September 16, 2024

In the early years of retail, assortment management was limited to a handful of variables. Mostly, the populace was relatively localized, with most people having similar roots and ideas and spending their whole lives in the same town. Local business owners, therefore, had a good idea of what their customers desired. Today, retailers run national and international chains. A one-size-fits-all strategy would lead to “stock outs” on popular goods and markdowns on others, which would lose the store’s hard-earned revenues. When customers cannot locate the items they want to purchase, customer satisfaction and loyalty are negatively affected. To keep pace with evolving needs, retailers must develop more complex methods of matching consumer needs. A failure to provide a varied assortment that meets the demands of a broader customer base will lead to higher billings, while a failure to do so would leave the door open for competitors. AI makes assortment management and optimization more timely, aligned, and lucrative by precisely forecasting how many variations to provide, how many of a given item are needed to minimize stockouts and markdowns, the storage and display capacity required, etc. How Does AI Help? The right stock at the right store AI models can look at factors like past sales, retail display space, local trends, internet activity, weather forecasts, and more to determine which products would be best for a specific retail location. This AI-based optimization ensures that items are displayed where they can be sold at full price, thus helping to cut down on markdowns. Real-time data analysis also allows retailers to respond instantly to changes in demand, reducing stockouts by moving more items to where they are most likely to be sold. Also, AI models can move goods from one store to another so that businesses can take advantage of local trends. By generating shopper-focused, trend-appropriate assortments, the company can meet customers’ short-term and long-term needs across every category and even predict them. Predictive capabilities AI-enabled technology and systems intelligently mimic human behavior to improve outcomes. Using machine learning, automatic triggers detect periodic trends, peaks, and dips in data and notify merchants and suppliers. Retailers can predict future market behavior by researching past buying patterns, resulting in more precise forecasting and inventory alignment.  By better understanding client preferences, intentions, and behaviors, AI enables shops to collect shopper information in an automated and predictive manner. It can also prevent under or overstocking that affects the bottom line and, in the case of perishable commodities, causes spoilage. Furthermore, the complicated mathematics inherent in AI allows it to provide credible recommendations for upselling and cross-selling more effectively. Better curation Curation helps clients find their needs without overwhelming them with options, brands, or packing sizes. It also increases shelf space. Traditional curation (micro merchandising) is primarily concerned with margins, volume, shop size, location, and what shoppers within a specific zip code purchase. While these are necessary prerequisites, they lack the analytical capacity of artificial intelligence to cross-reference a massive array of data points across various consumer indices. Using AI will allow merchants to understand, for example, if shoppers prefer the brand over price or whether they prioritize pricing over packaging size. This data demonstrates to a retailer what alternatives a client will accept within a specific category or price range. It can help eliminate “dead inventory” and discover items that perform poorly but are frequently purchased by a chain’s highest-volume customers. On the other hand, traditional curation is concerned with gross profit or volume and often overlooks opportunities to retain valuable clients. Respond before competition Because of its extensive supply networks, massive product assortments, and poor profits, retail is regarded as one of the most competitive industries. Traditional offline stores, as well as e-commerce, face stiff competition. AI can help retailers stay relevant by constantly refining product assortments and improving business operations. Product selection is one of the most critical factors that merchants can control to differentiate themselves from the competition. With sophisticated analytics and artificial intelligence, retailers can make better decisions about which things to stock in their stores and adjust their product assortments to local client preferences and store sizes. By leveraging AI’s ability to foresee upcoming trends and identify shortcomings before negatively impacting the market, retailers can create a more profitable and competitive private-label strategy.   Needs-driven assortment optimization  AI employs data mining to examine data samples in real-time and make recommendations based on what works. There is no need to wait until the following calendar review to understand whether an item has experienced a temporary dip or a massive drop. This allows retailers to choose products with proven results. In terms of what customers genuinely want, AI enables merchants to differentiate between perception and reality. For instance, customers may desire to purchase a specific product, but pricing or other factors may discourage the purchase. More likely, probabilities can be generated using advanced modeling and forecasting approaches.  AI can also accelerate product success by projecting SKU-level customer preferences and affinity using demand patterns and buyer propensity modeling. Over time, this approach would lead to increased sales and margins and improved retailer and supplier collaboration. As a result, there is a more level playing field with products that provide tangible benefits and are in line with actual client demands. Conclusion According to research from IDC, 65% of retailers say AI is essential for merchandise analytics, and 54% cite improving ecosystem collaboration with suppliers as a top priority. As more and more retailers get on board with AI, it will not be a differentiator but a necessity. Making suitable investments early on helps you get an early start and leverage your first-mover advantage. Talk to Valiance to understand how our AI solutions can help you optimize the assortment at your retail outlet or e-commerce site.

A data silo is a store of data maintained by one division or team and isolated from the rest of the company. The phrase has agricultural origins and refers to the ideal circumstance in which grain and grass in a field “silo” are shielded from the weather. Within a business unit, however, siloed data is far from desirable. It is frequently incompatible with other data sets, making it difficult for users in other parts of the organization to access it for insights. For instance, finance, administration, human resources, marketing teams, and other departments require precise information to execute their jobs. These departments typically store their data in distinct locations known as data or information silos. As the quantity and diversity of data assets increase, so do data silos. Technical, organizational, and cultural factors can all contribute to data silos. They are common in large corporations because various business units can function independently and have their own goals, priorities, and IT budgets. However, without a well-planned data management strategy, any firm can end up with data silos. The Problem with Data Silos Data is healthy only when it is freely accessible and understood within your organization. If information is difficult to obtain and use promptly, or if it cannot be trusted, it does not offer value. A company that digitizes but does not break down data silos will not reap the full benefits of digital transformation.  Organizations must provide decision-makers with a 360-degree perspective of data relevant to their analyses to become genuinely data-driven. Data silos can lead to a host of challenges: Leads to flawed decision-making  Incomplete and inconsistent data sets can result in poor decision-making. Since data silos prevent users from accessing data, corporate plans and decisions are not based on all accessible information. Data warehouses and lakes that integrate diverse data types for business intelligence (BI) and analytics applications might be derailed by silos. In addition, data from one silo could be inconsistent with other data sets. For instance, a marketing department may arrange consumer data differently than others. Such inconsistencies can produce data quality, accuracy, and integrity challenges that affect both operational and analytic applications’ end users. Gets in the way of productivity Data silos drive up an organization’s IT costs by acquiring more servers and storage devices. In many cases, these additional procurements are also implemented and managed by departments rather than the organization’s data management team, leading to increased costs and inefficient use of IT resources. Moreover, isolated data sets limit opportunities for data exchange and collaboration among users from multiple departments. Achieving business objectives or a shared vision is impossible without teamwork and open communication. Teams working on a self-contained project may overlook critical data streams or mix up comparable data sets that should be kept separate. They may even be working with an outdated version of the same data. When teams only have access to a portion of the data, they may operate with inadequate information, which can limit efficiency and lead to duplication of effort since some of the information requested by one department may already exist in another.  Causes data security and compliance issues Businesses with siloed data find it challenging to establish a complete and effective data governance structure capable of protecting them from data breaches and cyber threats. Data silos also impede an organization’s capacity to detect occurrences that could potentially result in data privacy violations. Such businesses are highly vulnerable to noncompliance with data privacy requirements.  Silos also make it more challenging to comply with data privacy and protection requirements. Individual users, for instance, may keep certain data in Excel spreadsheets or online business platforms like Google Drive. This data can create a security challenge, especially if accessed via mobile devices. Provides a lackluster customer experience 47% of marketers believe that data silos are the key reason they don’t have a complete picture of the customer journey. By decreasing productivity and making it harder for enterprises to access relevant information, client satisfaction and customer experience suffer. Throughout their buying journey, customers have multiple interactions with a particular company, including marketing and sales communications, website and social media visits, and support and billing discussions. When all this information is kept in separate silos, it can pose considerable hurdles and hinder marketing efforts. How Valiance Can Help  At Valiance, we have a simple approach to breaking down data silos. Integrating data The most obvious strategy to break down data silos is integrating them with other systems. The most common type of data integration is via extract, transform, and load (ETL), which involves extracting data from source systems, consolidating it, and loading it into a target system or application. Real-time integration, data virtualization, and extract, load, and transform (ETL) are other data integration approaches that may be employed against silos. Centralizing data repositories These repositories could be data warehouses or data lakes and contain massive amounts of data from many systems in the form of structured, unstructured, and semistructured data, which are utilized in data science applications. Structured transaction data is stored in data warehouses for BI, analytics, and reporting applications. These centralized repositories, when combined, provide a logical solution to silos. Enterprise data management and governance A good data governance program may directly minimize the number of data silos in an organization and promote shared data standards and norms. An enterprise data strategy better connects the data management process with business activities. This method will not only remove current data silos but also prevent the formation of new ones. A comprehensive data architecture design helps document data assets, maps data flows, and offers a roadmap for data platform deployments. The right interface Finding the right interface to allow employees to examine the organization’s data is also important. A change management project to transform an organization’s culture may also be required. Low-code, cloud-native technologies can also help since they can merge various data silos quickly and effectively via intelligent connectivity and automation services. Adding artificial intelligence (AI) and machine learning

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

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.

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 the ever-evolving landscape of retail, staying ahead of the curve is not just an advantage—it’s a necessity. As we navigate the digital age, the integration of cutting-edge technologies is reshaping how businesses understand and cater to their customers. At the forefront of this revolution is computer vision, a transformative force that’s unlocking the power of real-time customer insights and revolutionizing decision-making in retail. The Retail Revolution: From Intuition to Data-Driven Insights Gone are the days when retail success hinged solely on intuition and historical data. Today’s retail environment demands a more nuanced, real-time approach to understanding customer behavior and preferences. This shift has given rise to a new era of retail analytics, where every customer interaction becomes a valuable data point, informing strategies and driving growth. But what exactly is fueling this transformation? The answer lies in the convergence of advanced technologies, with computer vision taking center stage. Understanding Computer Vision in Retail Computer vision, a branch of artificial intelligence that trains computers to interpret and understand visual information, is revolutionizing how retailers perceive and interact with their physical spaces and customers. By leveraging sophisticated algorithms and machine learning models, computer vision systems can analyze video feeds and images in real-time, extracting valuable insights that were previously invisible to the human eye. In fact, a  McKinsey report found, “the average number of AI capabilities that organizations use, such as natural-language generation and computer vision, has also doubled—from 1.9 in 2018 to 3.8 in 2022. Among these capabilities…computer vision has remained the most commonly deployed each year.” Why?  Computer vision can directly grow businesses’ ROI. Whether it’s improving productivity, streamlining processes and tracking downtime, reducing operating costs, or monitoring safety compliance, computer vision improves business efficiency. In the retail context, this translates to a wealth of applications: 1. Customer Flow Analysis: Tracking how customers move through a store, identifying high-traffic areas and bottlenecks. 2. Demographic Insights: Anonymously assessing customer demographics to tailor product offerings and marketing strategies. 3. Queue Management: Monitoring checkout lines to optimize staffing and reduce wait times. 4. Product Interaction: Analyzing how customers engage with products on shelves, informing product placement and store layout decisions. 5. Security and Loss Prevention: Identifying potential security risks and reducing shrinkage through real-time monitoring. The Real-Time Advantage: Why It Matters The true power of computer vision in retail lies in its ability to provide real-time data. According to research from McKinsey, companies who use customer analytics extensively are “significantly more likely to outperform the market,” seeing results like:  This is compared to companies who don’t use customer analytics extensively.  Not only can customer analytics help you predict customer behavior and trends, resulting in more effective marketing, higher customer loyalty, sales, ROI and so on, but you can also leverage real-time analytics for strategic and more agile decision-making.  As the retail landscape evolves, with e-commerce reshaping consumer habits, brick-and-mortar stores must adapt to stay competitive. Monitoring foot traffic through methods like cameras and sensors allows for a more accurate understanding of how customers navigate the store. This understanding allows for making informed decisions that enhance store functionality, customer satisfaction, and business performance. Figure: Implementing foot traffic analysis in retail sector from Vishvesh Soni (2021) The utilization of heatmaps in a retail store is crucial for several reasons. Firstly, they provide a clear and intuitive visual representation of customer traffic within the store, highlighting the paths and areas that are most frequented by customers. This information is invaluable for strategic product placement. Retailers can use these insights to position high-value or high-margin products in areas with high customer traffic, potentially increasing the likelihood of purchase. Additionally, heatmaps can reveal underutilized sections of the store, offering opportunities for improvement or reorganization. This could involve rearranging store layouts, introducing new product lines, or enhancing the visual appeal of these areas to attract more customers. Figure: Store traffic analysis implementation in retails from Vishvesh Soni (2021) Spotlight on Innovation: Valiance’s Foot Fall Vision As we explore the landscape of computer vision in retail, it’s crucial to highlight cutting-edge solutions that are pushing the boundaries of what’s possible. One such innovative product is Foot Fall Vision by Valiance Solutions, a comprehensive customer tracking and analysis tool that exemplifies the power of computer vision in retail. Foot Fall Vision: Computer Vision Based Visitor Analysis Valiance’s Foot Fall Vision leverages advanced AI and computer vision technology to provide retailers with deep insights into customer behavior and store performance. Let’s explore some of its key features and benefits: For instance, the system’s ability to distinguish between employees and customers solves a common problem in foot traffic analysis, ensuring more accurate data. The demographic insights feature allows for more targeted marketing and product placement strategies, potentially increasing sales and customer satisfaction. Moreover, the behavioral analysis capabilities of Foot Fall Vision can lead to more intuitive store layouts, enhancing the overall shopping experience. By understanding how customers move through the store and interact with different product displays, retailers can create more engaging and efficient shopping environments. The system’s focus on sales conversion optimization is particularly noteworthy. In an era where online competition is fierce, brick-and-mortar stores need every advantage they can get. By providing insights into the factors that influence purchase decisions, Foot Fall Vision equips retailers with the knowledge they need to increase conversion rates and maximize the value of each store visit. The Future of Retail Analytics The Future of Retail Analytics: Solutions like Valiance’s Foot Fall Vision are at the forefront of a new era in retail analytics. As these technologies continue to evolve, we can expect even more sophisticated capabilities, such as emotion recognition, augmented reality integration, and hyper-personalized shopping experiences. The key for retailers will be to stay abreast of these developments and to view technologies like Foot Fall Vision not just as tools for data collection, but as strategic assets that can drive business growth and enhance customer experiences. The integration of computer vision and real-time data analytics in retail is not just a technological upgrade—it’s a fundamental shift in how businesses understand and serve their

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,

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

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.

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.”