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.

Downtime is Inevitable. Unplanned Downtime does not have to be.

Downtime is an Inevitable Aspect, but Unplanned Downtime Can Be Prevented. Downtime and production losses are something every manufacturer experiences. The good news is technology solutions like MERLIN are available that dramatically reduce the main sources of revenue loss: Unplanned Downtime, Minor Stoppages, and Changeover Time.

When solutions like MERLIN are implemented, manufacturers quickly realize how much time and revenue is lost with traditional strategies that are manual, time-consuming, and ineffective.

Based on more than 25 years of experience in manufacturing, we’ve outlined the top 3 profit killers in the industry and how they can be avoided.

  1. Minor Stoppages
    Minor stoppages are typically the most hidden factors of profit loss, with dramatically more impact on downtime and revenue than manufacturers realize.

Traditional manual, paper-based systems rarely capture minor stoppages, and the data is often unreliable.

MERLIN, along with its IIOT technology solutions, captures every downtime event and the root cause of each stoppage.

Example: A packaging manufacturer manually tracked stoppages but only captured unplanned downtime lasting 5 minutes or more.

The manufacturer implemented MERLIN’s Tempus Enterprise Edition platform to gain real-time visibility into machine-level performance, including all stoppages.

MERLIN identified micro stops in just one week, totalling 7 hours. These were unplanned stops that were previously not recorded. The platform also alerted operators at the time of each stoppage so problems could be fixed as they happened.

  1. Unplanned Downtime
    Downtime is the largest source of lost production time and revenue. Yet, it’s estimated that 80% of manufacturers cannot accurately calculate their downtime or understand the costs associated with lost production.

MERLIN Tempus provides real-time insight into the source of unplanned downtime, including which machines have the most occurring faults and the most aggregated downtime.

  1. Changeover Time
    Changeover time accounts for the largest source of overall downtime. Yet, most manufacturers have little insight into how long changeovers take or what they can do to reduce changeover time.

A SMED initiative (Single Minute Exchange of Dies) is the standard technique for analyzing and reducing the time it takes to complete equipment changeovers. Most SMED initiatives are manual projects using Excel spreadsheets and stopwatches.

MERLIN Tempus accurately compares estimated changeovers vs actual and accelerates cost savings.

Are you ready to stop the profit killers in your manufacturing organization? It’s easier than you think. Rapid implementation of MERLIN Tempus means you’ll have visibility into your plant, line, and machine data in just days! Contact an expert from Memex today to learn more.

Essential Industry 4.0

In today’s manufacturing landscape, unplanned downtime is one of the leading causes of lost productivity, resulting in delays, dissatisfied customers, and substantial revenue losses. Recent studies estimate that this issue alone costs industrial manufacturers a staggering $50 billion annually. However, the solution lies in embracing Industry 4.0, the digital transformation of manufacturing, which leverages data analytics, artificial intelligence, machine learning, and other advanced technologies to enhance productivity, agility, customer satisfaction, and sustainability¹.

Despite the immense potential of Industry 4.0, many manufacturers still struggle to scale up their efforts and fully realize the value of their digital transformations². Financial hurdles, organizational challenges, and technology roadblocks are among the obstacles they face².

The cost of not adopting Industry 4.0 can be substantial, as evidenced by the average cost of an hour of downtime for a factory, estimated to be $260,000⁴. However, implementing Industry 4.0 solutions, such as predictive maintenance, can drastically reduce these costs³. Moreover, failing to embrace Industry 4.0 technologies means missed opportunities for improving customer service, delivery lead times, employee satisfaction, and environmental impact¹.

Industry 4.0 goes beyond addressing downtime and offers transformative benefits for manufacturers. It represents the current era of connectivity, advanced analytics, automation, and advanced manufacturing technology that has been revolutionizing global business for years². While small and medium-sized businesses (SMEs) may face challenges in adopting Industry 4.0 due to limited resources and knowledge, there are also advantageous trends for them. These include new business models, value-added services, networking, collaboration, increased flexibility, and enhanced quality¹.

SMEs should not underestimate the potential of Industry 4.0. By investing in research and development related to Industry 4.0, they can tap into a market with an estimated value creation potential of $3.7 trillion for manufacturers and suppliers by 2025². This represents an unprecedented opportunity for SMEs to innovate and compete globally.

In conclusion, Industry 4.0 is not a mere buzzword but a necessity for manufacturers aiming to remain competitive and drive growth. With the significant costs associated with unplanned downtime and the tremendous potential of Industry 4.0, overcoming the challenges and embracing this digital transformation is essential. By adopting Industry 4.0 technologies, businesses can unlock increased productivity, customer satisfaction, and sustainability. SMEs, in particular, should recognize the beneficial trends and seize the opportunity to innovate and thrive in the global market. The future belongs to those who adapt and evolve with Industry 4.0.

If you have any further questions about Industry 4.0 or need more information, please ask!

How to plan for a successful POC

Companies involved in a proof-of-concept (POC) project or phased adoption approach typically begin by connecting a single system to the network and enrolling it into a manufacturing operations management system. The success of such a project relies heavily on following the right steps for deploying and commissioning the machine, along with establishing a clear framework of objectives.

Regarding large-scale information systems, network topology plays a crucial role. It encompasses layers 0, 1, and 2, determining the system’s performance, security measures, error detection capabilities, and resource utilization. To ensure an effective topology, it is important to assess and create it carefully, considering factors like performance, security, maintenance, scalability, and management.

Choosing the right machine asset(s) for a POC or phased plan requires clearly understanding the desired outcomes. The objectives may include automatically capturing operational events, and specific process data, enabling operator interaction based on operations, and even auto-creation of jobs, error reason code identification, and operator response lookup. It’s also important to consider machine-specific capabilities, such as multi-spindle functionality, pallets, tombstones, multi-part count, high-speed part count, and its position in the value stream. Is it a finishing machine determining the throughput for a group of operations? Or is it a constraint machine that collects data to assist in resolving constraints and adopting a continuous improvement methodology?

Selecting the appropriate information system for manufacturing operations involves evaluating various options. From legacy systems like SCADA and process mapping to MES, batch-run systems, and emerging operations and monitoring systems, stakeholders must prioritize their desired functions and features. The challenge lies in identifying deliverables and quantifying unexpected aspects, especially with numerous products making similar claims. Factors to consider include the system’s services, distribution capabilities, scalability, expandability, productization, breadth of intellectual property (IP) across manufacturing, IT, system integration, engineering, vendor stability, and longevity.

An essential aspect of successful adoption is the collection methodology. A system that preprocesses events by normalizing and storing them as events into a single source type proves highly efficient, reducing the need for extensive post-processing. On the other hand, systems that collect raw data events as states often face efficiency and performance issues due to analytics and metrics calculations being performed after collection.

The chosen data storage methodology determines the flexibility of the information system. Storing events as sourcing pattern events enables answering both the “what happened” and “why” questions, tracking jobs through operations, recording multiple events over time, and benefiting from an event sourcing patterns approach. Additionally, calculating metrics globally rather than individually for each object in the system reduces complexity and ensures congruent results.

Job management is a critical feature that should be supported by the system, allowing for sales orders, work orders, part numbers, product standards, and operation steps processes. These elements provide granular job-specific information, metrics, and operational states. A structured product management feature should be considered for effective job tracking.

Adaptability is another crucial aspect of a manufacturing information system, enabling the expansion of event categorization and the addition of operational and process states as needed. This flexibility is essential for continuous improvement efforts and tracking new constraint sources or non-conformity reasons.

Once the topology is established, machines are selected, and the information system is chosen, it’s time to plan and execute the roadmap for the POC or phased plan. The roadmap should consist of specific, measurable, and qualifiable objectives that can be successfully applied to the rest of the plant. Internal champions should be selected to allocate necessary production, engineering, and IT resources. Finally, a kickoff meeting with the vendor should be arranged to assess their action plan and determine the distribution of responsibilities.