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In knowledge retrieval apps, the way prompts work has to do directly with accuracy, efficiency, and user experience. An unclear prompt might lead to inaccurate and irrelevant results, negatively impacting the user experience. This article covers some best practices to ensure your AI responds precisely to the information you are seeking. As and when you step into a huge library, stacked with millions of books, each holding a repository of knowledge on diverse subjects, you begin searching for something specific – let’s say, the latest advancements in solar energy technology. However, without knowing how to effectively ask the librarian or use the cataloging system, you could end up with books on basic solar concepts, historial solar studies, or even unrelated subjects like lunar astronomy. This narrative vividly illustrates the crux of prompt engineering in the sophisticated digital arena of knowledge retrieval.  Prompt engineering in artificial intelligence (AI) is akin to asking a librarian a well-formulated question. It involves the adept creation of queries and instructions, guiding AI systems—our contemporary digital librarians—to navigate through extensive information repositories and extract the most pertinent and precise answers. Let’s learn the subject in more detail. Introduction to Prompt Engineering for Knowledge Retrieval Applications Prompt engineering, at its most fundamental, involves the design and optimization of queries or instructions to guide AI systems in effectively parsing and retrieving the right information from expansive data sets. It is a nuanced subject that combines elements of language, psychology, and data science to interact with AI in a way that yields the most accurate and relevant results. In knowledge retrieval apps, prompt engineering is not just about asking questions. It’s about asking the right questions in the right way. Whether it’s a business analyst seeking specific market trends or a student exploring a complex scientific concept, how they frame their query significantly impacts the quality of information retrieved. Importance in Knowledge Retrieval Applications The importance of prompt engineering in knowledge retrieval applications is multi-faceted: Core Principles of Prompt Engineering Understanding User Intent Fundamental to effective prompt engineering is grasping the user’s underlying intent. This involves interpreting not just the words used, but the context and purpose behind a query. For instance, when a user asks about “the impact of climate change on agriculture,” they could be seeking economic, environmental, or social perspectives. Recognizing these nuances is critical in shaping accurate prompts. Clarity and Precision in Prompt Design The effectiveness of a prompt is often tied to its clarity and specificity. Vague or overly broad prompts can lead AI systems down a rabbit hole of irrelevant information. Precision in prompt design helps in narrowing down the focus, leading to more relevant and concise answers. Contextualization of Queries Embedding context within prompts is a skill that significantly enhances the relevance of the information retrieved. It involves adding necessary background details that guide the AI system. For instance, specifying the time frame or geographic focus in a prompt can drastically change the nature of the information retrieved. Types of Prompts in Knowledge Retrieval Open-ended vs. Targeted Prompts Open-ended prompts are designed to explore a wide range of responses, ideal for brainstorming or exploratory research. In contrast, targeted prompts are specific, seeking particular pieces of information, suitable for precise, fact-based queries. Iterative Prompts These prompts involve a series of questions that build on each other, allowing users to delve deeper into a topic. Iterative prompts are particularly useful in complex research areas where understanding evolves step by step. Exploratory vs. Confirmatory Prompts Exploratory prompts are used to gather broad information on a new or unfamiliar topic. Confirmatory prompts, on the other hand, aim to validate or refute specific hypotheses or beliefs. Best Practices for Prompt Engineering for Knowledge Retrieval Applications Balancing Specificity and Flexibility Crafting prompts that strike the right balance between being too broad and overly narrow is crucial. For instance, if a researcher is looking into the “effects of meditation on stress,” a prompt that’s too broad like “tell me about meditation” might bring up a vast array of unrelated information. Conversely, a prompt that’s overly narrow, such as “how does meditation reduce cortisol levels in women aged 30-40?” might miss relevant studies outside this demographic. An optimally balanced prompt might be “summarize recent research on meditation’s impact on stress management.” Incorporating Context and Background Information Including relevant context can significantly refine the information retrieved. Consider a business analyst seeking information on “emerging market trends.” Without context, this prompt could return a generic overview. However, by adding context, such as “emerging market trends in the electric vehicle industry in Europe in 2023,” the prompt becomes far more targeted, likely yielding specific and useful insights. Use of Natural Language and User-Friendly Terminology Prompts should be phrased in a way that’s both natural and easy to understand. For example, a medical student might seek information on a complex topic like “myocardial infarction.” Instead of using technical terms, a more effective prompt could be “explain heart attacks and their causes in simple terms.” This approach makes the interaction more intuitive, especially for users not well-versed in medical jargon. Iterative Refinement of Prompts The process of developing an effective prompt is often iterative. Start with a general prompt and refine it based on the responses received. For instance, an initial query about “renewable energy sources” might lead to various subtopics. Based on interest, subsequent prompts can be more specific, like “compare solar and wind energy efficiency,” gradually honing in on the precise information needed. Leveraging Keywords and Phrases Identifying and using the right keywords or phrases can dramatically enhance the precision of information retrieval. For a student researching “Shakespeare’s influence on modern literature,” including keywords like “Shakespearean themes,” “contemporary adaptations,” or “modern Shakespeare interpretations” in the prompt can direct the AI to focus on specific aspects, ensuring more relevant results. Anticipating Misinterpretations and Ambiguities Being aware of how an AI might misinterpret a prompt is important. For instance, a query about “Apple’s latest developments” could be interpreted as concerning the fruit or the tech company.

Accurate demand forecasting is the bedrock of successful businesses, enabling them to optimize inventory, reduce costs, and exceed customer expectations. However, navigating the intricacies of SKU (Stock Keeping Unit) demand forecasting is no easy task. Shockingly, industry reports reveal that up to 70% of companies struggle with SKU demand forecasting, leading to costly inventory imbalances and missed revenue opportunities. In this blog, we will delve into the top five common pitfalls that undermine SKU demand forecasting accuracy and provide actionable solutions to overcome them. Additionally, we will showcase real-life examples of renowned brands that have achieved remarkable success by implementing robust forecasting strategies. Common Pitfalls in SKU Demand Forecasting: Inadequate Data Analysis and Modeling In today’s data-driven landscape, a staggering 60% of companies still grapple with data analysis and modeling challenges. Relying on incomplete or inaccurate data leads to subpar forecasting accuracy, and the consequences are dire. In fact, organizations plagued by data analysis shortcomings experience a 5% to 10% increase in inventory carrying costs and a 3% to 8% reduction in customer service levels, resulting in dissatisfied customers and lost sales opportunities. Ignoring Seasonality and Market Trends Market dynamics and seasonality exert a significant influence on SKU demand, yet a considerable number of businesses fail to incorporate them into their forecasting processes. Research indicates that overlooking these crucial factors can result in a 20% to 40% decrease in forecasting accuracy. Consequently, companies face challenges such as excessive inventory, missed sales during peak seasons, and dissatisfied customers due to stockouts. Lack of Collaboration between Departments Siloed decision-making hampers accurate SKU demand forecasting and undermines overall organizational efficiency. Surveys indicate that 80% of businesses suffer from inadequate collaboration between departments, leading to fragmented forecasts and a lack of consensus on demand projections. This disjointed approach yields poor inventory allocation, increased carrying costs, and missed revenue opportunities. Conversely, organizations that foster cross-functional collaboration witness a 15% to 25% improvement in forecasting accuracy and a 10% to 15% reduction in excess inventory. Overreliance on Historical Data While historical data provides a valuable foundation for forecasting, relying solely on it can be detrimental. In a rapidly evolving marketplace, companies must consider external factors, such as macroeconomic trends and competitor actions, to augment their forecasting models. According to industry reports, businesses that strike a balance between historical data and external factors achieve a remarkable 30% to 50% increase in forecasting accuracy, resulting in optimized inventory levels and improved customer satisfaction. Ineffective Demand Forecasting Tools and Technology Outdated or inadequate demand forecasting tools impede accurate SKU demand projections, hindering businesses from capitalizing on market opportunities. Astonishingly, a survey reveals that 65% of companies express dissatisfaction with their current forecasting tools. These limitations hinder scalability, adaptability, and efficiency. By embracing advanced forecasting technologies, including artificial intelligence (AI) and machine learning, companies witness a staggering 25% to 40% enhancement in forecasting accuracy, enabling precise inventory planning and strategic decision-making. Overcoming the Common Pitfalls: Comprehensive Data Analysis and Modeling To address the first pitfall, organizations must invest in advanced analytics and machine learning algorithms. By harnessing the power of these technologies, businesses witness a 40% to 60% improvement in forecasting accuracy. The ability to analyze vast amounts of data, identify patterns, and incorporate complex variables empowers companies to make informed decisions, reduce inventory costs, and optimize customer service levels.   Incorporating Seasonality and Market Trends Companies can overcome the second pitfall by leveraging advanced demand forecasting models that account for seasonality and market trends. By doing so, businesses achieve a 25% to 35% increase in forecasting accuracy. Accurate predictions allow companies to align inventory levels with consumer demand, prevent stockouts during peak seasons, and capture market share through targeted marketing and promotions. Foster Cross-Functional Collaboration Breaking down departmental silos is crucial to addressing the third pitfall. Organizations that foster cross-functional collaboration witness a significant 20% to 30% improvement in forecasting accuracy. By establishing a collaborative environment that encourages knowledge sharing and data-driven decision-making, businesses achieve streamlined forecasting processes, enhanced forecast reliability, and reduced inventory holding costs. Balancing Historical Data and External Factors To avoid the fourth pitfall, companies should adopt a balanced approach that incorporates both historical data and external factors. By leveraging real-time market intelligence and competitor insights, businesses experience a 30% to 50% increase in forecasting accuracy. This enables agile inventory management, faster response to market changes, and improved customer satisfaction. Adopting Advanced Forecasting Tools and Technology To mitigate the final pitfall, organizations should embrace advanced forecasting tools empowered by AI and machine learning algorithms. By leveraging these technologies, businesses witness a remarkable 25% to 40% improvement in forecasting accuracy. AI-powered forecasting tools enable companies to automate processes, generate accurate predictions, and gain actionable insights for inventory optimization and strategic decision-making. Real-life examples There are many real-life examples of businesses that have successfully avoided the common pitfalls of SKU demand forecasting. Here are a few examples: Walmart: Walmart, a global retail giant, has established a highly advanced demand forecasting system that relies on a diverse range of data sources, including sales history, customer surveys, and market research. By utilizing multiple forecasting methods, Walmart aims to minimize the risk of inaccurate predictions. As a result of these forecasting efforts, Walmart has achieved a significant reduction in its inventory costs, estimated at approximately $3 billion per year. The forecast accuracy of Walmart stands at 85%, and the success metrics associated with this accuracy include reduced inventory costs, improved customer service, and increased sales by an estimated 5% annually. Amazon: Another notable company with a sophisticated demand forecasting system is Amazon. By leveraging various data sources such as sales history, customer search behavior, and product reviews, Amazon generates accurate demand forecasts. Furthermore, the company incorporates machine learning techniques to enhance the precision of its predictions. Amazon’s efforts in demand forecasting have yielded substantial benefits, including a reduction in out-of-stock rates and an improvement in customer satisfaction. With a forecast accuracy of 95%, Amazon has successfully reduced its inventory costs by an estimated $5 billion per year and experienced an

In today’s rapidly evolving manufacturing landscape, digital transformation has become a strategic imperative for organizations aiming to thrive in a highly competitive marketplace. The convergence of technology and industrial processes has given birth to the Industrial Internet of Things (IIoT), a network of connected devices, sensors, and systems that has the potential to revolutionize manufacturing operations and drive overall business performance. With its ability to collect real-time data, enable predictive analytics, and enhance decision-making, IIoT has emerged as a game-changer for manufacturers worldwide. In this blog, we will explore the profound impact that IIoT can have on manufacturing operations, shedding light on its transformative capabilities. Operational Efficiency and Cost Optimization: One of the key advantages of IIoT lies in its potential to optimize operational efficiency and drive cost savings for manufacturers. By harnessing the power of real-time data, organizations can gain enhanced visibility into their processes, enabling them to identify bottlenecks, streamline workflows, and minimize downtime. According to a McKinsey report, the adoption of IIoT in manufacturing can lead to a productivity improvement of up to 30% and a reduction in maintenance costs of up to 50%. A striking example of the transformative impact of IIoT is seen in Rolls-Royce, a global leader in engine manufacturing. Through their “TotalCare” program, Rolls-Royce utilizes IIoT technology to monitor engine performance in real-time. This enables them to predict maintenance needs and address issues proactively, resulting in an astounding 80% reduction in unscheduled maintenance events and annual cost savings of approximately $250 million. (Source: GE Digital) Enhanced Quality Control and Predictive Maintenance: IIoT plays a pivotal role in improving quality control and enabling predictive maintenance in manufacturing operations. By integrating sensors and real-time monitoring systems, manufacturers can detect deviations from desired parameters, ensuring consistent product quality and reliability. Gartner predicts that by 2025, predictive maintenance enabled by IIoT will reduce machine downtime by 50% and increase equipment lifespan by up to 20%. Michelin, a renowned tire manufacturing company, exemplifies the power of IIoT in enhancing production processes and customer value. By incorporating smart sensors into their tires, Michelin gains real-time visibility into tire performance and usage. This data enables proactive monitoring, leading to a 15% reduction in maintenance costs, a 10% extension in tire lifespan, and a 7% improvement in fuel efficiency. (Source: Microsoft) Supply Chain Optimization and Demand Responsiveness: Efficient supply chain management is critical for manufacturers to meet customer demands while minimizing costs. IIoT facilitates seamless connectivity and data exchange across the supply chain, driving optimization and demand responsiveness. Deloitte estimates that IIoT-enabled supply chains can reduce logistics costs by up to 30% and improve order fulfillment rates by up to 20%. Walmart, the world’s largest retailer, serves as a prime example of how IIoT can optimize supply chain operations. Through the implementation of IIoT-enabled devices such as RFID tags and sensors, Walmart achieves real-time visibility into inventory levels, reducing stockouts and ensuring accurate demand forecasting. The result is an improved supply chain efficiency, reduced costs, and enhanced customer satisfaction. (Source: Walmart) Worker Safety and Productivity Enhancement: Ensuring worker safety and maximizing productivity are paramount in manufacturing environments. IIoT plays a critical role in achieving these goals by equipping workers with wearable devices and real-time monitoring systems. This enables organizations to create a safe working environment, providing timely alerts in case of potential hazards. Additionally, IIoT enables real-time performance monitoring of production lines, facilitating swift adjustments and optimizations to enhance worker productivity. General Electric (GE) is a prime example of how IIoT can enhance worker safety and productivity. By leveraging wearable devices and real-time monitoring systems, GE has reduced workplace accidents by 47% and increased worker productivity by 20%. (Source: General Electric) Data-Driven Decision Making and Predictive Analytics: The abundance of data generated by IIoT devices empowers manufacturers with valuable insights for data-driven decision-making and predictive analytics. By analyzing real-time data streams, manufacturers can identify patterns, trends, and anomalies, allowing them to make informed decisions and optimize processes. IDC estimates that organizations embracing IIoT can achieve up to a 30% improvement in critical process cycle times.  John Deere, a leading agricultural equipment manufacturer, leverages IIoT to enhance their product offerings and customer experience. By collecting and analyzing data from connected farming equipment, John Deere provides farmers with real-time insights and recommendations for optimizing their farming practices. This has resulted in a 20% increase in crop yields and significant cost savings for farmers. (Source: John Deere) Innovation and New Business Models: IIoT unlocks new avenues for innovation and the development of disruptive business models. By leveraging IIoT, manufacturers can explore value-added services such as remote monitoring, predictive maintenance-as-a-service, and outcome-based business models. This enables organizations to differentiate themselves in the market, create new revenue streams, and forge stronger customer relationships. Amazon, a global e-commerce giant, has transformed the manufacturing landscape through their IIoT-enabled business model. Through the integration of IIoT devices and data analytics, Amazon has optimized their fulfillment processes, enabling faster delivery and improved customer experience. Furthermore, Amazon’s use of collaborative robots in their warehouses showcases the potential of IIoT in automating and streamlining operations. (Source: TechRepublic) Conclusion: The potential impact of IIoT on manufacturing operations and overall business performance is profound. Real-life examples from industry leaders such as Rolls-Royce, Michelin, Walmart, General Electric, John Deere, and Amazon demonstrate the transformative capabilities of IIoT in optimizing operational efficiency, enhancing quality control, streamlining supply chains, improving worker safety and productivity, enabling data-driven decision-making, and fostering innovation. As digital transformation becomes a necessity in the manufacturing industry, decision-makers in the space must recognize the strategic importance of IIoT. By embracing IIoT, manufacturers can embark on a journey of connected manufacturing, driving operational excellence, sustainable growth, and competitive advantage in the digital age. The era of IIoT-powered manufacturing has arrived, and those who seize its potential will lead the way into a more efficient, productive, and innovative future.

Reducing the volume of data transmitted for image classification is a crucial task, particularly when dealing with large quantities of images and the associated costs and network constraints. In our quest for an efficient and cost-effective solution, we have devised a comprehensive approach that leverages edge analytics and intelligent processing to minimize unnecessary data transmission. By implementing machine learning capabilities at the edge level and employing selective image analysis, we are able to significantly reduce the number of images sent to the cloud for classification. This not only optimizes resource utilization but also has the potential to reduce computational costs. In this article, we explore our methodology for reducing image data and the various approaches used to achieve accurate classification while mitigating the associated expenses. Consider the scenario where a camera captures images at a rate of 2 frames per second, resulting in an overwhelming total of 7,200 images per hour and a staggering 172,800 images within a 24-hour period! Considering each image size to be around 600KB, a staggering 121 GB of data would be required for a single day. This amount of data is quite substantial and could lead to high costs and potential network congestion. To address this challenge, one possible solution is to limit the data transmitted. By enabling smart cameras to perform image analysis locally, we significantly limit the amount of data sent to the cloud. Leveraging machine learning capabilities at the edge level, our intelligent processing begins by capturing a reference image, which serves as a benchmark for subsequent comparisons. Rather than transmitting every image, we adopt a selective approach. If subsequent images appear similar to the reference image, they are deemed redundant and not transmitted. However, if a change is detected, such as the presence of a new object, the corresponding image is then sent to the cloud for further analysis, ensuring that only relevant data is processed remotely. To maintain accurate comparisons over time, we periodically update the reference image to adapt to changing lighting conditions. Every 15 minutes, a new reference image is captured, while every fourth week, a fresh set of reference images is created to account for variations in time. By incorporating these updates, we ensure precise and reliable classification results. An important step in minimizing redundant image transmissions involves cropping specific areas of the images. Through careful observation, we have identified that the left and right sections of the images predominantly consist of plantations, making it highly unlikely for animals to traverse those regions. Additionally, the presence of insects in certain images, as depicted in Fig. 2.a., can lead to false interpretations of changes by the classification model. Therefore, we strategically crop the peripheral areas of the images, as shown in Figures 1.a., 2.a., and 3.a., effectively eliminating unnecessary image transmissions caused by leaf movement and insect appearances. This targeted cropping technique confines the transmission to only the essential parts of the images, further optimizing the data sent for analysis. Furthermore, we employ a crucial principle in our data reduction strategy: no image is transmitted unless an object is detected. By examining Figures 1.a., 2.a., and 3.a., we can better grasp this concept. In the initial image (Fig. 1.a.), which serves as the reference, subsequent images like Fig. 2.a., while similar, do not contain any animals and therefore do not require transmission. However, in Fig. 3.a., where an animal is present, the image becomes a candidate for transmission. This selective approach ensures that only images capturing relevant objects are sent for further processing, significantly reducing the volume of data transmitted. To enable the device to identify objects accurately, we have implemented several approaches, as depicted in Fig. 4. The first approach involves calculating the signal-to-noise ratio (SNR) of each image in comparison to the reference image. Fig. 5 showcases the distribution of SNR probability values. Notably, the orange line indicates a higher likelihood of SNR values between 20 and 40, which determines the images to be transmitted. Conversely, SNR values beyond 40, as indicated by the blue lines, demonstrate a lower likelihood and therefore do not require transmission. This approach ensures that only images with significant changes relative to the reference are sent for further analysis, optimizing data usage. In the second approach, we utilize the density plot of peak difference values, as shown in Fig. 6. By calculating the average difference value, which represents the mean of absolute differences between two images, we can distinguish between classified and non-classified images. Images with average difference value greater than 4 are classified as significant changes and thus eligible for transmission, while those with value below 4 are considered non-classified and can be excluded from data transmission. The Average difference value (mean of absolute differences between two images) are calculated as given in the following formula: The third approach incorporates the density plot of similarity scores, illustrated in Fig. 7. Here, similarity scores are plotted against the probability density function. The images with high probability density values in the score range of 85 to 92 are selected for transmission as classified files, as denoted by the orange lines. This approach enables us to focus on transmitting images that exhibit distinct similarities to the reference, ensuring accurate classification results while minimizing data volume. Lastly, we employ the density plot of mean square error (MSE) of images, as depicted in Fig. 8. By plotting the probability density against the MSE values, we identify the classified images within the MSE value range of 18 to 27, as they exhibit high probability density values. These images are deemed suitable for transmission, as they provide crucial data for accurate species classification. Implementing these comprehensive approaches not only significantly reduces the amount of data transmitted but also yields cost-saving implications at the cloud level. By selectively transmitting images that capture relevant objects and changes, we minimize the computational resources required at the cloud. The reduced workload translates to efficient resource utilization, potentially lowering overall computational costs compared to the previous scenario where all 172,800 images

AI for ESG (Environmental, Social, and Governance) is quickly becoming a critical tool for organizations seeking to become more sustainable. Sustainable investments were predicted to be worth $30 trillion in 2018, a 34% increase over 2016. Investors (and the general public) are increasingly interested in determining if and how enterprises are ecologically and socially responsible. Simultaneously, boards and management have realized that ESG is critical to their firms’ long-term sustainability. It’s no surprise, therefore, that over 90% of S&P 500 companies are already publishing ESG reports in some form. As firms face unprecedented ESG concerns, artificial intelligence (AI) can help establish more responsible business practices. However, organizations must employ AI responsibly since the computational power required to gather, analyze, and act on huge volumes of data is huge. How AI Addresses Challenges Data collection and standardization Collecting and standardizing data on ESG performance can be difficult and time-consuming. Many organizations struggle to gather the necessary data, particularly for social and governance metrics. Additionally, without widely accepted standards for ESG data collection and reporting, comparing performance across organizations is challenging. AI can help automate the data collection process, reducing the time and resources for gathering and processing ESG data. It may also standardize data by recognizing patterns and trends, making it simpler to compare performance across organizations. Materiality It can be challenging to determine which ESG issues are most material to an organization and its stakeholders. Materiality is often context-specific, and different stakeholders could have different priorities. Organizations must identify ESG issues that matter to them and their stakeholders and report on those issues meaningfully. AI makes this possible by analyzing large amounts of data, including social media and other online content, helping identify patterns and trends. Organizations can thus identify the ESG issues that are most important to them and their stakeholders. Assurance Assuring the accuracy and integrity of Environmental, Social, and Governance (ESG) data is crucial for making informed investment decisions and promoting sustainable business practices. However, due to the reliance on self-reported data, it can be challenging. Self-reported data could be biased and manipulated, leading to inaccuracies and unreliable information. AI plays a significant role in addressing these challenges by validating and verifying self-reported data. The algorithms can analyze large amounts of data and identify patterns and anomalies that indicate inaccurate or unreliable information. Additionally, AI can assist in data gathering and collection, ensuring that information is collected in a consistent and unbiased manner. Furthermore, AI can create a more efficient and effective assurance process by automating data cleaning, analysis, and report generation tasks. This can help reduce the risk of human error and improve the overall accuracy and integrity of ESG data. Integration Integrating ESG information into financial reporting and decision-making can be challenging. Many organizations still view ESG information as separate from financial information and may not fully integrate it into their decision-making processes. AI can assist in integrating ESG data into financial reporting and decision-making by providing organizations with an automated and streamlined data collection, analysis, and reporting process. It can help organizations to understand the potential risks and opportunities associated with their operations and make more informed decisions. By providing insights not immediately apparent when looking at financial data alone, AI enables organizations to make better-informed decisions considering long-term sustainability. Sustainability Organizations may find it challenging to balance short-term financial goals with long-term sustainability objectives. This may cause organizations to prioritize short-term goals over long-term sustainability initiatives. AI can help by providing insights into the trade-offs between different ESG initiatives and the potential financial and reputational risks. Additionally, AI can monitor the progress of ESG initiatives and identify areas of improvement. As a result, organizations can stay on track to achieve their sustainability objectives. Furthermore, AI can analyze data from various sources and provide early warning signals of potential reputational risks and financial impacts. Limited understanding Some organizations may have a limited understanding of ESG issues and the impact of their operations on the environment and society. This makes it difficult for them to identify and report on the most material ESG issues. AI can help organizations better understand the environmental and social impacts of their operations by providing them with insights into data that they may not have been aware of or able to gather previously. As ESG reporting becomes more important for organizations, it is important that they address these challenges to ensure that they provide accurate, reliable, and meaningful information to stakeholders. Benefits of Using AI for ESG Environmental By integrating data from sensors and other sources to assist with decision-making, AI has the ability to make greener judgments and mitigate environmental hazards caused by climate change.  A research paper from Elsevier shows that over 20% of energy savings can be achieved by forecasting and adjusting the building’s real-time energy needs based on sensor data. Other applications include: Social AI can assist in studying social networks, identifying patterns, and addressing social concerns more quickly and correctly. Research published in an Elsevier journal indicates that AI can estimate the demand for healthcare services and improve the deployment of healthcare staff and resources, particularly in disadvantaged regions. According to the study, this method can result in more effective resource allocation and better healthcare results. Other use cases include: Governance Having a more efficient way to function is only one example of how AI can promote the “G” of ESG. It can, for example, be used to study public spending and service delivery. It can help firms make better-informed, data-driven decisions that include environmental, social, and governance aspects. A study published in the journal IEEEAccess showed how government forms and applications were rapidly and accurately processed through AI-led automation. This reduced the burden on individuals and organizations while increasing the speed and accuracy of decision-making. Other use cases include: The Future of AI-led ESG Initiatives Data is the common thread in AI’s ESG applications. Over the past decade, data has grown from 6.5 zettabytes in 2012 to 97 zettabytes in 2022, enabling current AI

Low code no code platforms have revolutionized the software development industry recently. Gartner predicts that by 2024, 65% of app activity will result from no-code development. Further, the market for these platforms will grow to $26.9 billion by 2023, $3.8 billion up from 2017. The demand for low code, no code (LCNC) platforms is driven by the need for organizations to deliver software faster, with increased agility, and at lower costs. This is especially important in the current digital landscape, where businesses face increased competition and pressure to innovate. This blog post will explore what LCNC platforms mean, their capabilities, key features, everyday use cases, and ROI. What Is Low-Code, No-Code Platform Development? LCNC platforms are software development tools that allow users to create, deploy, and manage applications without extensive programming knowledge. These platforms typically provide drag-and-drop interfaces and pre-built templates, making it easy for non-technical users to create basic software applications. Some everyday use cases include building workflow automation tools, developing web and mobile applications, and creating simple databases and dashboards. The main idea behind the LCNC platform is to empower business users and other non-technical stakeholders to become more self-sufficient and take a more active role in the software development process. By providing drag-and-drop interfaces and pre-built templates, LCNC platforms allow organizations to automate business processes, build web and mobile apps, and create custom software solutions, all while reducing development time and costs.  Choosing Between No Code and Low Code Low-code and no-code are similar concepts in that both refer to software development tools that do not require extensive programming knowledge. However, there is a subtle difference between the two. Low-code refers to platforms that provide a visual, drag-and-drop interface for creating software applications and also include the option for manual coding. As a result, users can take advantage of pre-built templates and other visual tools to quickly create basic applications. At the same time, they will have the flexibility to add custom functionality using code if necessary. No-code, however, refers to platforms that do not require manual coding. Instead, users can create software applications using pre-built templates and visual tools. While being more manageable and accessible to non-technical users, they also have more limitations, as there is no flexibility to write code manually. Thus, the main differences are the level of technical understanding required to use the platform and the customizability. Why Opt For An LCNC Platform? Essential Use Cases Of LCNC Platforms LCNC platforms are suitable for a wide range of use cases, but some of the most common include: While these are only a few examples, LCNC platforms have several other use cases. For example, some LCNC platforms are generic, while others cater to specific industries, like healthcare or retail. The ROI Of Low Code No Code Platforms Various statistics demonstrate how LCNC platforms increase return on investment (ROI). Some studies show that LCNC platforms can help organizations significantly reduce the time and cost of developing and deploying new applications. For example, a study by Forrester Research found that organizations with lesser coding can develop and deploy an application in just a fraction of the time and cost compared to traditional development methods, as much as 10x faster and at 60% lower prices Another study by Gartner Research found that by 2024, 65% of application development will be via platforms with minimal coding. An LCNC approach to developing enterprise apps requires fewer developer resources and is more appealing to small businesses. When coding is less, average companies can avoid hiring two software developers and save more than $4.4 million in three years. Further, an IDC report found that customers achieved a five-year ROI of 59% with low-code and intelligent process automation. These statistics demonstrate that LCNC platforms can help organizations reduce the time and cost of developing and deploying new applications, which can lead to significant savings in terms of both time and money, thus increasing ROI. However, it is essential to note that the specifics of the return on investment will depend on the specific use case and should be carefully considered and evaluated. 

Low code no code AI platforms function much like other LCNC platforms, allowing non-technical users to leverage AI.  It employs a drag-and-drop interface that enables customers to personalize AI solutions with pre-built models and data connectors. No code platforms go a step further— users can create and deploy artificial intelligence models and applications without any code. Its immense potential lies in the fact that even organizations with limited programming knowledge can unleash the power of AI. Market Outlook for No Code Low Code AI Low-code development technologies will reach $26.9 billion in 2023, up 19.6% from 2022, according to Gartner. By 2025, 70% of new workplace apps will use LCNC technology, a big jump from less than 25% in 2020. The spurt results from companies using complex legacy technologies and techniques having to formulate resilient responses to changing market requirements. This has increased the demand for quick and cost-effective applications, fueling the advent of low-code and no-code (LCNC) development platforms. The growing popularity of LCNCs also reflects the growing requirement to develop applications and digitize processes more quickly. Despite the AI programmer scarcity, LCNC AI helps firms innovate and go to market more quickly. Use Cases of Low Code No Code AI Industries with High Usage of LCNC AI Applications  Currently, many industries and organizations use low-code and no-code AI, including: As AI technologies evolve, many industries and business areas will adopt low-code and no-code platforms. The Future of Low Code, No Code AI We can expect low-code and no-code AI platforms to become increasingly automated and self-service oriented. As a result, users can design and deploy AI models and applications with minimal support. As LCNC AI platforms combine with cloud, IoT, and edge computing, they can provide more powerful and adaptable AI solutions. Moreover, with greater democratization of AI, organizations can leverage AI for strategic decision-making rather than for executive tasks. For instance, LCNC systems will focus more on providing transparent and interpretable models that consumers can trust. Natural language processing (NLP) will enable LCNC systems to handle complex tasks, including language translation, sentiment analysis, and text summarization. Wrapping Up As companies embrace AI, low-code and no-code AI markets are projected to increase. On the other hand, a major disadvantage could be an increasing dependence on a single provider, leading to compromised security. However, the benefits far outweigh the risks.  The frameworks are based on coding languages such as PHP, Python, and Java. Users work in graphically rich simulation environments where they can drag and drop program components, link them, and observe what happens. LCNC platforms are also minimal in maintenance and extremely scalable, allowing operations to achieve enterprise-grade performance, have great readability, expedite debugging and code updates, and allow faster iteration.  As a result, the LCNC wave could be a major driving force toward becoming what Gartner has termed a “Composable Enterprise.”  

In today’s global economy, monitoring the complete supply chain is critical to gaining a competitive edge. Demand and output alter dynamically, making it essential for supply chains to enhance their operations and adapt to evolving client demand. In addition, effective supply chains should provide products and services in a timely, reliable, and cost-effective manner. Leaders and analysts realize that Supply Chain Disruptions are significant hazards that must be understood. Non-integrated production and distribution systems can hinder progress. Many experts need help understanding these disturbances and what tactics and policies can help cope with them. And this is precisely where Supply Chain Simulation can help. By analyzing performance before execution, you can understand how a new plan will influence the whole supply chain before implementing it. Why Should the Supply Chain be Simulated? A faulty plan can cause a ripple effect across the supply chain, resulting in excess inventory, severe backlogs, poor product predictions, imbalanced capacity, poor customer service, unclear production plans, high backlog charges, or even lost revenues. Although ERP and SCM solutions provide several benefits, their ability to undertake what-if scenarios and predictive analyses is restricted. A simulation helps one better understand how connections and operations change over time due to evolving policies and parameters. The following are some examples of popular models used across industries: Increasingly, academicians propose that the discrete event simulation (DES) system may be the most appropriate for supply chains. DES replicates real-world supply chain systems, divided into logically independent operations that continue autonomously across time. Each event occurs within a specific process, and a timestamp is attributed to it. The behavior of intricate systems is codified as an organized sequence of well-defined events happening at one particular instant in time and marking a state change in the system. Discrete event simulation provides dynamic details and opportunities for greater insight by expanding the design, analysis, and optimization toolset for supply chain managers. Eventually, the simulation model used would depend on the purpose and requirements of the firm. The most significant advantage of supply chain simulation is that it allows you to evaluate the system’s performance before implementing an action. It also enables you to conduct better what-if analyses to improve planning and evaluate different operational options without interrupting the real-world process. The results? Seamless flow of goods, on-shelf availability resulting, and improved efficiency thanks to eliminating nonvalue added work. How Does Supply Chain Simulation Work? A supply chain simulation depicts the behavior of a logistics network over time using logical principles. For example, it is possible to begin manufacturing only when inventory dips below a certain level. You can also integrate various rules and study and test their interactions during disruptive events like strikes and natural catastrophes. When Should Supply Chain Simulation Be Used? Modern supply chains produce a large amount of data and are vulnerable to various threats. Both these aspects enhance analytical complexity and encourage dynamic simulation modeling. Simulation is especially beneficial when the underlying system is too complex to be explored using mathematical-analytical approaches. Simulation can also achieve the following: Use Cases of Supply Chain Simulation A supply chain simulation has multiple elements, including analyzing the product mix, evaluating different scenarios, and answering what-if questions related to strategy. Here are some examples of when to use supply chain simulation. Product Mix Analysis Choosing the correct product mix is one of the most critical tasks facing a supply chain company. Some goods may be aligned with the organization’s long-term plan, while others may need to be more profitable. In most cases, these decisions are complicated and determining how they impact the organization’s bottom line is difficult. Simulations give helpful solutions to product mix concerns, allowing planners to analyze potential consequences before executing a strategy, resulting in higher profit margins. Scenario Analysis When procuring items, supply chain planners often have many possibilities. Many decisions about supplier location, pricing, delivery cost, shipping, risk, tariffs, supply continuity, and quality come into play. Using a scenario analysis based on predictive analytics, planners can examine these and other aspects to decide which of many alternatives presents the lowest risk and highest profit while best supporting the company. What-if Analysis What-if analysis allows supply chain planners to anticipate what might happen if X, Y, or Z happened. It identifies threats not merely profitability threats but reputational and other risks to the whole supply chain. With advanced scenario modeling and constraint-based planning, it prepares planners for shifts in market demand. Planners create and test what-if scenarios to evaluate how one event affects projections across the supply chain. Conclusion Supply chain simulation is an evolving, practical technique critical for succeeding in today’s volatile environment. According to a McKinsey study, 73 % of companies encountered problems in their supplier base, while 75% faced production and distribution problems. Though getting everyone on board with a supply chain optimization initiative might be difficult, there is significant value in gathering suppliers, consumers, and management in the same room to examine the whole process using an interactive simulation that animates each link in the chain. Even when teams first utilize simulation as a silo tool to drive in-house process improvement initiatives, they rapidly learn that sharing their simulations with customers can help them interact with stakeholders and strengthen the bidding process. In addition, organizations that experience supply chain difficulties require the reliability of supply chain simulations.

Toilet paper is perhaps the one phrase that sums up the pandemonium that was unleashed across several parts of the world when the COVID-19 pandemic first began to sweep across borders.  Depending on where they were in the world, customers may associate another word with the supply chain disruption that characterized the pandemic in its early days. Many items disappeared from the shelves for weeks. Several others that were being imported from other countries remained off the shelves, as the importer permanently closed the business due to the unforeseen crisis. As companies today vie for competitive advantage, their ability to proactively find and deal with supply chain risks will be a critical differentiator. Although supply chain planners have been recording textual data about risks in business systems, this information is rarely used strategically. Moreover, evaluating hundreds of textual bits manually is difficult, time-consuming, and error-prone. For instance, a single product could amass over 400,000 comments in the course of a year! This is where emerging technologies such as NLP hold immense potential by automating and providing predictive insights. Natural Language Processing (NLP) is a combination of computer science, artificial intelligence, and linguistics that uses models to understand and find patterns in natural language. Typically, humans communicate with machines through the use of specialized programming languages or predefined answers. However, NLP transcends these limits and enables users to connect with computer systems using natural speech and writing patterns. Natural language processing can be applied in several ways to supply chains and logistics. For instance, through training, the model begins recognizing different language patterns; analyzing this data can lead to information, insights, and even automated actions. These capabilities allow businesses to resolve supply chain risks proactively rather than reactively. Benefits Of NLP Capture information Using natural language processing and unstructured data querying, it is possible to look at published material that is available to the public. This content may appear in blogs, videos, social media, news, and other formats. By keeping an eye on social media and scraping websites, businesses can be proactive and evolve their strategy. For instance, social media listening via Twitter and Facebook can be effectively tracked. The software monitors important terms or phrases that can potentially affect the supply chain, and an algorithm analyzes and derives business intelligence from the gathered data. Thus, NLP can efficiently detect issues with certain suppliers, keep track of major environmental changes, provide insights on repercussions, verify sourcing, and monitor competition. It can also help improve supply chain governance, ethical practices, policy and procedure changes, reputation management, and other predictable trends. Generate supply chain maps Supply chain mapping (SCM) is the process of documenting information shared by companies, suppliers, and individuals involved in a company’s supply chain to create a global map of the entire network. For instance, the precise origin of all goods and shipments can be mapped. Initial findings indicate that supply chain mapping solutions generated through Natural Language Processing (NLP) can help businesses: Leverage adaptive chatbots Natural language processing solutions for the supply chain improve consumer-facing applications such as customer service chatbots that can engage with clients, suppliers, manufacturers, and distributors. This would reduce the amount of effort required to carry out orders and eliminate potential inaccuracies. Chatbots can also expedite the procurement process by communicating with supply chain professionals, collecting requirements by chatting in natural language from any location, and simplifying the process for people with low technical expertise. Monitor data changes NLP algorithms can monitor internal data changes in real time, which can aid in maintaining accurate master data. Web scraping collects benchmark industry data for finding transportation rates, fuel, and expenses. This information assists firms in benchmarking their performance against industry standards and identifying potential cost savings. Similarly, NLP can be used to scrape data from logistics carriers’ and shipping ports’ websites. It can analyze the impact of a crisis and suggest measures like increasing the safety stock inventory levels, adopting other modes of transportation and locating new routes to ship products. Web scraping and social media listening can also provide useful supplier data for labor relations, regional political constraints, local news about strikes or riots, and weather events that can disrupt supplier operations and result in supply risk. This data can be further evaluated to generate early warning indicators. Improve customer service down the chain Because supply chains generate a lot of data, finding the best way to use this data to optimize the supply chain is important. NLP allows users to ask complicated questions and guides them through the data to help them find answers. Companies can respond well to stakeholders further down the supply chain if they automate their customer service. Using natural language processing makes automating customer service much easier. When stakeholders ask questions, NLP gives them the correct answers or points them in the right direction. Because of this, the administrative costs in customer service centers go down and customer satisfaction goes up all along the supply chain. Capture information across multiple languages Most businesses operate on a worldwide scale, and language constraints can impede process efficiency. Language limitations are a major concern in global supply chains and logistical performance (e.g., pickup directions and instructions for truck drivers). NLP helps resolve this issue by allowing local stakeholders to communicate in their own language. It can also analyze, organize, and translate data so that it is accessible to all users, as well as translate papers from one language to another to decrease regional language barriers. Track compliance among suppliers Web scraping can be used to track crucial external information about major suppliers. Monitoring a supplier’s stock market performance and reviewing its financial records, for example, can give information about the supplier’s financial stability. Furthermore, online scraping and social media listening can provide additional information about labor relations, regional political constraints, local news about strikes or riots, and weather events, all of which can affect supplier operations and increase supply risk. Using this data, early warning indicators can be generated, and supply chain

Demand planning is the practice of forecasting the demand for a product or service so it can better serve customers. Having the right amount of inventory without incurring shortages or spending money on surplus inventory is at the heart of demand planning. While it may seem unrealistic to expect businesses to consistently match their supply to demand without losing revenue or clients, several customers have realized this vision through AI-based demand planning. So, what exactly is AI-based demand planning? As the name suggests, AI-based demand planning leverages the power of artificial intelligence and machine learning to analyze sales and consumer trends, historical sales, and seasonality data through a combination of sales forecasting, supply chain management, and inventory management. By optimizing your ability to forecast demand efficiently, demand planning can become an established, continuous process that informs your sales and operations strategy. Why Choose AI-based Demand Planning? Utilizing ML and AI demand forecasting has numerous advantages. Adopting AI methodologies can facilitate accurate forecasting at all organizational levels. According to McKinsey, demand management in the supply chain using AI-based methods can reduce supply chain network errors by 30–50%. Applications based on AI and ML utilize data to make predictions. The dimension reduction, cross-validation, and grid search mechanisms enable algorithms to optimize the model and minimize errors by adjusting various features and parameters. Forrester estimates that over the next two years, 55% of organizations will invest in artificial intelligence.  If you have been considering investing in AI-based demand planning, here are five good reasons to make the transition. Top Five Benefits of AI-based Demand Planning   1  Brings accuracy to demand forecasting A superior AI ensures that data can be organized and analyzed in minutes. Since AI transcends historical data and is not reliant solely on rules, it requires businesses to establish fewer rules initially. With the AI handling time-consuming data preparation processes, your precision will improve. Moreover, since data is frequently updated, the results are more accurate. Thus, adopting ML and AI can help retailers harness the power of their data while retaining complete control of it. It is also possible to deliver a unique forecast model for each product.  AI can recognize similarities, make connections, and anticipate patterns that were not initially programmed, providing supply chain professionals with a bird’s-eye view of their inventory and its underlying relationships.  Considering the importance of forecasting from a financial standpoint, retailers’ losses from missed sales or excess inventory add up quickly. With innovative solutions, it is possible to avoid overstock, understock, and out-of-stock situations. 2 Timeliness in demand forecasting  AI systems are often superior to humans when it comes to data-intensive, monotonous tasks. They can assist logistics companies and retailers in analyzing vast amounts of data, identifying inefficiencies and detecting opportunities for improvement. AI systems can analyze multiple data sets simultaneously, combing through information manually and making connections that usually take a lot of time. Therefore, they can provide results in a fraction of the time. Amazon, the leading e-commerce giant, uses AI at nearly every level of its supply chain, particularly in its warehouses. The timely AI predictions help the company fulfil orders faster than most competitors. AI can also help optimize route planning and shipment updates, allowing companies to predict demand and supply. For instance, many companies use technologies like RFID tags to track products through the supply chain. However, sometimes items ship without these tags or events, causing them to be unreadable. Using AI, businesses can analyze things like average shipment times and weather patterns to offer accurate results. 3 Immense cost savings with the right demand planning strategy Besides enhancing accuracy and timeliness, AI can save time, money, and productivity by taking on various administrative and data-intensive tasks. The AI handles administrative tasks, allowing human employees to focus on other projects. For instance, manual tasks such as document filing can cost businesses 6,500 hours per year–a significant amount of time. Imagine what a business could accomplish if it had over 6,000 additional hours to work! This productivity benefit is far too advantageous for logistics companies to ignore. A logistics leader saved 100 million miles and 10 million gallons of fuel annually with AI-optimized route planning. 4 Improved customer service with demand estimation and forecasting Chatbots, typically used in customer service roles, are one of the most common applications of AI, enabling supply chains to provide 24-hour customer service. By delegating customer engagement to artificial intelligence, logistics companies can free up human employees’ time. Not only does this improve efficiency, but it also enhances customer service by increasing the customer’s access to information. Customers can check their order status with an application like Alexa to provide prompt and accurate responses. In addition to being a quick, hands-free information gateway, smart speakers are already common in many homes. People who are used to using these technologies will appreciate the ability to use them to learn about their orders. 5 Strategic decision-making with demand analysis and forecasting Using AI to find outliers in demand planning makes it easier for a business to make good decisions by spotting changes early on and putting adequate measures in place at the right time. A superior AI goes even further by recommending clear courses of action that consider internal constraints and parameters. The risk of interventions that don’t work to meet demand is reduced, as are inefficiencies in the supply chain. Supply chains must make the most of their shipping to deliver products on time. Often, this means planning the fastest, safest way to get from point A to point B. AI is ideal at making these predictions by looking at traffic and weather patterns to determine the best course of action. Since factors like these are dynamic, optimal routes may change daily. As a result, supply chains need tools like AI to analyze data and plan routes quickly. Wrapping Up AI demand forecasting alters how businesses manage their supply chains and make essential business decisions. Rather than relying on manual processes, AI-based demand forecasting collects,