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Weaving Together Siloed Business Functions with Digital Thread

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Click to learn more about author Keith Higgins.

Over 60% of manufacturers are planning to increase their investments in smart factory initiatives over the next few years. That ratio will undoubtedly increase as existing digital transformation projects continue to scale up post-pandemic and new cutting-edge enterprise technology solutions emerge over the next few years. 

When looking at the different functions across an industrial enterprise today, including product and machine design, production engineering, plant operations, and supply chain, you will find many legacy software applications where data is locked up. For manufacturers, data silos typically build up in systems used for day-to-day planning and operations, including enterprise resource planning (ERP), electronic manufacturing services (EMS), manufacturing execution systems (MES), manufacturing operations management (MOM), or other software systems and tools.

When these various enterprise systems are not integrated across an organization, manufacturers may miss critical insights that could improve factory operations. As Sujeet Chand, our company’s CTO, highlighted at our recent Automation Fair at Home event, “It is critical that organizations tie these siloed functions together digitally to get operational value from their digital transformation initiatives.”

Digital Thread: Tying Enterprises Together

A digital enterprise connects assets, systems, and processes across an organization. However, optimizing digital transformation initiatives requires a way to visualize how those components work together as data flows through an industrial ecosystem. The digital thread offers a detailed, virtual perspective of information flows – production performance data (e.g., speed, downtime), product specification data (e.g., quality parameters, system rejects), supply chain data (e.g., inventory levels, late deliveries, quality), etc. Basically, a digital thread is the trail of information collected throughout the lifecycle of a product, asset, system, or process. This digital record provides the entire value chain with universal access to unified digital data captured during design, virtual simulation, and physical operations. Executives and leaders can leverage the digital thread to help to evaluate and improve value streams in real time.  

Digital mirroring of your plant and information flow can provide critical insights for better daily management. Here are three different ways digital thread improves business value:

1. Accelerate Innovation

Success in manufacturing has always been tied to capacity to innovate quickly. Digital thread enables accelerated innovation through real-time collaboration. Manufacturers no longer need to email design files across their organization, accepting that there will inevitably be version control issues. Machine builder engineers, who are designing the machine, and field engineers on site establish a single source of truth by exchanging information in real time. Organizations can remove dependencies by bringing every stakeholder together in real time from the start. They can kick off product design, machine design, and production engineering simultaneously to reduce timelines and evaluate design manufacturability.

2. Optimize Operations

The key to optimizing operations is connecting the plant to the digital content created by other business functions. Digital thread can be used to commission new production lines virtually – enabling manufacturers to decrease time to market and protect investments. Rather than waiting to commission manufacturing lines until the machinery is bolted to the plant floor, manufacturers can ensure that operations will run smoothly by validating manufacturing processes and debugging programmable logic controller (PLC) code via a digital twin

3. Maximize Workforce Productivity

Workforce productivity is top-of-mind for many organizations, with 58% of the workforce approaching retirement and processes that are becoming increasingly complex. With digital thread, organizations can empower their workforce to reach their potential with high-fidelity training and on-the-job instruction. With access to hands-free, step-by-step directions, new personnel are equipped to deliver a first-time fix their first day on the production floor. From veterans to new hires, digital thread connects business systems together to enable the creation of a historical record of all trainings and on-the-job instructions needed to keep production running smoothly. Virtual reality and augmented reality can shorten training time by as much as 75% by enabling low-risk, high-fidelity environment training and real-time equipment instruction for new or retrained employees.

For example, our company helped a global manufacturer implement a digital thread alongside MES at most of its 20 manufacturing locations, resulting in a 50% lead time reduction to customers, a 50% reduction in defective parts, and a 4% improvement in productivity. This performance boost increased both customer satisfaction and market share.

The pace of innovation is faster than ever. To stay ahead of the competition, organizations must maximize workforce productivity and optimize operations by delivering information to the appropriate person or system at the right time. Manufacturers seeking to realize their full digital potential and reap the highest ROI when it comes to digital transformation must futureproof their digitization investments and rethink the way they manage operations by leveraging digital thread. 

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.dataversity.net/weaving-together-siloed-business-functions-with-digital-thread/

Big Data

Pandas vs SQL: When Data Scientists Should Use Each Tool

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Pandas vs SQL: When Data Scientists Should Use Each Tool

Exploring data sets and understanding its structure, content, and relationships is a routine and core process for any Data Scientist. Multiple tools exist for performing such analysis, and we take a deep dive into the benefits and different approaches of two important tools, SQL and Pandas.


By Matthew Przybyla, Senior Data Scientist at Favor Delivery.

Photo by rigel on Unsplash.

Both of these tools are important to not only data scientists but also to those in similar positions like data analytics and business intelligence. With that being said, when should data scientists specifically use pandas over SQL and vice versa? In some situations, you can get away with just using SQL, and some other times, pandas is much easier to use, especially for data scientists who focus on research in a Jupyter Notebook setting. Below, I will discuss when you should use SQL and when you should use pandas. Keep in mind that both of these tools have specific use cases, but there are many times where their functionality overlap, and that is what I will be comparing below as well.

Pandas

Photo by Kalen Kemp on Unsplash.

Pandas is an open-source data analysis tool in the Python programing language. The benefit of pandas starts when you already have your main dataset, usually from a SQL query. This main difference can mean that the two tools are separate. However, you can also perform several of the same functions in each respective tool. For example, you can create new features from existing columns in pandas, perhaps easier and faster than in SQL.

It is important to note that I am not comparing what pandas does that SQL cannot do and vice versa. I will be picking the tool that can do the function more efficiently or preferable for data science work — in my opinion, from personal experience.

Here are times where using pandas is more beneficial than SQL — while also having the same functionality as SQL:

  • creating calculated fields from existing features

When incorporating a more complex SQL query, you often are incorporating subqueries as well in order to divide values from different columns. In pandas, you can simply divide features much easier like the following:

df["new_column"] = df["first_column"]/df["second_column"] 

The code above is showing how you can divide two separate columns and assign those values to a new column. In this case, you are performing the feature creation on the whole entire dataset or dataframe. You can use this function in both feature exploration and feature engineering in the process of data science.

  • grouping by

Also referring to subqueries, grouping by in SQL can become quite complex and require lines and lines of code that can be visually overwhelming. In pandas, you can simply group by one line of code. I am not referring to the ‘group by’ at the end of a simple select from table query, but one where there are multiple subqueries involved.

df.groupby(by="first_column").mean() 

This result would be returning the mean of the first_column for every column in the dataframe. There are many other ways to use this grouping function, which are outlined nicely in the pandas documentation.

  • checking data types

In SQL, you will often have to cast types, but sometimes it can be a little clearer to see the way pandas lays out data types in a vertical format rather than scrolling through a horizontal output in SQL. You can expect some examples of data types returned to be int64, float64, datetime64[ns], and object.

df.dtypes 

While these are all fairly simple functions of pandas and SQL, in SQL, they are particularly tricky and sometimes just much easier to implement in a pandas dataframe. Now, let’s look at what SQL is better at performing.

SQL

Photo by Caspar Camille Rubin on Unsplash.

SQL is probably the language that is used most by the most amount of different positions. For example, a data engineer could use SQL, a Tableau developer, or a product manager. With that being said, data scientists tend to use SQL frequently. It is important to note that there are several different versions of SQL, usually all having a similar function, just slightly formatted differently.

Here are times where using SQL is more beneficial than pandas — while also having the same functionality as pandas:

  • WHERE clause

This clause in SQL is used frequently and can also be performed in pandas. In pandas, however, it is slightly more difficult or less intuitive. For example, you have to write out redundant code, whereas, in SQL, you simply need the WHERE.

SELECT id
FROM table WHERE id > 100 

In pandas, it would be something like:

df[df["id"] > 100]["id"] 

Yes, both are simple, but SQL is just a little more intuitive.

  • JOINS

Pandas has a few ways to join, which can be a little overwhelming, whereas in SQL, you can perform simple joins like the following: INNER, LEFT, RIGHT.

SELECT one.column_A, two.column_B
FROM first_table one INNER JOIN second_table two ON two.id = one.id 

In this code, joining is slightly easier to read than in pandas, where you have to merge dataframes, and especially as you merge more than two dataframes, it can be quite complex in pandas. SQL can perform multiple joins, whether it be INNER, etc., all in the same query.

All of these examples, whether it be SQL or pandas, can be used in at least the exploratory data analysis portion of the data science process, as well as in feature engineering, and querying model results once they are stored in a database.

Summary

This comparison of pandas versus SQL is more of a personal preference. With that being said, you may feel the opposite of my opinion. However, I hope it still sheds light on the differences between pandas and SQL, as well as what you can perform the same in both tools, using slightly different coding techniques and a different language altogether.

To summarize, we have compared the benefits of using pandas over SQL and vice versa for a few of their shared functions:

  •  creating calculated fields from existing features
  • grouping by
  • checking data types
  • WHERE clause
  • JOINS

Original. Reposted with permission.

Related:

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Source: https://www.kdnuggets.com/2021/06/pandas-vs-sql.html

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Big Data

Evaluate Your Model – Metrics for Image Classification and Detection

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Source: https://www.analyticsvidhya.com/blog/2021/06/evaluate-your-model-metrics-for-image-classification-and-detection/

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Build Web App instantly for Machine Learning using Streamlit

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Source: https://www.analyticsvidhya.com/blog/2021/06/build-web-app-instantly-for-machine-learning-using-streamlit/

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Artificial Intelligence

Automation Is Changing the Workplace: 7 Tips to Help You Adapt

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We have already started using a wide array of tools to make our professional lives simpler. From video conferencing software, to project management tools for collaboration, businesses are rapidly embracing innovative opportunities in technology.

What’s more, automation is becoming the new norm. To help your employees focus on their core responsibilities, you have likely already implemented various automated solutions. You might have started using chatbots to alleviate some of your support team’s workload. Then again, perhaps you have started using automatic reporting tools connected to your analytics software.

With remote work on the rise, too, businesses are looking for even more automation opportunities to simplify their workflows. In case you’re among them, this list can help you find the most appropriate, effective solutions. After all, automation helps eliminate or minimize risk and human error, it frees up your time, and helps your employees avoid burnout. It’s a win-win, no matter how you look at it.

The only potential problem is adoption, since you will need time to implement the tools and train your staff. In the end, it will be worth your while. Without further ado, here are the core automation opportunities for the upcoming period that your business will benefit from.

Employee training for successful automation

The first step is preparing for automation deals with the human element of the process: your employees. Behind every great software solution, you have a team of people managing, updating, and securing its performance at every turn.

That is why before you implement a robust automation solution, you need to train your staff. They need to know how and to what extent they can rely on the tools at hand. If automation refers to processes as well as tools, all the more reason to regularly re-train them, too.

Consistency in contingent workforce management

Remote and contingent workers in the US have become a matter of great relevance in the past few years. That said, managing them has also become an issue that some companies still aren’t certain how to handle.

For companies without internal resources to handle such a process, contingent workforce solutions in the USA are rapidly becoming a necessity. These solutions help automate the onboarding and offboarding of temporary workers in your business and ensure legal compliance. Automating these intricate processes helps reduce costs, avoid fees, and allows cultural fit for all hiring in and outside of the country.

Marketing and social media automation

No matter your industry, your business cannot survive without a strong marketing strategy. Nowadays, companies that fail to regulate their marketing output risk compromising their reputation. Failing to respond to a review, a comment, an email, or help a customer can all backfire.

Automating your marketing processes, such as social media scheduling and email campaigns can take so much pressure off your team. AI-driven tools for social media help your team filter out relevant data and use it to improve your future campaigns, too. Automated reporting helps prevent wasting hours of their time on sifting through data, so that they can focus on creative output instead.

Leveraging data and analytics with automation

Data analytics in and out of your marketing department can and should be automated. Collecting and analyzing data from all sectors of your business helps you keep your business performance in check. Instead of putting such a complex and error-prone process on your employees’ shoulders, automation can save the day.

Nowadays, advanced analytics tools with the ability to continuously collect data help companies improve all processes. You can spot extraneous expenses and cut them, the least productive team members and help them, and notice market opportunities for your brand.

Minimizing risk and error in finance management

Perhaps the most sensitive of all departments, your finance sector is prone to issues primarily when your employees cause them. It’s only natural, since these intricate mathematical and statistical processes are filled with repetitive tasks.

Fortunately, most US companies now use automatic accounting solutions that help automate recurring invoices, salary payments, and the like financial data integrations. It’s a good habit that has also spread to legal teams, who use a range of legal software to automate key processes.

Improving customer service with chatbots

For most businesses dealing with growth, handling swarms of customers seems both like a daunting challenge and an immense opportunity. In that growth, you cannot expect employees in your customer service department to handle every single request or query.

The beauty of utilizing chatbots is that you can automate a wide array of customer service processes without any harm to your business. Actually, you can expect an improvement in the overall customer experience as a result. Chatbots respond without delays, provide accurate information, and have no bias. That makes them the perfect vessel to interact with disgruntled as well as curious customers.

Employee engagement and satisfaction

Before you embrace another (or your very first) automation solution, make sure to conduct a thorough performance analysis. This data will serve as the basis for comparison once you’re ready to introduce novelty through automation. Pay special attention to how your teams feel about their work before and after using new automation options.

Knowing that over 40% of employees spend most of their time on menial, repetitive tasks, you can imagine how automation can help. The same research shows that almost 90% of employees think that automation makes them more efficient. Take menial, repetitive, mind-numbing tasks off their hands, and your workers will be much more satisfied and engaged.

The overall purpose of your business doesn’t change when you embrace automation. On the contrary, with it, you have more time and other resources to focus on what matters most. Automation tools aren’t here to replace the human impact within your business, they are here to amplify it. Make sure to consider the listed opportunities for your own industry and business, and you will allow your business to grow. 

Image Credit: Freepik

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Source: https://datafloq.com/read/automation-is-changing-workplace-7-tips-help-you-adapt/15647

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