2023.06.02 13:22 groovin_gal Rescue Dog (Basset Hound) Behavioral Change
2023.06.02 13:22 AplusDijital The Benefits of Choosing MSI Granite for Your Home
When it comes to enhancing the aesthetics and functionality of your home, selecting the right materials is crucial. One such material that stands out for its durability, versatility, and elegance is MSI Granite. With its numerous advantages and wide range of options, MSI Granite is an excellent choice for homeowners looking to add value and style to their living spaces. In this article, we will explore the various benefits of choosing MSI Granite for your home.submitted by AplusDijital to u/AplusDijital [link] [comments]
1. IntroductionIntroduce the topic and set the context for the article, highlighting the importance of selecting the right materials for home improvement projects.
2. What is MSI Granite?Provide a brief overview of MSI Granite, explaining its composition, characteristics, and how it differs from other types of granite.
3. Durability and StrengthDiscuss the exceptional durability and strength of MSI Granite, emphasizing its ability to withstand daily wear and tear, making it ideal for high-traffic areas in your home.
4. Variety of Colors and PatternsHighlight the extensive range of colors and patterns available in MSI Granite, showcasing how it can complement various design styles and personal preferences.
5. Easy MaintenanceExplain how MSI Granite requires minimal maintenance, making it a practical choice for busy homeowners. Discuss the cleaning and care tips to keep the granite surfaces looking pristine.
6. Heat and Scratch ResistanceEmphasize the heat and scratch resistance properties of MSI Granite, assuring homeowners that their countertops and surfaces can withstand hot pots, pans, and sharp objects without damage.
7. Resistant to StainsHighlight how MSI Granite is highly resistant to stains, making it an excellent option for kitchens and bathrooms where spills and liquids are common.
8. Value Addition to Your HomeExplain how installing MSI Granite can increase the value of your home, as it is considered a premium material that adds a touch of luxury and sophistication.
9. Eco-Friendly OptionDiscuss the eco-friendly nature of MSI Granite, highlighting its sustainable sourcing practices and recyclability, making it an environmentally conscious choice.
10. Versatility in DesignDescribe the versatility of MSI Granite in terms of customization options, such as edge profiles, finishes, and shapes, allowing homeowners to create unique and personalized designs.
11. Applications in Different Areas of Your HomeExplore the various applications of MSI Granite throughout different areas of the home, including kitchen countertops, bathroom vanities, fireplace surrounds, and outdoor spaces.
12. Professional InstallationEmphasize the importance of professional installation for MSI Granite to ensure a flawless and long-lasting result. Discuss the expertise and skills required for proper installation.
13. Cost-EffectivenessHighlight the cost-effectiveness of MSI Granite in the long run, as its durability and low maintenance requirements eliminate the need for frequent repairs or replacements.
14. Longevity and Timeless BeautyDiscuss how MSI Granite offers long-term value, both in terms of its durability and timeless aesthetic appeal that never goes out of style.
15. ConclusionSummarize the main points discussed in the article, reiterating the benefits of choosing MSI Granite for your home and encouraging readers to consider it for their upcoming projects.
2023.06.02 13:21 firefighter6436 My wife is into free use. Anyone have any kinky fun ideas?
2023.06.02 13:21 Unitedinnovator Decoding Research Data Analysis: A Pivotal Tool For Business Decision-Making
Research Data Analysis: the term might seem complex, but its essence is integral to business success. Essentially, it involves inspecting, cleaning, altering, and modeling data to uncover useful insights, draw conclusions, and support decision-making. For businesses, it provides a strategic lens to view operations, trends, patterns, and potential market opportunities. Let's delve deeper to understand its significance and the role it plays in business decision-making.submitted by Unitedinnovator to u/Unitedinnovator [link] [comments]
● Understanding Research Data Analysis○ Data analysis is the science of extracting meaningful insights from raw data.
○ It involves various statistical, computational, and analytical methods applied to data sets to reveal patterns, anomalies, and relationships.
○ The ultimate aim is to derive actionable insights that can inform business decisions and strategies.
● Importance Of Research Data Analysis In Business○ In today's data-driven world, businesses generate and have access to vast amounts of data.
○ However, this data is only as valuable as the valuable insights that can be extracted from it.
○ Research data analysis helps businesses, to -
■ Understand Customer Behavior
● Through analysis, businesses can understand their customer's preferences, buying habits, and needs, enabling them to tailor their offerings accordingly.
■ Improve Operational Efficiency
● Data analysis can identify bottlenecks in business operations and suggest improvements, leading to cost savings and increased productivity.
■ Drive Innovation
● By analyzing market trends and consumer needs, businesses can uncover opportunities for new products or services.
■ Manage Risk
● Data analysis can help identify potential risks and develop strategies to mitigate them.
■ Make Informed Decisions
● Data-backed decisions tend to be more accurate and effective, leading to better business outcomes.
● Role Of Data Analysis In Business Decision Making○ In business decision-making, data analysis acts as a compass, guiding businesses through the vast sea of information toward their strategic objectives.
○ It helps in -
■ Setting Goals
Data analysis provides evidence-based insights to set realistic and measurable goals.
■ Formulating Strategies
By revealing trends, patterns, and relationships, data analysis helps businesses devise effective strategies.
■ Performance Tracking
Data analysis helps monitor key performance indicators (KPIs), providing a clear view of whether the business is on the right path to meeting its goals.
■ Risk Management
Through predictive analysis, businesses can anticipate potential issues and devise strategies to manage risks.
■ Resource Allocation
Data analysis can help determine the most efficient way to distribute resources, optimizing productivity and profitability.
● The role of research data analysis services in business decision-making is pivotal.
● It turns raw data into valuable insights, guiding strategic decisions and propelling business growth.
● In the modern business landscape, where data is abundant but actionable insights are scarce, mastering data analysis can provide a significant competitive edge.
● It isn't solely about collecting data; it's also about making the data work for you.
● By effectively analyzing data, businesses can navigate the complexities of their operations and the market landscape, steering their way toward success.
Navigating The Diverse Landscape Of Research Data Analysis Types - A Guide For Businesses
● In the realm of business decision-making, research data analysis stands as a compass, guiding businesses through the myriad complexities of data toward valuable insights.
● However, to harness its full potential, it is crucial to understand that not all data analysis is the same.
● Different types of analysis serve different purposes and yield distinct insights.
● Detailed below are the varied types of research data analysis and their respective value to businesses.
● Descriptive Analysis - The What○ Descriptive analysis is the simplest form of data analysis. It answers the question "What happened?".
○ By summarizing past data, it provides a clear understanding of the events that have occurred.
○ Businesses use descriptive analysis to assess past performance and analyze historical trends.
○ Descriptive analysis includes measures like averages, percentages, and frequency counts.
○ Graphical representation of data such as bar graphs, pie charts, and histograms are common tools in descriptive analysis.
For example, businesses like Ph.D. thesis writing online providers use data analysis services to gain insight into the market.
● Inferential Analysis - The Why○ The inferential analysis takes a step beyond the 'what' to explore the 'why'.
○ It involves drawing conclusions from the data that extend beyond the immediate dataset.
○ In other words, inferential analysis enables businesses to make data-backed assumptions and predictions about larger populations based on a smaller sample.
○ The inferential analysis is particularly useful in market research, where it can provide insights into customer behavior, preferences, and attitudes.
○ For instance, businesses can analyze survey data to draw conclusions about their target market's preferences.
● Predictive Analysis - The Future○ Predictive analysis ventures into the realm of prediction, answering the question of "what could happen?".
○ By leveraging statistical algorithms and machine learning techniques, it extrapolates from existing data to forecast future trends or behaviors.
○ Predictive analysis can help businesses anticipate future sales, customer churn, market trends, and more.
○ This forward-looking approach aids in proactive decision-making and risk mitigation.
● Prescriptive Analysis - The Action○ Prescriptive analysis is the pinnacle of data analysis, addressing the question of "what should we do?".
○ It not only forecasts future outcomes but also suggests actions to benefit from these predictions.
○ Prescriptive analysis is used in various business contexts, such as determining optimal pricing strategies, improving supply chain efficiency, and enhancing customer experience.
○ This type of analysis is often powered by complex algorithms, simulation, and machine learning.
● Research data analysis is not a one-size-fits-all solution; instead, it encompasses a diverse array of methods, each suited to different business objectives.
● From understanding the past with descriptive analysis to shaping the future with prescriptive analysis, each type offers unique value.
● In the dynamic business landscape, understanding these different types of research data analysis can empower businesses to extract maximum value from their data.
● By tailoring the type of analysis to their specific needs and objectives, businesses can turn raw data into a potent strategic tool, fueling informed decisions, optimizing operations, and driving growth.
Research Data Analysis
Unfolding The Steps Of Research Data Analysis - A Comprehensive Guide For Businesses
Research data analysis plays a significant role in any business operation. It provides a clearer understanding of the business landscape, reveals patterns and trends, and informs strategic decision-making. However, deriving meaningful insights from data is not an instantaneous process; it's a journey that comprises several critical steps. Detailed below is an in-depth exploration of these steps, from data collection to interpretation.
● Step 1
Data Collection○ The journey of data analysis begins with data collection.
○ It involves gathering relevant information that can answer business questions or support decision-making.
○ Data can be gathered from numerous sources, such as transaction records, customer surveys, social media, or external research reports.
● Step 2
Data Cleaning○ Once data is gathered, the subsequent step is data cleaning, also known as data preprocessing.
○ This step aims to identify and rectify errors and inconsistencies in the data, such as duplicate entries, missing values, or incorrect formatting.
○ Clean data is crucial for ensuring the accuracy and reliability of the subsequent analysis.
● Step 3
Data Transformation○ After cleaning, the data is transformed into a suitable format for analysis.
○ This process can include activities like normalization (scaling data to a standard range), discretization (converting continuous variables into discrete ones), or encoding (converting categorical data into numerical format).
● Step 4
Data Analysis○ This is the core stage where the actual analysis occurs.
○ Depending on the business objectives, different analytical techniques may be employed, including -
■ descriptive analysis (understanding what happened),
■ inferential analysis (exploring why something happened),
■ predictive analysis (forecasting future outcomes), and
■ prescriptive analysis (suggesting optimal actions).
● Step 5
Data Interpretation○ Data interpretation involves making sense of the results of the analysis.
○ This stage is about translating the technical findings into meaningful information that can advise business decisions.
○ For example, an increase in sales might be interpreted as a positive response to a recent marketing campaign.
● Step 6
Data Visualization○ Data visualization aids in understanding complex data by representing it in a graphical form.
○ Charts, graphs, and infographics make it easier to spot trends, compare variables, and comprehend the story that the data is telling.
● Step 7
Reporting & Decision-Making○ The final step is to communicate the findings and insights to the relevant stakeholders through reports or presentations.
○ The insights derived from data analysis should inform decision-making, guiding the business toward its strategic objectives.
● Research data analysis is a systematic process that demands meticulous execution at each step.
● It involves transforming raw data into actionable insights through a journey of collection, cleaning, transformation, analysis, interpretation, visualization, and reporting.
● Businesses that understand and adeptly navigate this process stand to gain a substantial competitive edge by unlocking the potential of data to inform strategies, optimize operations, and drive growth.
● It's not just about having data; it's about understanding it, and that's where the magic of data analysis lies.
Harnessing The Power Of Research Data Analysis In Business Operations
In today's dynamic business environment, the difference between success and failure often hinges on the ability to make data-driven decisions. More than ever, businesses need to understand their markets, customers, and internal processes, and research data analysis provides the necessary tools for such understanding. This article explores how businesses can utilize data analysis in various aspects of their operations, including marketing, sales, supply chain, and human resources.
● Data Analysis In Marketing○ Effective marketing begins with understanding the target audience, and data analysis offers invaluable insights into customer behavior, preferences, and trends.
○ By analyzing customer data, businesses can identify successful marketing strategies, segment their audience for targeted campaigns, and predict future marketing trends.
○ Moreover, through sentiment analysis, businesses can gauge customer reactions to marketing campaigns and adjust their strategies accordingly.
● Data Analysis In Sales○ Sales data analysis can be a gold mine of insights for a business.
○ By scrutinizing sales data, businesses can identify best-selling products, peak sales periods, and the effectiveness of sales strategies.
○ Furthermore, predictive analysis can forecast future sales trends, enabling proactive planning and inventory management.
● Data Analysis In Supply Chain Management○ Supply chains generate a wealth of data, from procurement and production to distribution and customer service.
○ Analyzing this data can help businesses optimize their supply chain operations, diminishing costs, and elevating efficiency.
○ For instance, data analysis can help identify bottlenecks in the supply chain, predict demand and supply trends, and streamline logistics and inventory management.
● Data Analysis In Human Resources○ Data analysis is increasingly finding application in human resources (HR) management.
○ HR analytics involves analyzing employee data to inform HR policies and strategies.
○ For instance, data analysis can provide insights into employee performance, retention, and satisfaction.
○ These insights can guide recruitment strategies, employee development programs, and workplace policies, contributing to a more productive and satisfied workforce.
● In summary, research data analysis is a potent tool for businesses, providing insights that inform decision-making across various operational domains.
● Whether it's understanding customer preferences, forecasting sales, optimizing supply chains, or enhancing human resource management, data analysis is key to unlocking operational efficiency and strategic advantage.
● In the competitive world of business, data-backed decisions are the currency of success.
● By effectively leveraging research data analysis, businesses can ensure they are making decisions based on facts, not just hunches, positioning them for sustained success in their respective markets.
Tackling Challenges In Research Data Analysis - A Strategic Guide For Businesses
Research data analysis has emerged as a cornerstone of successful business decision-making. By providing deep insights into market trends, customer behavior, and operational efficiency, research data analysis can give a competitive advantage to companies like United Innovator. However, like any powerful tool, its implementation is not without challenges. This article sheds light on some of these common hurdles and offers practical solutions for businesses to overcome them.
● Data Privacy Concerns○ With the surge in data collection comes increased concern over privacy.
○ Businesses must ensure they are compliant with privacy laws such as GDPR and CCPA when handling personal data.
○ In order to overcome this challenge, businesses can adopt robust data governance practices, anonymize personal data, and ensure transparency about data usage with their customers.
● Ensuring Data Quality○ Poor quality data can lead to inaccurate insights and misguided decisions.
○ It's imperative for businesses to ensure the data they analyze is accurate, complete, and up-to-date.
○ Regular data cleaning and validation can help maintain data quality.
○ Moreover, deploying data quality management tools can automate the process, reducing errors and improving accuracy.
● Lack Of Skilled Personnel○ Data analysis requires specialized skills and knowledge, which many businesses may lack.
○ This shortage can be addressed by investing in training existing employees or hiring skilled data analysts.
○ Collaborating with external data analytics consultants is another viable option, particularly for small businesses.
● Managing Large Volumes of Data○ With the rise of big data, managing the sheer volume of information can be overwhelming.
○ However, advancements in data storage solutions, such as cloud computing and data warehousing, can help manage and organize large data sets.
○ In addition, tools like Hadoop and Spark can process big data efficiently, making it easier to analyze.
● Integrating Data From Various Sources○ Businesses often collect data from numerous sources, leading to disparate data types and formats.
○ Integrating this data for analysis can be challenging.
○ However, data integration tools can help combine data from different sources into a unified view.
○ Implementing a data warehouse can also facilitate the integration process.
● Making Data Analysis Actionable○ Translating data insights into actionable decisions is a key challenge.
○ It's not enough to collect and analyze data; businesses need to interpret and apply these insights effectively.
○ Creating a culture of data literacy within the organization can help bridge this gap.
○ Also, visualizing data using tools like Tableau or Power BI can make insights more understandable and actionable.
● While challenges in research data analysis are undeniable, they are not insurmountable.
● With the right strategies, businesses can navigate these obstacles and harness the power of data analysis.
● By addressing data privacy concerns, ensuring data quality, developing skilled personnel, managing large data volumes, integrating various data sources, and making data actionable, businesses can unlock the full potential of data analysis.
● In doing so, they transform these challenges into opportunities, driving their growth and competitive edge in the data-driven business landscape.
Pioneering The Future With Advanced Research Data Analysis Techniques
● Research data analysis is at the forefront of innovation in the business world. Companies offering Ph.D. research proposals are using this actively nowadays.
● From understanding customer behaviors to predicting future trends, data analysis has become a crucial tool for business success.
● The evolution of this field is continuously unveiling new techniques and methodologies that offer deeper insights and more precise predictions.
● Explored below are some of the advanced techniques in research data analysis that are shaping the future of businesses.
● Machine Learning○ Machine learning is a subdiscipline of artificial intelligence that makes it possible for systems to learn and improve from experience without explicit programming.
○ It is particularly effective in analyzing large and complex datasets.
○ Machine learning algorithms can help businesses predict customer behavior, identify patterns and anomalies, automate decision-making processes, and much more.
● Natural Language Processing (NLP)○ NLP is another AI technique that allows computers to understand, interpret, and generate human language.
○ For businesses, NLP can be used to analyze text data like customer reviews, social media comments, or call center transcripts, providing valuable insights into customer sentiment and preferences.
● Deep Learning○ Deep learning is another aspect of machine learning that apes the functioning of the human brain to process data.
○ It's exceptionally useful for tasks like image and speech recognition and can also be employed for complex prediction tasks.
○ For instance, deep learning can be used in predictive maintenance, where it can predict equipment failures by analyzing various sensor data.
● Network Analysis○ Network analysis involves studying the relationships between entities.
○ In a business context, it can be used to understand relationships between different products, customers, or various business units.
○ By analyzing these networks, businesses can identify influential entities, understand community structures, and optimize communication or distribution paths.
● Time Series Analysis○ Time series analysis involves analyzing data that is collected over time to identify patterns or trends.
○ This method is commonly used for forecasting purposes.
○ For instance, businesses can use time series analysis to forecast sales, stock prices, or product demand.
● Sentiment Analysis○ Sentiment analysis, also called opinion mining, is a technique used to determine the sentiments expressed in a piece of text.
○ Businesses can use sentiment analysis to understand customer opinions and feelings towards products, services, or brands, often using data from social media or customer reviews.
● The advanced techniques of research data analysis open new doors for businesses to understand their environment, optimize their operations, and innovate their strategies.
● From machine learning to sentiment analysis, these techniques offer a deeper, more nuanced understanding of data, enabling businesses to make better-informed, data-driven decisions.
● In the evolving world of business, the importance of staying on the cutting edge of data analysis techniques cannot be overstated.
● Businesses that adapt and adopt these advanced methodologies will not only obtain a competitive edge but also pioneer the future of their respective industries.
2023.06.02 13:20 jennyslate26 How to clean a pasta maker? – The best way
|submitted by jennyslate26 to u/jennyslate26 [link] [comments]|
2023.06.02 13:16 bbnt93 Paranoid I’m poisoning myself
2023.06.02 13:12 canichangeit110 Hey guys I've started my streak, let me know how you guys feel about it after 90 days.
2023.06.02 13:10 ThrowRA-Illustrious6 My (26M) and my girlfriend (22F) had an argument after I told her I wanted to go home and sleep early
2023.06.02 13:09 rusticgorilla Supreme Court ruling makes it even riskier for unions to strike
In August 2017, the Union, which represents Glacier’s truck drivers, was engaged in collective bargaining negotiations with Glacier. Unhappy with the company’s response to its bargaining demands, the Union devised and executed a scheme to “intentionally sabotage” Glacier’s business operations and destroy its property. On the morning of August 11, Glacier had numerous concrete deliveries scheduled, with drivers starting work between 2 AM and 7 AM. Knowing this, the Union “coordinated with truck drivers to purposely time [a] strike when concrete was being batched and delivered” with the specific purpose “to cause destruction of the concrete.” At 7 AM, once “Union representatives knew there was a substantial volume of batched concrete in Glaciers barrels, hoppers, and ready-mix trucks, they called for a work stoppage.” A Union agent made a throat-slashing gesture to signal a “sudden cessation of work.”Non-union employees were dispatched to clean the trucks, preventing damage. However, the mixed concrete had to be destroyed.
On the day the strike began, 43 drivers were scheduled to work. The drivers arrived at staggered start times running from 2 a.m. to 7 a.m. Local 174 called the strike at 7 a.m., when all of the scheduled drivers had arrived for work…When the strike began, some trucks were at Glacier’s yard waiting to be loaded, some were returning to the yard to be reflled and some were out with loads of concrete to be delivered. Sixteen of the striking drivers returned trucks containing undelivered concrete to Glacier’s yard. These drivers left their trucks running so that Glacier could dispose of the concrete as the Company saw fit.Glacier sued the Teamsters in Washington state court for intentionally destroying its property. In doing so, the company indirectly challenged existing Supreme Court precedent set in 1959’s
It is not for us to decide whether the National Labor Relations Board would have, or should have, decided these questions in the same manner. When an activity is arguably subject to § 7 [which includes strikes] or § 8 [unfair labor practice] of the Act, the States as well as the federal courts must defer to the exclusive competence of the National Labor Relations Board if the danger of state interference with national policy is to be averted…If the Board decides, subject to appropriate federal judicial review, that conduct is protected by § 7, or prohibited by § 8, then the matter is at an end, and the States are ousted of all jurisdiction. Or, the Board may decide that an activity is neither protected nor prohibited, and thereby raise the question whether such activity may be regulated by the States.Glacier should have brought its complaint to the NLRB, which would have decided whether this particular strike violated the law. Instead, Glacier brought the case to the Washington state courts, lost, and ultimately appealed to the U.S. Supreme Court.
The Board has long taken the position—which both the Union and Glacier accept—that the NLRA does not shield strikers who fail to take “reasonable precautions” to protect their employer’s property from foreseeable, aggravated, and imminent danger due to the sudden cessation of work. Given this undisputed limitation on the right to strike, we proceed to consider whether the Union has demonstrated that the statute arguably protects the drivers’ conduct. Davis, 476 U. S., at 395. We conclude that it has not. The drivers engaged in a sudden cessation of work that put Glacier’s property in foreseeable and imminent danger…The Union failed to “take reasonable precautions to protect” against this foreseeable and imminent danger.With this ruling, the Supreme Court partly reverses Garmon. Employers will now be allowed to sue unions in state court before the NLRB completes its review of the case. As Ian Millhiser explains in Vox, the outcome (1) is costly for unions and (2) creates a more uncertain atmosphere for strikes:
Glacier Northwest is still a significant loss for unions, in large part because it does not draw clear lines indicating when Garmon still applies and when it does not. Suppose, for example, that a single angry worker picks up a piece of their employer’s equipment and smashes it at the beginning of a work stoppage. Does this one worker’s wildcat action render the entire union vulnerable to litigation?Justices Roberts, Sotomayor, Kagan, and Kavanaugh joined Barrett’s opinion. Justices Thomas, Gorsuch, and Alito concurred in judgment, but wrote or joined separate opinions advocating for the Supreme Court to overturn Garmon altogether. Justice Thomas wrote:
Similarly, imagine a company much like Glacier Northwest, except that this company is so busy that it always has at least one truck full of wet concrete being delivered to a client. At what point are this union’s workers allowed to strike? And, if they do strike, what are the precise precautions the union must take in order to protect the employer’s trucks?
Questions like these will need to be decided in future litigation — and the mere existence of this litigation will only undermine Garmon even more. Striking unions will now potentially have to litigate one case in the NLRB while simultaneously litigating a second case whose purpose is to determine whether their employer is allowed to sue them in state court.
That will make it much easier for well-moneyed employers to grind down unions with legal fees.
The parties here have not asked us to reconsider Garmon, nor is it necessary to do so to resolve this case. Nonetheless, in an appropriate case, we should carefully reexamine whether the law supports Garmon’s “unusual” preemption regime. In doing so, I would bear in mind that any proper pre-emption inquiry must focus on the NLRA’s text and ask whether federal law and state law “are in logical contradiction,” such that it is impossible to comply with both.
The right to strike is fundamental to American labor law. Congress enshrined that right in the National Labor Relations Act (NLRA) and simultaneously established the National Labor Relations Board to adjudicate disputes that arise between workers and management. That decision reflected Congress’s judgment that an agency with specialized expertise should develop and enforce national labor law in a uniform manner, through case-by-case adjudication. For its part, this Court has scrupulously guarded the Board’s authority for more than half a century. See San Diego Building Trades Council v. Garmon, 359 U. S. 236 (1959). Under Garmon, and as relevant here, a court presented with a tort suit based on strike conduct generally must pause proceedings and permit the Board to determine in the first instance whether the union’s conduct is lawful if the conduct at issue is even “arguably” protected by the NLRA.The court incorrectly placed the onus of protecting Glacier’s property on the workers and the union, Jackson continued:
Today, the Court falters. As the majority acknowledges, the Board’s General Counsel has filed a complaint with the Board after a thorough factual investigation, and that complaint alleges that the NLRA protects the strike conduct at the center of this state-court tort suit. The logical implication of a General Counsel complaint under Garmon is that the union’s conduct is at least arguably protected by the NLRA. Consequently, where (as here) there is a General Counsel complaint pending before the Board, courts—including this Court—should suspend their examination. Garmon makes clear that we have no business delving into this particular labor dispute at this time.
But instead of modestly standing down, the majority eagerly inserts itself into this conflict, proceeding to opine on the propriety of the union’s strike activity based on the facts alleged in the employer’s state-court complaint. As part of this mistaken expedition, the majority tries its own hand at applying the Board’s decisions to a relatively novel scenario that poses difficult line-drawing questions—fact-sensitive issues that Congress plainly intended for the Board to address after an investigation.
To the extent that the majority’s conclusion rests on the alleged fact that “by reporting for duty and pretending as if they would deliver the concrete, the drivers prompted the creation of the perishable product” that “put Glacier’s trucks in harm’s way,” I see nothing aggravated or even untoward about that conduct. Glacier is a concrete delivery company whose drivers are responsible for delivering wet concrete, so it is unremarkable that the drivers struck at a time when there was concrete in the trucks. While selling perishable products may be risky business, the perishable nature of Glacier’s concrete did not impose some obligation on the drivers to strike in the middle of the night or before the next day’s jobs had started. To the contrary, it was entirely lawful for the drivers to start their workday per usual, and for the Union to time the strike to put “maximum pressure on the employer at minimum economic cost to the union.”
Nor was the onus of protecting Glacier’s economic interests if a strike was called in the middle of the day on the drivers—it was, instead, on Glacier, which could have taken any number of prophylactic, mitigating measures. What Glacier seeks to do here is to shift the duty of protecting an employer’s property from damage or loss incident to a strike onto the striking workers, beyond what the Board has already permitted via the reasonable-precautions principle. In my view, doing that places a significant burden on the employees’ exercise of their statutory right to strike, unjustifiably undermining Congress’s intent. Workers are not indentured servants, bound to continue laboring until anyplanned work stoppage would be as painless as possible for their master. They are employees whose collective and peaceful decision to withhold their labor is protected by the NLRA even if economic injury results.
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