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Deploying a Manufacturing Analytics System: On-premises vs. cloud-based solutions

A Manufacturing Analytics System (MAS) integrates across data sources and provides valuable insights into production processes. As companies evaluate their options, a key decision emerges: should they deploy the MAS onto their own premises, or opt for a cloud-based Software as a Service (SaaS) solution?

This article discusses the merits of each approach to help businesses make an informed decision. It focuses on five major discussion points: data security, scalability, maintenance, cost effectiveness, and support.

Data Security and Compliance

On-Premises: Tailored to Specific Needs

The primary advantage of on-premises deployments lies in the enhanced control and security it offers. Companies with highly sensitive data often prefer on-premise solutions due to their stringent security requirements. It can be easier to conform to stringent or inflexible policies by hosting the MAS internally. This setup allows for a more hands-on approach to data management, ensuring compliance with standards like GDPR, HIPAA, NIST, or other industry-specific regulations.

Cloud-Based Solutions: Robust, Standardized Security

Cloud-based MAS solutions have often been perceived as less secure, and some companies generally distrust the cloud. However, especially in recent years, cloud offerings have evolved significantly. Reputable cloud providers employ robust security measures, including advanced encryption, regular security audits, and compliance with various international standards. They have the resources and expertise to implement and maintain higher levels of security than individual organizations can achieve on their own. For businesses without the capacity or desire to manage complex security infrastructure, a cloud-based MAS offers a secure, compliant, and hassle-free alternative.

Scalability on Demand

On-Premises: Tailored to Specific Needs

An on-premises MAS deployment allows for extensive customization. Businesses can tailor the system to their specific IT and OT landscape, including guaranteed real-time responses. This capability is particularly beneficial for companies requiring deep integration with legacy systems and factory equipment. On the other hand, scaling on-premises solutions typically requires significant investment in hardware and infrastructure, as well as the technical expertise to manage these expansions.

Cloud-Based Solutions: Easy Scalability and Flexibility

Cloud-based MAS platforms shine in scalability. They allow businesses to scale their operations up or down with ease, without the need to invest in physical infrastructure. This scalability makes cloud solutions ideal for businesses experiencing rapid growth or fluctuating demands. Furthermore, cloud platforms are continually updated with the latest features and capabilities, ensuring businesses always have access to the most advanced tools without additional investment or effort in upgrading systems. A potential down-side is that ultimate control of the deployment lies with the cloud provider, which can be a hurdle for highly regulated industries.

Maintenance and Updates

On-Premises: Hands-On, Resource-Intensive Maintenance

Maintaining an on-premises MAS can require a dedicated IT personnel to manage hardware, perform regular software updates, and troubleshoot issues. This hands-on approach offers complete control over the maintenance schedule and system changes, but can be resource-intensive. Companies who already have specialized IT teams due to the nature of their operations may find this approach a natural fit.

Cloud-Based Solutions: Hassle-Free, Automatic Updates

Cloud-based solutions significantly reduce the burden of maintenance. The service provider typically manages all aspects of system maintenance, including regular updates, security patches, and technical support. Automatic updates ensure that the system is always running the latest software version, providing access to new features and improvements without additional effort or cost. This allows businesses to focus on their core operations, without the need to allocate and manage resources for system maintenance.

Cost Effectiveness

On-Premises: Higher Initial Investment but Predictable Long-Term Costs

Deploying any system on-premises typically involves a higher initial capital expenditure, including costs for hardware, software licensing, and installation. Over the long term, these costs can be more predictable, or at least there are no cloud-subscription fees to factor in. For organizations with the necessary infrastructure already in place, this model can be cost-effective, particularly when considering the longevity and stability of the investment.

Cloud-Based Solutions: Lower Upfront Costs with Ongoing Expenses

Cloud-based MAS solutions offer lower initial costs and much quicker setup compared to on-premise installations. Businesses can avoid significant expenses on hardware and infrastructure. This subscription model converts upfront investments into ongoing operational expenses. In addition to the ease of setup, this can be more cost-effective in the short term. However, for businesses with long-term predictable usage patterns, it is important to consider the cumulative costs over an extended period.

Support

On-Premises: Customized and Direct Control

This model of deployment demands a significant commitment of internal resources for maintenance and troubleshooting, necessitating dedicated, skilled IT personnel. While on-prem provides an unmatched level of control and customization, as discussed earlier in this post, the reliance on in-house capabilities for supporting the MAS can be a considerable burden on manufacturing customers.

Cloud-Based Solutions: Broad, Expert Support with 24/7 Availability

Cloud-based MAS solutions boast a scalable, expert support structure, alleviating the need for an in-house IT team to manage the MAS deployment. This is particularly important for operations spread across multiple locations or time zones. Automatic updates and maintenance conducted by the provider ensure the system remains up-to-date without any additional effort from the customer side. Furthermore, troubleshooting is accelerated in a cloud-based system because the infrastructure is standardized and uniform. This consistency reduces complexity and variability, which significantly improves the efficiency and speed of support services.

Conclusion

The choice between deploying a MAS on-premises or in the cloud depends on various factors including data security needs, customization requirements, budget constraints, network reliability, and maintenance capabilities. Each option has its merits, and the decision should align with the specific operational, financial, and strategic objectives of the organization. At EthonAI, we offer both options to meet our customers’ needs effectively.

How modern data analytics enables better decision-making

This is a reproduction of the article “Please explain it to me! How modern data analytics enables better decisions,” which we wrote for the United Nations Conference on Trade and Development (UNCTAD).

Why AI’s potential is still underutilized in decision-making

British industrialist Charles Babbage (1791-1871) once said, “Errors using inadequate data are much less than those using no data at all.” Three industrial revolutions later, it’s surprising how often decisions are still made on gut feeling without data. But it doesn’t have to be like that. The distinguishing factor of the ongoing fourth industrial revolution is the unparalleled access to and connection of data. However, having data is one thing; another is to make good use of them. That’s where AI comes in.

Management and policymaking are about decision-making, which is best when grounded in facts. Facts, on the other side, are verified truths derived from analyzing and interpreting data. The challenge then is to collect and present data in a form that can be turned into information and actionable knowledge. Luckily, the rampant developments in computer science and information technologies, enable decision-makers more, faster, and better access to data. In addition, AI can help decision-makers establish the needed facts by generating data-driven insights inaccessible to humans to date. But, as this article shows, it isn’t a silver bullet. A special type of AI is needed.

With the recent rise of AI models there have been impressive developments in both creative content generation as well as automation. Yet, when it comes to decision-making, AI still faces two critical challenges: First, the complexity and opacity often found in AI models can deter trust and adoption. Many AI models operate in a “black-box” fashion, where users cannot comprehend how a suggestion has been made. When domain experts are unable to validate the AI’s outputs against their own knowledge, they tend to distrust the AI output. Second, AI systems are currently limited in their ability to perform causal reasoning, which is a critical element in any decision-making process. Knowing that an event relates to another event is interesting, but knowing what causes the events to happen is a game-changer.

Hands-on experience from the industry

The key to addressing AI’s two drawbacks is to design systems that are explainable and can be augmented with domain knowledge. To give a concrete example, consider the following. We conducted a field experiment with Siemens, where we observed factory workers who engaged in a visual quality inspection task of electronic products. Participants were divided into two groups: one aided by a “black-box” AI and the other by AI capable of providing explanations for its recommendations. The group using the explainable AI significantly outperformed those factory workers who got recommendations from the “black-box” AI. And even more interestingly, users with the explainable AI system better knew when to trust the AI and when to depend on their own domain expertise, thereby outperforming the performance of the AI system alone. Hence, when humans work together with AI, the results are superior to letting the AI make decisions alone!

Explainability is not only helpful for creating trust among decision-makers. It can also be leveraged to get the best out of humans and AI strengths. AI can sift through amounts of data, whereas humans can complement this with the physical understanding of the process to establish cause-and-effect relationships. Consider for example our research in a semiconductor fabrication facility, where we provided process experts with explainable AI tools to identify the root causes of quality issues. While the AI was able to reveal complex correlations between various production factors and the quality of the outcomes, it was the human experts who translated these insights into actionable improvements. By cross-referencing the AI’s explanations with their own domain knowledge, experts were able to design targeted experiments to confirm the underlying causes of quality losses. The result? Quality losses plummeted by over 50%. This case emphasizes the indispensable role of human expertise in interpreting data and applying it within the context of established cause-and-effect relationships.

Conclusion

The key message is that data is proliferating and AI is here to help, but without the human in the loop, don’t expect better decision-making. We need to build AI systems and tools that support the human decision-maker in getting to facts faster. For this, the recent developments in Explainable AI and Causal AI offer a promising path forward. Such tools allow users to reason about the inner workings of AI systems and incorporate their own domain knowledge when judging the AI output. It helps explain the causal relations and patterns that the AI is picking up from the data–ultimately enabling decision-makers to make better decisions.