the inherent efficiency of new equipment improves. One of the strengths of the efficiency gap component of the NPC metric is that it provides a relative comparison against the

Cybersecuritybest performance that has been achieved on the same plant/line for the same product. Hence, by definition, it is a real achievable target. Another advantage of this metric type is that it is inherently adaptive, as the target will change over time as the line becomes more efficient.

While Industry 4.0 infrastructure could be sufficient for monitoring NPC, digital twins have the potential to provide a greater opportunity to improve NPC. In addition to providing the infrastructure required for high-fidelity measurement and identification of NPC, digital twins enable simulation-based scenario analysis of the impact that changes to equipment

and processes could have on NPC. Some more specific examples could include such things as:

• Simulate the impact of energy-aware scheduling on product sequence, idle runs, and, ultimately, NPC • Simulate the impact of product sequence (change-over and runout) on packaging yield

• Compare the NPC for production of Incident Response Plansthe same SKU across different lines (and plants)

• Provide quantified evidence-based input into the determination of minimum viable run-length (map the NPC by production runlength)

• Compare NPC variation (for the same line and Regular Audits and Assessments

3.SKU) by shift/operator-crew

 It Is Not the Tool (Digital Twin) That Will Bring You Success, It Is What You Do With It While digital twin platforms promise a wealth of different opportunities for business impact, they are typically a significant investment. A strong business case can help to garner the support to acquire one and, importantly, to increase the li

4.kelihood of realizing the aspired return on investment.

 Waste reduction is an ever-appealing direction as it can be very profitable while also reducing the environmental impact of the business (and consequentially of its upstream supply chain). It can sometimes also result in an effective capacity increase of the plant. Waste reduction opportunities become progressively more difficult to find in plants that are

5.not new to this journey and are already somewhat efficient.

 Having a specific pathway to focus on (such as NPC) can support and strengthen a business case as well as improve the realized return on investment. Importantly, a focus on NPC can provide a pragmatic and structured pathway to quantify and predict a realistic and achievable target. The digital twin paradigm provides the required informatics infrastructure

and scenario analysis opportunity to enable NPC to be both monitored and improved. No one wins if end users do not realize a return on their digitization investment.