Manufacturing Analytics: The Power of Data in the Manufacturing Industry

The effects of the pandemic have hit the manufacturing industry in ways no one could have predicted. During the last 18 months, a new term has come up frequently in the news and in conversation: the supply chain crisis. Manufacturers have been disrupted in almost every facet of their business, and they have been put to the test as to whether they can weather these challenges or not. 

Manufacturing Analytics: The Power of Data in the Manufacturing Industry

 

Manufacturing businesses that began a digital transformation prior to the current global crisis have been more agile in handling the disruptions. That is because manufacturers using data analytics and cloud technology can be flexible in adopting the capabilities they need for important business goals, be able to identify inefficiencies more quickly and be equipped to adopt a hybrid workforce to make sure production doesn’t stall. 

The pandemic has identified and accelerated the need for manufacturers to digitize and harness the power of modern technology. Real-time data and analytics are fundamental to the manufacturing industry because they create the contextual awareness that is crucial for optimizing products and processes. This is especially important during the supply chain crisis, but this goes beyond the scope of the pandemic. Manufacturers will want to, despite the external circumstances, automate for quicker and smarter decisions in order to remain competitive and have a positive impact on the bottom line. 

In this article, we’ll identify the use cases and benefits of manufacturing analytics, which can be applied in any situation at any time. 

What is Manufacturing Analytics?

Manufacturing analytics is used to capture, process, and analyze machine, operational, and system data in order to manage and optimize production. It is used in critical functions – such as planning, quality, and maintenance – because it has the ability to predict future use, avoid failures, forecast maintenance requirements, and identify other areas for improvement. 

To improve efficiency and remain competitive in today’s market, manufacturing companies need to undergo a digital transformation to change the way their data is collected. Traditionally, manufacturers capture data in a fragmented manner: their staff manually check and record factors, fill forms, and note operation and maintenance histories for machines on the floor. These practices are susceptible to human error, and as a result, risk being highly inaccurate. Moreover, these manual processes are extremely time-consuming and open to biases. 

Manufacturing analytics solves these common issues. It collects data from connected devices, which reduces the need for manual data collection and, thereby, cuts down the labor associated with traditional documentation tasks. Additionally, its computational power removes the potential errors and biases that traditional methods are prone to. 

Because manufacturing equipment collects massive volumes of data via sensors and edge devices, the most efficient and effective way to process this data is to feed the data to a cloud-based manufacturing analytics platform. Without the power of cloud computing, manufacturers are generating huge amounts of data, but losing out on potential intelligence they have gathered. 

Cloud-based services provide a significant opportunity for manufacturers to maximize their data collection. The cloud provides manufacturers access to more affordable computational power and more advanced analytics. This enables manufacturing organizations to gather information from multiple sources, utilize machine learning models, and ultimately discover new methods to optimize their processes from beginning to end. 

Additionally, manufacturing analytics uses advanced models and algorithms to generate insights that are near-real-time and much more actionable. Manufacturing analytics powered by automated machine data collection unlocks powerful use cases for manufacturers that range from monitoring and diagnosis to predictive maintenance and process automation. 

Use Cases for Cloud-Based Manufacturing Analytics

The ultimate goal of cloud-based analytics is to transition from having descriptive to predictive practices. Rather than just simply collecting data, manufacturers want to be able to leverage their data in near-real-time to get ahead of issues with equipment and processes and to reduce costs. Below are some business use cases for automated manufacturing analytics and how they help enterprises achieve predictive power:

Demand Forecasting and Inventory Management

Manufacturers need to have complete control of their supply chain in order to better manage inventory. However, demand planning is complex. Manufacturing analytics makes this process simpler by providing near-real-time floor data to support supply chain control, which leads to improved purchase management, inventory control, and transportation. The data provides insight into the time and costs needed to build parts and run a given job, which gives manufacturers the power to more accurately estimate their needs for material to improve planning. 

Managing Supply Chains

For end-to-end visibility in the supply chain, data can be captured from materials in transit and sent straight from external vendor equipment to the manufacturing analytics platform. Manufacturers can then manage their supply chains from a central hub of data collection that organizes and distributes the data to all stakeholders. This enables manufacturing companies to direct and redirect resources to speed up or down. 

Price Optimization

In order to optimize pricing strategies and create accurate cost models, manufacturers need exact timelines and costs. Having an advanced manufacturing analytics platform can help manufacturers determine accurate cycle times to ensure prices are appropriately set. 

Product Development

To remain competitive, manufacturing organizations must invest in research and development (R&D) to build new product lines, improve existing models, and introduce new services. Manufacturing analytics makes it possible for this process to be simulated, rather than using traditional iterative modeling. This reduces R&D costs greatly because real-life conditions can be replicated virtually to predict performance. 

Robotization

Manufacturers are relying more on robotics. As these robots become more intelligent and independent, the data they collect while they execute their duties will increase. This valuable data can be used within a cloud-based manufacturing analytics platform to really control quality at the micro-level. 

Computer Vision Applications

Modern automated quality control harnesses advanced optical devices. These devices can collect information via temperature, optics, and other advanced vision applications (like thermal detection) to precisely control stops.

Fault Prediction and Preventative Maintenance

Using near-real-time data, manufacturers can predict the likelihood of a breakdown – and when it may happen – with confidence. This is much more effective than traditional preventive maintenance programs that are use-based or time-based. Manufacturing analytics’s accuracy to predict when and how a machine will break down allows technicians to perform optimal repairs that reduce overall downtime and increase productivity. 

Warranty Analysis

It’s important to analyze information from failed products to understand how products are withstanding the test of time. With manufacturing analytics, products can be improved or changed to reduce failure and therefore costs. Collecting warranty data can also shed light on the use (and misuse) of products, increase product safety, improve repair procedures, reduce repair times, and improve warranty service. 

Benefits of Manufacturing Analytics

In short, cloud-based manufacturing analytics provides awareness and learnings on a near-real-time basis. For manufacturers to be competitive, contextual awareness is crucial for optimizing product development, quality, and costs. Production equipment generates huge volumes of data, and manufacturing analytics allows manufacturers to leverage this data stream to improve productivity and profitability. Here are the tangible benefits and results of implementing manufacturing analytics:

Full Transparency and Understanding of the Supply Chain

In today’s environment, owning the supply chain has never been more critical. Data analytics can help mitigate the challenges that have cropped up with the current supply chain crisis. For manufacturing businesses, this means having the right number of resources. Data analytics allows manufacturers to remain as lean as possible, which is especially important in today’s global climate. Organizations need to use data analytics to ensure they have the right amount of material and optimize their supply chains during a time when resources are scarce and things are uncertain. 

Reduced Costs

Manufacturing analytics reveals insights that can be used to optimize processes, which leads to cost savings. Predictive maintenance programs decrease downtime and manage parts inventories more intelligently, limiting costs and increasing productivity. Robotics and machine learning reduce labor and the associated costs. 

Increased Revenue

Manufacturers must be dynamic in responding to demand fluctuations. Near-real-time manufacturing analytics allows companies to be responsive to ever-changing demands. At any given time, manufacturing companies have up-to-date insights into inventory, product, and supply chains, allowing them to adjust to demand accordingly in order to maintain delivery times. 

Improved Efficiency Across the Board

The amount of information that product equipment collects enables manufacturers to increase efficiency in a variety of ways. This includes reducing energy consumption, mitigating compliance errors, and controlling the supply chain. 

Greater Customer Satisfaction

At the end of the day, it is important to know what customers want. Data analytics is a crucial tool in collecting data from customer feedback, which can be applied to streamlining the process per the customer’s requirements. Manufacturers can analyze the data collected to determine how to personalize services for their consumers, thereby, increasing customer satisfaction. 

Conclusion

The effects of COVID-19 have shaken up the manufacturing industry. Because of the pandemic’s disruptions, manufacturers are realizing the importance of robust tools – like cloud computing and data analytics – to remain agile, lean, and flexible regardless of external challenges. The benefits that organizations can reap from these technologies go far beyond the horizon of the current supply chain crisis. Leading manufacturers are using data from systems across the organization to increase efficiency, drive innovation, and improve overall performance in any environment.

2nd Watch’s experience managing and optimizing data means we understand industry-specific data and systems. Our manufacturing data analytics solutions and consultants can assist you in building and implementing a strategy that will help your organization modernize, innovate, and outperform the competition. Learn more about our manufacturing solutions and how we can help you gain deep insight into your manufacturing data!

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Cloud Migration Challenges: 6 Reasons the Cloud Might Not be What You Think it Is

A lot of enterprises migrate to the public cloud because they see everyone else doing it. And while you should stay up on the latest and greatest innovations – which often happen in the cloud – you need to be aware of the realities of the cloud and understand different cloud migration strategies. You need to know why you’re moving to the cloud. What’s your goal? And what outcomes are you seeking? Make sure you know what you’re getting your enterprise into before moving forward in your cloud journey.

cloud migration challenges

1. Cloud technology is not a project, it’s a constant

Be aware that while there is a starting point to becoming more cloud native – the migration – there is no stopping point. The migration occurs, but the transformation, development, innovation, and optimization is never over.

There are endless applications and tools to consider, your organization will evolve over time, technology changes regularly, and user preferences change even faster. Fueled by your new operating system, cloud computing puts you into continuous motion. While continuous motion is positive for outcomes, you need to be ready to ride the wave regardless of where it goes. Once you get on, success requires that you stay there.

2. Flex-agility is necessary to survival

Flexibility + agility = flex-agility, and you need it in the cloud. Flex-agility enables enterprises to adapt to the risks and unknowns occurring in the world. The pandemic continues to highlight the need for flex-agility in business. Organizations further along in their cloud journeys were able to quickly establish remote workforces, adjust customer interactions, communicate completely and effectively, and ultimately, continue running. While the pandemic was unprecedented, more commonly, flex-agility is necessary in natural disasters like floods, hurricanes, and tornadoes; after a ransomware or phishing attack; or when an employee’s device is lost, stolen, or destroyed.

3. You still have to move faster than the competition

Gaining or maintaining your competitive edge in the cloud has a lot to do with speed. Whether it’s the dog-eat-dog nature of your industry, macroeconomics, or a political environment, these are the things that speed up innovation. You might not have any control over these things, but they’re shaping the way consumers interact with brands. Again, when you think about how the digital transformation evolved during the pandemic, you saw winning business move the fastest. The cloud is an amazing opportunity to meet all the demands of your environment, but if you’re not looking forward, forecasting trends, and moving faster than the competition, you could fall behind.

4. People are riskier than technology

In many ways, the technology is the easiest part of an enterprise cloud strategy. It’s the people where a lot of risk comes into play. You may have a great strategy with clean processes and tactics, but if the execution is poor, the business can’t succeed. A recent survey revealed that 85% of organizations report deficits in cloud expertise, with the top three areas being cloud platforms, cloud native engineering, and security. While business owners acknowledge the importance of these skills, they’re still struggling to attract the caliber of talent necessary.

In addition to partnering with cloud service experts to ensure a capable team, organizations are also reinventing their technical culture to work more like a startup. This can incentivize the cloud-capable with hybrid work environments, an emphasis on collaboration, use of the agile framework, and fostering innovation.

5. Cost-savings is not the best reason to migrate to the cloud

Buy-in from executives is key for any enterprise transitioning to the cloud. Budget and resources are necessary to continue moving forward, but the business value of a cloud transformation isn’t cost savings. Really, it’s about repurposing dollars to achieve other things. At the end of the day, companies are focused on getting customers, keeping customers, and growing customers, and that’s what the cloud helps to support.

By innovating products and services in a cloud environment, an organization is able to give customers new experiences, sell them new things, and delight them with helpful customer service and a solid user experience. The cloud isn’t a cost center, it’s a business enabler, and that’s what leadership needs to hear.

6. Cloud migration isn’t always the right answer

Many enterprises believe that the process of moving to the cloud will solve all of their problems. Unfortunately, the cloud is just the most popular technology operating system platform today. Sure, it can help you reach your goals with easy-to-use functionality, automated tools, and modern business solutions, but it takes effort to utilize and apply those resources for success.

For most organizations, moving to the cloud is the right answer, but it could be the wrong time. The organization might not know how it wants to utilize cloud functionality. Maybe outcomes haven’t been identified yet, the business strategy doesn’t have buy-in from leadership, or technicians aren’t aware of the potential opportunities. Another issue stalling cloud migration is internal cloud-based expertise. If your technicians aren’t cloud savvy enough to handle all the moving parts, bring on a collaborative cloud advisor to ensure success.

Ready for the next step in your cloud journey?

Cloud Advisory Services at 2nd Watch provide you with the cloud solution experts necessary to reduce complexity and provide impartial guidance throughout migration, implementation, and adoption. Whether you’re just curious about the cloud, or you’re already there, our advanced capabilities support everything from platform selection and cost modeling, to app classification, and migrating workloads from your on-premises data center. Contact us to learn more!

Lisa Culbert, Marketing

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Top 10 Cloud Optimization Best Practices

Cloud optimization is a continuous process specific to a company’s goals, but there are some staple best practices all optimization projects should follow. Here are our top 10.

Top 10 Cloud Optimization Best Practices

1. Begin with the end in mind

Business leaders and stakeholders throughout the organization should know exactly what they’re trying to achieve with a cloud optimization project. Additionally, this goal should be revisited on a regular basis to make sure you remain on track to achievement. Create measures to gauge success at different points and follow the agreed upon order of operations to complete the process.

2. Create structure around governance and responsibility

Overprovisioning is one of the most common issues adding unnecessary costs to your bottom line. Implement specific and regulated structure around governance and responsibility for all teams involved in optimization to control any unnecessary provisioning. Check in regularly to make sure teams are following the structure and you only have the tools you need and are actively using.

3. Get all the Data you Need

Cloud optimization is a data-driven exercise. To be successful, you need insight into a range of data pieces. Not only do you need to identify what data you need and be able to get it, but you also need to know what data you’re missing and figure out how to get it. Collaborate with internal teams to make sure essential data isn’t siloed or already being collected. Additionally, regularly clean and validate data to ensure reliability for data-based decision making.

4. Implement Tagging Practices

To best utilize the data you have, organizing and maintaining it with strict tagging practices in necessary. Implement a system that works from more than just a technical standpoint. You can also use tagging to launch instances, control your auto parking methodology, or in scheduling. Tagging helps you understand the data and see what is driving spend. Whether it’s an environment tag, owner tag, or application tag, tagging provides clarity into spend, which is the         key to optimization.

5. Gain Visibility into Spend

Tagging is one way to see where your spend is going, but it’s not the only way required. Manage accounts regularly to make sure inactive accounts aren’t continuing to be billed. Set up an internal mechanism to review with your app teams and hold them accountable. It can be as simple as a dashboard with tagging grading, as long as it lets the data speak for itself.

6. Hire the Right Technical Expertise

Get more out of your optimization with the right technical expertise on your internal team. Savvy technicians should work alongside the business teams to drive the goals of optimization throughout the process. Without collaboration between these departments, you risk moving in differing directions with multiple end goals in mind. For example, one team might be acting with performance or a technical aspect in mind without realizing the implication on optimization. Partnering with optimization experts can also keep teams aligned and moving toward the same goal.

7. Select the Right Tools and Stick with Them

Tools are a part of the optimization process, but they can’t solve problems alone. Additionally, there are an abundance of tools to choose from, many of which have similar functionality and outcomes. Find the right tools for your goals, facilitate adoption, and give them the time and data necessary to produce results. Don’t get distracted by every new, shiny tool available and the “tool champions” fighting for one over another. Avoid the costs of overprovisioning by checking usage regularly and maintaining the governance structure established throughout your teams.

8. Make sure your Tools are Working.

Never assume a tool or a process you’ve put in place is working. In fact, it’s better to assume it’s not working and consistently check its efficiency. This regular practice of confirming the tools you have are both useful and being used will help you avoid overprovisioning and unnecessary spending. For tools to be effective and serve their purpose, you need enough visibility to determine how the tool is contributing to your overall end goal.

9. Empower Someone to Drive the Process.

The number one call to action for anyone diving into optimization is to appoint a leader. Without someone specific, qualified, and active in managing the project with each stakeholder and team involved, you won’t accomplish your goals. Empower this leader internally to gain the respect and attention necessary for employees to understand the importance of continuous optimization and contribute on their part.

10. Partner with Experts.

Finding the right partner to help you optimize efficiently and effectively will make the process easier at every turn. Bringing in an external driver who has the know-how and experience to consult on strategy through implementation, management, and replication is a smart move with fast results.

2nd Watch takes a holistic approach to cloud optimization with a team of experienced data scientists and architects who help you maximize performance and returns on your cloud assets. Are you ready to start saving? Let us help you define your optimization strategy to meet your business needs and maximize your results. Contact Us to take the next step in your cloud journey.

-Willy Sennott, Optimization Practice Manager

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Steps to Continuous Cloud Optimization

Cloud optimization is an ongoing task for any organization driven by data. If you don’t believe you need to optimize, or you’re already optimized, you may not have the data necessary to see where you’re over-provisioned and losing spend. Revisit the optimization pillars frequently to best evolve with and take advantage of everything the cloud has to offer.

Begin with the end in mind

The big question is, where are you trying to go? This question should constantly be revisited with internal stakeholders and business leaders. Define the process that will get you there and follow the order of operations identified to reach your optimization goal. Losing sight of the purpose, getting caught up in shiny new tools, or failing to incorporate the right teams could lead you off path.

Empower someone to drive the process

This is pivotal because without this appointed person, cloud optimization will not happen. Give someone the power to drive optimization policies throughout the organization. Companies most successful in achieving optimization have a good internal mandate to make it a priority. When messages come from the top, and are enforced through a project champion, people tend to pay attention and management is much more effective.

Fill the data gaps

Cloud optimization is a data driven exercise, so you need all the data you can get to make it valuable. Your tools will be much more compelling when they have the data necessary to make smart recommendations. Understand where to get the data in your organization, and figure out how to get any data you don’t have. Verify your data regularly to confirm accuracy for intelligent decision making geared toward optimization.

Implement tagging practices

The practice of not only implementing, but also actively enforcing your tagging policies, drives optimization. Be it an environment tag, owner tag, or application tag, tags help you understand your data and what or who is driving spend.

Enforce accountability

While lack of tagging and data gaps prevent visibility, overprovisioning is also an accountability issue. Just look at the hundred plus AWS services alone that show up on a bill for an organization that’s a long-time user. It’s not uncommon for 20-30% of the total to be attributed to services they never even knew existed at the time they migrated to the cloud.

Hold your app teams accountable with an internal mechanism that lets the data speak for itself. It can be as simple as a dashboard with tagging grading, because everybody understands those results.

Rearchitect and refactor

Migrating to the cloud via a lift and shift can be a valuable strategy for certain organizations. However, after a few months in the cloud, you need to intentionally move forward with the next steps. Reevaluating, refactoring and rearchitecting will occur multiple times along the way. Without them, you end up spending more money than necessary.

Continuous optimization is a must

Optimization is not a one and done project because the possibilities are constantly evolving. Almost every day, a new technology is introduced. Maybe it’s a new instance family or tool. A couple years ago it was containers, and before that it was serverless. Being aware of these new and improved technologies is key to maintaining continuous optimization.

Engage with an experienced partner

There are a lot of factors to consider, evaluate, and complete as part of your cloud optimization practice. To maximize your optimization efforts, you want someone experienced to guide your strategy.

One benefit to partnering with an optimization expert, like 2nd Watch, is that an external partner can diffuse the internal conflicts typically associated with optimization. So much of the process is navigating internal politics and red tape. A partner helps meld the multiple layers of your business with a holistic approach that ensures your cloud is running as efficiently as possible.

-Willy Sennott, Optimization Practice Manager

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Cloud Optimization: Top 5 Challenges and Why Tools Can’t Solve Them

Optimizing your cloud is essential for maximizing budgets, centralizing business units, making informed decisions, and driving performance. Regardless of whether you’re already in the cloud or you’re just beginning to consider migrating, you need to be aware of the challenges to optimization in order to avoid or overcome them and reach your optimization goals.

1. Complexity

The most pervasive challenge of optimization in the cloud is the complexity of the task. Regardless of the cloud platform – AWS, Azure, Google Cloud, or a hybrid cloud strategy – the intricacies are constantly evolving and changing. Trying to stay on top of that as an individual business requires a good amount of time, resources, and effort. Adding new tools and processes to your cloud requires integration, stakeholder agreement, data mining, analysis, and maintenance. While the potential outcomes from optimization are business-changing, it’s an ongoing process with many moving parts.

2. Governance

Standardized governance frameworks bring decentralized business units and disparate stakeholders together to accomplish business-wide objectives. Shared responsibility, from central IT to individual app teams, prevents the costly consequences of overprovisioning.  While many organizations are knowingly overprovisioned, they can’t seem to solve the problem. Part of the issue is simply a lack of overall governance.

3. Data

Cloud optimization is a data driven exercise. If it’s not data driven, it’s not scalable. You need to maximize your data by knowing what data you have, where it is, and how to access it. Also important is knowing what data is missing. Many organizations believe they have complete metrics, but they’re not capturing and monitoring memory, which is a huge piece of the puzzle. In fact, memory is one of the most constrained points of data across organizations.

4. Visibility

Incredibly important within data discovery and data mapping is gaining visibility through tagging. Without an enforced and uniform tagging strategy as part of your governance structure, spend can increase without accounting for it. Tags provide insight into your cloud economics, letting you know who is spending what, what are they spending it on, and how much are they spending. It’s not uncommon to see larger organizations with a number of individual linked accounts and no one knows who they belong to. We’ve even found, after some digging, that the owners of those accounts haven’t been with the company for months! To get the cost saving benefits from cloud optimization, you need visibility throughout the process.

5. Technical expertise

You need a certain level of technical expertise and intuition to take advantage of all the ways you can optimize your cloud. Too often, techs aren’t necessarily thinking about optimization, but rather make decisions based on other performance or technical aspects. Without optimization at the forefront of these deterministic behaviors, the business drivers may not perform as expected. Partner with data scientists and architects to map connections between data, workloads, resources, financial mechanisms, and your cloud optimization goals.

Tools are part of the solution, but not the entire solution.

While tools can help with your cloud optimization process, they can’t solve these common challenges alone. Tools just don’t have the capability to solve your data gaps. In fact, one foundational issue with tools is the specific algorithms used to generate recommendations. Regardless of whether or not the tool has complete data, it will still make the same recommendations, thereby creating confusion and introducing new risks.

It takes work to get the best results. Someone has to first be able to deduce the information provided by your tools, then put it into context for the various decision makers and stakeholders, and finally, your application owners and businesses teams have to architect the optimization correctly to be able to take advantage of the savings.

In choosing the right tools to aid your optimization, be aware of ‘tool champions’ who create internal noise around decision making. New tools are launched almost daily, and different stakeholders are going to champion different tools.

Once you find a tool, stick with it. Give it a chance to fully integrate with your cloud, provide training, and encourage adoption for best results. The longer it’s a part of your infrastructure, the more it will be able to aid in optimization.

2nd Watch takes a holistic approach to cloud optimization from strategy and planning, to cost optimization, forecasting, modeling and analytics. Download our eBook to learn more about adopting a holistic approach to cloud cost optimization.

-Willy Sennott, Optimization Practice Manager

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