The Perpetual Licensing Model: A Path to True Ownership and Lower Cost of Ownership for Industry 4.0

In the rapidly advancing era of Industry 4.0, manufacturers must choose the right technology to optimize their shop floor and embrace the potential of digital transformation. While some may opt for quick-fix solutions like cloud-based SaaS monitoring systems due to executive pressure, they often find themselves disillusioned with the lackluster results and the absence of true ownership. In contrast, the perpetual licensing model stands out as a compelling alternative, offering data security and control and a lower cost of ownership in the long run.

SaaS Model: A Short-Term Solution with Long-Term Pitfalls

Many manufacturers, seeking quick wins and minimal effort, gravitate towards Software-as-a-Service (SaaS) models for Industry 4.0 implementation. However, likening this approach to joining a gym and expecting results without putting in the effort is an accurate analogy. The initial allure of low upfront costs and easy deployment can blind manufacturers to the long-term consequences of a SaaS-based system.

The true cost of the SaaS model becomes evident when calculating the expenses over a ten-year period. At $99 per machine per month, with 25 machines, the annual cost amounts to $29,700, reaching a staggering $297,000 over ten years. Shockingly, despite these significant expenditures, the manufacturer does not own any part of the software or infrastructure, making it akin to renting a service rather than possessing it.

Perpetual Licensing: The Gateway to Ownership and Lower Total Cost

In contrast, the perpetual licensing model offers a more attractive path to ownership and a lower total cost of ownership. Although the initial investment may appear higher, it pales in comparison to the cumulative expenses of the SaaS model over time. Let’s delve into the numbers for a clearer understanding.

With perpetual on-premise deployment, the licensing and deployment cost amount to $120,000 (for 25 machines) plus annual software maintenance of $12,900, totaling $129,000 over a decade. Adding these costs together results in a total cost of ownership of $249,000. While this number may seem substantial, it is significantly lower than the $297,000 spent on SaaS with no ownership rights.

Advantages of Perpetual Licensing over the SaaS Model:

1. **True Ownership**: Manufacturers gain full ownership of the software licenses by opting for the perpetual licensing model. This ownership grants them more control over customization, data security, and future upgrades, ensuring they are not at the mercy of a third-party provider.

2. **Data Security and Control**: Perpetual on-premise deployment guarantees data security within the manufacturer’s infrastructure. This level of control is particularly crucial for sensitive manufacturing data that needs to be safeguarded from potential cyber threats.

3. **Lower Total Cost of Ownership**: As demonstrated by the comparison, the perpetual licensing model proves to be the more cost-effective choice in the long term, offering considerable savings over the SaaS model, especially beyond the ten-year mark.

4. **Flexibility and Customization**: Unlike SaaS, perpetual licensing allows manufacturers to tailor the software to their specific needs and processes. This customization can lead to greater operational efficiency and productivity gains.

5. **Predictable Costs**: With perpetual licensing, manufacturers have more predictable costs, knowing exactly what they need to budget for the software and maintenance expenses without unexpected price hikes.


While the SaaS model may appear tempting at first glance with its lower initial costs and easy setup, it eventually reveals itself as an expensive and unfulfilling option in the long run. On the other hand, the perpetual licensing model proves to be a prudent investment, granting true ownership, data security, control, and a lower total cost of ownership. By committing to “own it” through perpetual licensing, manufacturers can confidently navigate the Industry 4.0 landscape, embracing the transformative potential of advanced technology while optimizing their shop floor operations.

Note to reader: The perpetual licensing model is reflective of MERLIN Tempus. Discounts on licensing increase with volume. The example for SaaS is a real-life example of a simple monitoring system costing more than a comprehensive operations management system. There is no denying the fact that there is cost justification in on-premise deployment with perpetual licensing. Furthermore, with emerging data of shocking data retention and egress charges, the scale is tipped fully in the direction of ownership and control.

Exposing the Vulnerabilities of Cloud Environments: Embrace On-Premise Machine Monitoring Systems for Enhanced Security

As organizations navigate the treacherous landscape of data breaches in cloud environments, it becomes evident that the illusion of security is shattered. The Verizon Data Breach Investigations Report (DBIR) 2020 reveals an alarming surge of 43% in web application breaches, with over 80% of these incidents leveraging stolen credentials [¹]. Compounding the issue, nearly a quarter of all breaches involved cloud assets, with compromised credentials responsible for a staggering 77% of these cases [²].

Amidst these vulnerabilities, a stark reality emerges: the reliance on cloud vendors and third parties exposes organizations to potential security gaps beyond their control. The lack of complete oversight in securing and protecting access to data within cloud environments raises concerns about maintaining a robust security posture.

To address these challenges and avoid exposing the organization to expanding threats, an alternative solution presents itself: embracing on-premise machine monitoring systems. By adopting an on-premise approach, organizations regain control over their data security and mitigate the risks associated with cloud environments.

An on-premise machine monitoring system empowers organizations to establish stringent measures within their own infrastructure. By safeguarding sensitive information in their secure environment, organizations eliminate the vulnerabilities inherent in relying solely on cloud platforms. With complete control over data management, access controls, and security protocols, organizations can proactively safeguard against stolen credential data breaches.

Moreover, on-premise machine monitoring systems seamlessly integrate with existing internal IT security measures. By augmenting robust password policies and implementing multi-factor authentication (MFA) for all users, organizations fortify their defense mechanisms. Combining technology-driven solutions with comprehensive security training for employees further strengthens the overall security posture. By equipping users with knowledge and tools to identify and thwart social engineering attacks, such as phishing and vishing, organizations can effectively diminish the risk of compromised credentials.

Embracing an on-premise machine monitoring system not only addresses the vulnerabilities of cloud environments but also empowers organizations to take charge of their data security. By investing in their own infrastructure, organizations regain control over their security landscape, mitigating the risks posed by expanding threats.

In conclusion, the vulnerabilities of cloud environments and the reliance on cloud vendors and third parties necessitate a strategic shift towards on-premise machine monitoring systems. By adopting this alternative solution, organizations regain control over their data security, reduce the risks of stolen credential data breaches, and reinforce their overall security posture.

References: [¹] “Verizon DBIR 2020: Credential Theft, Phishing, Cloud Attacks” – CyberArk. Available at: [Link to the source] [²] “Stolen credentials, cloud misconfiguration are most common causes of breaches: study” – IT World Canada. Available at: [Link to the source] [³] “Tackling The Double Threat From Ransomware And Stolen Credentials” – Forbes. Available at: [Link to the source] [⁴] “How to Prevent Stolen Credentials in the Cloud” – CSO Online. Available at: [Link to the source]

Critique on the Negative Implications of Cloud Computing

Introduction: Cloud computing has undoubtedly revolutionized the IT industry, offering numerous benefits such as scalability, flexibility, and increased accessibility. However, it is essential to critically analyze the negative implications associated with this technology. This critique explores the potential downsides of cloud computing, focusing on the high costs and hidden expenses highlighted in several articles.

  1. Escalating Costs: The first concern revolves around the escalating costs of cloud computing. As highlighted in the Forbes article, organizations often underestimate the expenses associated with cloud services. Factors such as data transfer fees, storage costs, and performance requirements contribute significantly to the overall expenditure. This cost escalation can lead to budget overruns and negatively impact an organization’s financial resources.
  2. Hidden Costs: The InformationWeek article draws attention to the hidden costs that organizations may encounter when adopting cloud computing. These costs include additional charges for data egress, network latency, and the complexity of managing multiple cloud providers. Such expenses can quickly accumulate, catching businesses off guard and straining their IT budgets. The lack of transparency in pricing models further exacerbates the challenge of accurately predicting and managing costs.
  3. Diminished Return on Investment (ROI): Another issue raised in the critique is the diminishing ROI associated with cloud computing, as mentioned in the Forbes article. While cloud migration initially offers cost savings and increased innovation, companies may experience diminishing returns over time. This can be attributed to factors like cloud sprawl, where the sheer volume of workloads leads to uncontrollable costs and complex infrastructure. As a result, organizations may find themselves spending more on cloud services than they did on their previous on-premises systems.
  4. Vendor Lock-In: A critical aspect discussed in the Wall Street Journal article is the issue of vendor lock-in. Once organizations commit to a specific cloud provider, it becomes challenging and costly to switch to an alternative provider or bring workloads back on-premises. This lack of flexibility can limit an organization’s agility and inhibit its ability to respond to changing business needs or take advantage of better pricing options.

Conclusion: While cloud computing has undoubtedly brought significant advancements, it is crucial to consider the negative implications associated with this technology. The critique has shed light on the high costs and hidden expenses, including budget overruns, hidden fees, and diminishing ROI. Additionally, the issue of vendor lock-in can hinder organizations’ flexibility and strategic decision-making. By recognizing these challenges, organizations can better prepare and strategize to mitigate the negative implications while leveraging the benefits of cloud computing effectively.



  1. “Cloud Computing Costs: Are You Spending Too Much?” – This article from Forbes explores the potential pitfalls and hidden costs associated with cloud computing. It discusses strategies to optimize cloud spending and highlights real-world examples of companies grappling with high cloud costs. [Link:]
  2. “The High Cost of Cloud Computing: A Wake-Up Call” – This article published on InformationWeek discusses the increasing costs of cloud computing and the need for organizations to manage their cloud spend effectively. It provides insights into cost optimization strategies, including resource allocation, automation, and cloud governance. [Link:]
  3. “The Cloud’s Hidden Costs: How to Budget Wisely” – This article on highlights the hidden costs of cloud computing and provides tips for budgeting wisely. It covers various cost factors, such as data transfer fees, storage costs, and the impact of performance requirements on pricing. [Link:]
  4. “The Hidden Costs of Cloud Computing” – This article from the Wall Street Journal delves into the less obvious expenses associated with cloud computing. It discusses factors like data egress charges, network latency costs, and the challenges of managing multiple cloud providers. [Link:]

The Cloud Backlash Has Begun

The great cloud migration, which began about a decade ago, brought about a significant revolution in the field of IT. Initially, small startups and businesses without the means to build and manage physical infrastructure were the primary users of cloud services. Additionally, companies saw the benefits of moving collaboration services to a managed infrastructure, leveraging the scalability and cost-effectiveness of public cloud services. This environment enabled cloud-native startups like Uber and Airbnb to thrive and grow rapidly.

In the subsequent years, a vast number of enterprises embraced cloud technology, driven by its ability to reduce costs and accelerate innovation. Many companies adopted “cloud-first” strategies, leading to a wholesale migration of their infrastructures to cloud service providers. This shift represented a paradigm change in IT operations.

However, as the cloud-first strategies matured, certain limitations and challenges have emerged. The efficacy of these strategies is now being questioned, and returns on investment (ROIs) are diminishing, resulting in a significant backlash against cloud adoption. This backlash is primarily driven by three key factors: escalating costs, increasing complexity, and vendor lock-in.

The widespread adoption of the cloud has led to a phenomenon known as “cloud sprawl,” where the sheer volume of workloads in the cloud has caused expenses to skyrocket. Data-intensive processes such as shop floor machine data collection should never have been considered for the cloud. Manufacturers are finding that datasets of hundreds of gigabytes should never have left the premises. Enterprises are now running critical computing workloads, storing massive volumes of data, and executing resource-intensive programs such as machine learning (ML), artificial intelligence (AI), and deep learning on cloud platforms. These activities come with substantial costs, especially considering the need for high-performance resources like GPUs and large storage capacities.

In some cases, companies spend up to twice as much on cloud services as their previous on-premises systems. This significant cost increase has sparked a realization that the cloud is not always the most cost-effective solution. As a result, a growing number of sophisticated enterprises are exploring hybrid strategies, which involve repatriating workloads from the cloud back to on-premises systems.

By developing true hybrid strategies, organizations aim to leverage the benefits of both cloud and on-premises systems. This approach allows them to optimize their IT infrastructure based on the specific requirements of different workloads and data science initiatives. Moreover, hybrid strategies offer greater control over costs, reduced complexity, and increased flexibility to avoid vendor lock-in.

In fact, leading technology companies like Nvidia have estimated that moving large and specialized AI and ML workloads back on premises can result in significant savings, potentially reducing expenses by around 30%.

In conclusion, while the great cloud migration brought undeniable advantages in terms of scalability and innovation, the limitations and challenges associated with cloud-first strategies have triggered a backlash. To address these issues, enterprises are embracing hybrid strategies, repatriating critical workloads to on-premises systems and leveraging the benefits of cloud and traditional infrastructure. This evolution represents the next generational leap in IT, enabling organizations to support their increasingly business-critical data science initiatives while regaining control over costs and complexity. If your organization has data being collected and stored in the cloud, you may want to start to plan to migrate that ever-growing data back to on-premise and mitigate the costs. If your organization is thinking of a cloud solution, think again.


Thomas Robinson is COO of Domino Data Lab,

What Is Continuous Improvement?

Continuous improvement projects are initiatives undertaken by organizations to enhance processes, products, or services incrementally over time. The goal is to achieve small, ongoing improvements that can bring significant long-term benefits. These projects are typically driven by a structured approach that involves identifying areas for improvement, implementing changes, and evaluating the results to guide further improvements. Here are some key aspects and strategies related to continuous improvement projects:

  1. Continuous Improvement Philosophy: Continuous improvement is rooted in the belief that small, continuous changes can add up to substantial improvements over time. It emphasizes the importance of seeking feedback, engaging employees at all levels, and fostering a culture of learning and innovation within the organization.
  2. Continuous Improvement Methodologies: Several methodologies and frameworks are commonly used in continuous improvement projects, including:
    • Lean: Lean principles focus on eliminating waste, streamlining processes, and optimizing efficiency. Techniques such as value stream mapping, 5S (sort, set in order, shine, standardize, sustain), and Kaizen events are often employed.
    • Six Sigma: Six Sigma aims to reduce defects and process variations by employing statistical analysis and problem-solving techniques. It follows a structured DMAIC (Define, Measure, Analyze, Improve, Control) approach.
    • PDCA (Plan-Do-Check-Act): Also known as the Deming Cycle or Shewhart Cycle, PDCA is an iterative four-step management method for continuous improvement. It involves planning a change, implementing it on a small scale, observing the results, and then standardizing or adjusting based on the outcomes.
    • Agile: Originally developed for software development, Agile methodologies, such as Scrum or Kanban, have been adopted in various industries. They emphasize iterative development, collaboration, and adaptability to respond to changing requirements.
  3. Steps in a Continuous Improvement Project:
    • Identify the objective: Clearly define the goal or problem that the project aims to address. It could be improving efficiency, reducing defects, enhancing customer satisfaction, or optimizing a specific process. Constraint identification can be achieved by implementing a Manufacturing Operations Management System such as MERLIN Tempus.
    • Gather data and analyze: Collect relevant data about the current state of the process or system. Analyze the data to identify areas of improvement, bottlenecks, or root causes of problems.
    • Generate solutions: Brainstorm potential solutions or changes to address the identified issues. Evaluate the feasibility, impact, and risks associated with each solution.
    • Implement changes: Select the most promising solution and implement it on a small scale or as a pilot project. Document the changes made and ensure proper communication and training to relevant stakeholders.
    • Monitor and measure: Track the performance metrics or key performance indicators (KPIs) to assess the impact of the implemented changes. Compare the results with the baseline data to determine the effectiveness of the improvements. This can easily be achieved through a Manufacturing Operations Management System such as MERLIN Tempus.
    • Standardize and sustain: Standardize the improved process or system once the changes have been proven effective. Develop procedures, guidelines, or training materials to sustain the changes over time.
    • Iterate and improve: Continuous improvement is an ongoing process. Learn from the project’s outcomes and use that knowledge to identify further areas for improvement. Repeat the steps to initiate new improvement projects.
  4. Tools and Techniques: Various tools and techniques can support continuous improvement projects, including:
    • Process mapping and flowcharts: Visual representations of processes help identify inefficiencies, bottlenecks, or unnecessary steps.
    • Root cause analysis: Techniques like the 5 Whys or fishbone diagrams help identify the underlying causes of problems.
    • Statistical analysis: Tools such as control charts, Pareto charts, or scatter diagrams can provide insights into process variations and patterns.
    • Quality management systems: Software solutions like Total Quality Management (TQM) or Enterprise Resource Planning (ERP) systems can streamline data collection, analysis, and reporting for continuous improvement initiatives.
    • All of the points listed under tools and techniques require data. In some cases, it takes up to three months. A Manufacturing Operations Management System such as MERLIN Tempus collects and presents data continuously. This means that a CI initiative can begin immediately with actionable and accurate automated data collection and operator insights.

Continuous improvement projects are fundamental to many organizations, enabling them to adapt, innovate, and stay competitive in a rapidly changing environment. By fostering a culture of continuous improvement, organizations can drive incremental enhancements that lead to long-term success.