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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,

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

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.

What is driving the marketplace today? According to Gartner, CX drives over two-thirds of customer loyalty, which represents more than brand and price combined. 86% of consumers say they will leave a brand after as few as two poor experiences, while 65% of consumers find a positive experience with a brand to be more influential than great advertising. Customer experience monitoring is no longer a luxury; it’s vital to your business’s very survival. While providing superlative service is the ultimate goal of every business, it is important to strike a balance between operational efficiency and profitability as well. Speech analytics is an integral component that helps you truly understand your customer so that you can remain highly profitable and in sync with their needs.  Speech analytics is a software technology that transcribes and extracts profound insights, trends, and metrics from every voice call with AI services that encompass transcription, speech technologies, and natural language processing. By comprehending, analyzing, and extracting insights from voice conversations, you can assess agent performance, the customer’s experience, and enterprise-wide strengths and weaknesses. Why Speech Analytics? According to McKinsey, in 2019, only 37% of contact centers believe they are creating value with advanced analytics. With contact centers receiving and making thousands of phone calls each day, ranging from sales and billing inquiries to reviews and complaints from customers, manually listening to and scoring every call received can be arduous. As a result, on an average, only about 5% of calls are scored, causing a lot of information to slip through the cracks. Here is where speech analytics comes into play. When a phone conversation concludes, a speech recognition platform converts the conversation into text for near-immediate analysis. Speech analytics reveals precisely how sentences are uttered and thus reveals their underlying meanings, enabling you to evaluate the types of calls you receive, how your agents handle them, and how the customer felt throughout each conversation. Often, the data is not synthesized into meaningful information for various reasons, including unclean or incomplete data, spread of data across multiple platforms, and a lack of understanding of which metrics are the most important. Combining advanced analytics such as speech and text will provide a clearer picture of the decisions that can improve your contact center’s performance. Maximizing Conversations with Speech Analytics Speech and text analytics allow businesses to comprehend the entire customer journey by discovering information they may not yet possess. It can identify trends and patterns that lead to vital business insights by analyzing millions of transactions across all channels, including traditional phone call recordings, call transcriptions, and even emails and text messages. Here are a few tangible benefits of incorporating speech analytics into your contact center. Surface actionable data Speech and sentiment analysis give you a deeper understanding of your customers’ pain points and help you identify business or product areas that require further research or improvement. You can quickly identify changing customer trends and adapt proactively to meet customers’ desires and needs. Boost agent retention Feedback is a crucial component for an agent to succeed. However, contact center managers cannot monitor and analyze every call. Speech analytics empowers you to extract the sentiment of both the customer and the agent under broad themes. For instance, an agent who is scored as demonstrating a “lack of knowledge” or “lack of compliance” will need to be mentored. Agents who demonstrate concepts of courtesy and ownership effectively can be recognized and compensated for their exemplary performance, leading to a boost in morale.   Enhance the client experience By combining speech and text analysis, it is possible to derive meaning from interactions across all channels and transform data into structured, usable, and even graphical information that is clearly understood by the entire organization. Understanding the hiccups within your multi-channel ecosystem, how customers perceive your organization, and where representatives miss the mark are all useful tools for making better, more data-driven decisions. Real-World Benefits and Use Cases McKinsey reported in 2019 that contact centers that utilized advanced analytics such as speech and text were able to decrease average response time by up to 40%, increase self-service containment rates by 5 to 20%, reduce employee costs by up to $5 million, and increase service-to-sales call conversion rates by nearly 50%— all while meeting customer expectations and engaging employees.  While deployment also includes streamlining your data and internal processes, when implemented right, you can use this information to drive improved results without the need for highly specialised data professionals. Speech to text analytics can: How Valiance Uses Speech to Text Analytics Valiance is a global AI & data analytics consulting firm helping clients of different sizes create decisioning software products using AI & Cloud Computing technologies. We use Amazon Transcribe, an Automatic Speech Recognition (ASR) tool, to generate transcripts out of audio/video files. We help customers monitor the conversation between the customer care executive and the customer through a speech to text algorithm, which checks if the right keywords are being used and flags inappropriate words. When this algorithm is run, the agents are scored and conversation insights such as call sentiment are extracted. These insights form the basis for training the agents further. It can also lead to product revisions and policy changes. Without this information, the real feedback from the customer may never reach management.  We help customers: Wrapping Up It’s time to gain a competitive advantage by leveraging the wealth of information at your fingertips and transforming it into actionable insights. Speech and text analytics enable you to get the most out of every conversation by delving beneath the surface of what customers are saying and getting to the core of their questions in real time. Moreover, AI-driven analytics provides predictive insights thanks to the continually learning, automated technology, so you can proactively address any gaps in your customers’ needs or your agents’ actions.

A PwC poll revealed that over half of the respondents said their buying habits had become “more digital” as a result of the pandemic. In 2020, online shopping accounted for 19.6% of retail sales, up from 15.8% in 2019. As customers realized how quickly and easily they could buy what they wanted from their homes, many became lifelong converts.  However, even as e-commerce continues to grow, it’s not all smooth sailing. Retail brands and marketplaces with thousands of products in their inventory face two common problems: One: An increasing number of product views that do not lead to conversions Two: Replicating the personal touch of in-store shopping AI can help address both these challenges effectively through laser-sharp personalization. Personalization in the E-Commerce Era A sad reality for many e-commerce retailers is that product views do not translate into better revenues. Customers may flip through scores of options, but are they really making purchases? One study revealed that 70% of consumers do not look past the first three pages, making the subsequent pages irrelevant inventory.  Many businesses have attempted to bridge the gap by offering some form of personalization. However, to most online retailers, this is little more than a revolving carousel of recommendations that works on segmentation. It simply lumps customers into large demographic groups based on historical data and stereotyped categories such as gender and age. Though this form of segmentation can suggest a few relevant items, it does not really count as personalization as every client in the specified category sees the same product. Many other sites use machine learning technologies that examine product correlations, identifying which goods are often bought together. This insight is limited in scope; it would be far more useful to examine the interaction between the specific customer and the type of goods he/she engages with. But where neither segmentation nor machine learning have delivered the customized experiences customers desire, AI can make a real difference because of its innate personalization capabilities. Ecommerce personalization refers to a set of practices in which an ecommerce marketplace showcases dynamic content based on consumer data, such as demographic factors, purchase intent, interests, browsing habits, purchasing patterns, and device type. It’s an important consideration as studies show that personalized product recommendations drive revenue. Here are a few recent statistics that underscore the importance of personalization: Clearly, when brands can provide convenience, customer understanding, and emotional engagement, customers are likely to be more loyal, resulting in higher profitability. How AI Can Help Personalize With AI, e-commerce businesses can provide the personalization shoppers crave, transforming every visit into a highly personal experience. New, AI-powered solutions take a one-product-to-one-shopper approach, building unique behavioral profiles based on over 300 data points per customer and predicting the next steps with a fairly high degree of certainty. This kind of e-commerce shopping experience is highly adaptable, reacting and changing in real-time based on the customer’s unique demands and needs. If done right, no two customers will ever have the same experience. Here’s how AI-powered personalization can help online retailers. Meet Customers at Their Point of Need There is no better way to create enhanced consumer experiences than by anticipating desires and providing customers with their favorite items at the right time. AI employs sophisticated machine learning algorithms to monitor browser history, page clicks, social activities (likes, shares), prior purchases, dwell times, geography, and so on, to determine a customer’s interests and preferences. By analyzing patterns of frequently purchased items and adapting web pages and features to meet the demands of individual customers, AI can meet customers at their point of need. As a result, businesses can better manage their inventory while customers benefit from an enjoyable shopping experience.  Enhance the In-Store/Offline Experience AI is also capable of enhancing the in-store experience. For example, AI-enabled kiosks and robots can assist customers in locating products within the store through voice commands or touch screen interfaces. AI can also assist in developing product suggestions based on a customer’s profile, shopping history, search queries, etc. AI-driven virtual assistants can respond to customer questions and make data-driven recommendations whenever customers require them. Augmented Reality interfaces, powered by artificial intelligence, will help customers test things without physically trying them on, thus aiding the purchase decision and enhancing the customer experience. Improve Customer Support In customer service, artificial intelligence significantly saves service time and helps minimize operational expenses. Using AI-powered chatbots and messaging agents to address customer issues in real-time will make customers feel cherished and enhance their overall experience. Programs powered by artificial intelligence can also deliver automated communications to customers of a scheduled service, a replacement part, or a recurring order. It can also facilitate creating automated service requests and provide support for order status, basic order changes, returns, and refunds. Promote Omnichannel Buying Eventually, the online and offline experiences should work together to boost sales. For instance, one retail giant has come up with the concept of offering various 4-star rated products at a  physical store by using its product recommendation engine to identify trending products and customers’ favorites. This ensures that the offline experience is profitable for both the company and the customer; the company saves retail real estate by stocking products with a high likelihood of conversions, while customers can shop with confidence, knowing they are buying products that are peer-approved. Others use AI-gathered data for personalized targeting, allowing customers to try best selling products at the physical stores, thus simplifying the shopping experience and saving the company the hassle of having to deal with product returns or investing in full-fledged retail stores. Reach Customers With More Targeted Campaigns AI can help retailers reach prospects with on-point offers. A smart use of AI-powered customization collects a shopper’s preferences at the time of purchase and pairs them with relevant product data to give suggestions across numerous touchpoints. AI-driven emails can recommend products based on shoppers’ style profiles, preferences, and browsing history. It can encourage repeat purchases. If a consumer recently bought jeans, offer them matching shirts, shoes, and

Demand forecasting is the practice of anticipating future customer demnd over a given period using historical data and other information. Or, to put it more simply, we can forecast future demand for a specific product by analyzing various market factors such as price changes, product designs, competition, advertising campaigns, consumer purchasing power, employment opportunities, population, and so on. Demand forecasting helps organizations make sound business judgments in a competitive market by providing essential information for capital investment and growth decisions. Forecasting is also helpful in accounting, production planning, process selection, capacity planning, facility layout planning, inventory management, etc. It also aids the S&OP process by making pricing and promotion plans easier to design. Demand planning has been routine for many company planners during the past decade, with many businesses incorporating S&OP methods, complex ERP systems, and new AI technologies into their operations. However, the COVID-19 outbreak generated massive demand shocks and intensified industry-wide instability. As the virus began to spread over the world, many businesses were impacted by previously unimaginable occurrences. Lockdowns compelled various manufacturing industries to cease operations, and it became critical to rethink the older demand planning models. Some items like groceries, tissue paper, and medical essentials like sanitizers were seeing massive demand due to panic buying, while other commodities like cars and automobiles faced a sudden slump. Crises are always unplanned. But wise organizations can plan for emergencies, however unexpected. Being at the forefront of AI and ML, our analysts at Valiance have put together several learnings that can help you not merely survive but thrive in a crisis. Learning #1 Greater visibility into channel data The rapid and anomalous shift in demand patterns during a crisis calls for greater visibility into channel data. By gaining insights into the stocks of all their channel partners, companies stand a better chance of restocking and enhancing fulfillment based on actual stock and sell-through information. Supported by powerful insights, they can comprehend client desires in near real-time and shift the needle from demand speculation to demand sensing. For instance, internet traffic is likely to pick up if in-store sales have degenerated due to lockdown. Providing attractive online offers would help convert this traffic into sales. This decision can only be made if the company has good channel visibility and can analyze data from different channels to determine where to focus its sales and promotional efforts. Channel visibility also empowers business leaders to integrate sales, inventory, and manufacturing to leverage previously underutilized channel intelligence. Organizations can make effective business decisions covering the entire gamut of the demand management process, including demand planning, collaborative forecasting and replenishment, revenue recognition, promotions and incentives, sales commissioning, and product life cycle management. Learning #2 Scenario-based thinking and control tower solutions While forecasts are based on the premise that the future will be similar to the present, scenario planning focuses on the future. It entails developing various narratives for distinct courses that will lead to diverse futures. Thus, scenarios present different, longer-term possibilities depending on unknown risks and uncertainties. It is also structured on a dynamic series of interacting events, causal processes, and critical decision points. In place of purely quantitative forecasting, it provides more versatility and preparedness to cope with risk and uncertainty. It involves understanding alternative futures, determining what may occur, and planning the exit strategy out of any crisis so that the possible solutions and outcomes have already been thought out when the crisis hits. This control tower-driven approach aims to consider intricacies and dependencies across a range of parameters. By comprehending the spectrum of potential outcomes, you can stress-test your portfolio of planned strategic movements against extremes and verify that your strategy is successful in various scenarios. Learning #3 Short-term approach with forecast modifiers Businesses must employ forecast modifiers for the short term to mitigate disruptions like COVID-19, taking time, size, channels, and product mix into account. By applying data from previous occurrences, we can improve our estimates for the future. Learning #4 Incorporating machine-learning algorithms When demand is consistent, seasonal, and expanding, a typical forecasting method incorporating sales data from preceding periods is effective. However, if demand increases or decreases abruptly, as, during a crisis, the models would require time to adjust to the new circumstances; hence, the projections will be too low or too high. Therefore, it is crucial to deactivate these conventional algorithms if demand fluctuates rapidly, particularly if they are linked to automated replenishment systems. Machine learning algorithms adapt rapidly and can select more accurate forecasting algorithms independently. Many AI systems of today create forecasts using hundreds of distinct algorithms. As the system detects a deterioration in the performance of a generally effective algorithm, it can instantly switch to more effective models and extract structural insights from the various SKU-store pairings. If this is not allowed, manual overrides must be used; in many cases, just adjusting the average sales of the previous days or weeks would suffice. When further data becomes available, and volatility subsides, model selection can be reconsidered. A daily adjustment of pricing, promotion, and markdown ensures maximum profit. The forecasting process also improves shelf space, product delivery, and deployment of personnel. A precise estimate of demand—by the hour, day, location, and price- can help make crucial decisions concerning inventories, staffing requirements, contact centers, and fleet personnel. Other recommendations The AI-ML Approach to Demand Forecasting Let us know if you’d like to future-proof your demand planning or build out scenarios that will help your business stay resilient during a crisis. Our AI/ ML solutions have enabled scores of companies to optimize their supply chain planning. Speak with our experts. Let’s craft your AI and data analytics journey together.

Data is the lifeblood of any successful business. It is fundamental to the way we run our personal and professional lives. Virtually every encounter generates data, be it software applications, social media links, mobile communications, or the growing numbers of digital services. Each of these encounters then generate even more data. It is estimated that the world’s data will grow to 175 zettabytes by 2025. Nearly 2.5 quintillion bytes of data was generated on a daily basis in 2020. With data being at the forefront, it is essential to remember that eventually data is as data does. Meaning, the true power of data will be realized only by what it achieves. Only by leveraging the scope, magnitude and exponential rise of data can we generate insights and tell apart the leaders from the laggards. As it stands, most companies recognize the potential of data but they still struggle with mobilizing it for meaningful impact. It is in this context that automated data pipelines become such an integral element of the conversation. Towards An Automated Data Pipeline? In the simplest terms, a data pipeline is the process by which raw data is moved from a source to a destination after simple or complex transformations are performed on it. When it comes to cutting-edge technologies, fully automated data pipelines may not seem like a priority. However, if you want to unlock the full potential of your data universe by extracting business intelligence and real-time insights, you need better control and visibility into your data source and destination. Developing a true data-driven ecosystem comes when you can extract data from its source, transform it, integrate it and analyze it for business applications. There’s eventually more to it than making the data people happy– disparate data can hold the entire company back. This is corroborated by the fact that 55% of B2B companies say their biggest challenge lies in leveraging data from disparate sources in a timely manner. Further, up to 80% of analytics projects still require manual data preparation and ingestion. According to 76% of data scientists, preparing their raw data for analysis is the least enjoyable part of their job. However, while the challenges of manually extracting data from various applications, transforming formats with custom code, and loading them into siloed systems are real, they can hardly be set aside. As businesses move from managing data to operationalizing AI, Gartner estimates a 500% increase in streaming data & analytics infrastructure. Furthermore, the current wave of supply chain disruption is forcing several companies to automate their data pipelines for better insights and visibility. Why Invest in Data Pipeline Automation? There can be different types of data pipelines like batch, real-time, cloud-native and open source. More broadly speaking, they can also be manual or automated. Automated platforms facilitate the implementation of even the most intricate data management approaches. You no longer have to worry about internal deployments; instead, you have access to a seamless, end-to-end environment for data collection, cleansing, and processing. You can diversify your data sources without worrying about data silos. An automated pipeline would simplify the process of maintaining and monitoring custom scripts to automate big data processes, cut operating costs, and connect all the technologies in your stack seamlessly. It is also less error-prone and provides a unified and centralized view of the entire process. Some of the major benefits include: Improved efficiency: Automating a company’s big data pipeline allows you to redirect up to 20% of engineering staff to more value-added activities. It also enables you to accelerate the implementation of big data projects, replace manual scripting with automated workflow management and data integration, reduce development time, eliminate coding errors, and provide faster business processing. Consolidated view: Automation also provides a consolidated view into workflows and real-time data visualization. It maximizes the performance of service-level agreements and enables IT to identify and correct potential issues, monitor and quickly identify the root cause of errors and failures, streamline various processes and consolidate steps. Superior BI/ Analytics: A fully automated data pipeline design enables your organization to extract data at its source, transform it into a usable format, and combine it with data from other sources, thereby increasing data management, business intelligence, data processing, and real-time insights. Dark data profitability: Gartner defines black data as “information assets (that) organizations collect, process and store during regular business activities, but fail to use for other purposes.” 7 Utilizing business intelligence and customer insights empowers businesses to generate revenue from dark data by strategizing and optimizing internal processes. Increased data mobility: Data can be moved quickly across applications and systems in real-time with a fully automated data pipeline. Data pipelines deliver key performance indicators and other metrics for marketing, sales, production, operations, and administration. Sharper customer insights: Full automation of the data pipeline eliminates the need to code or format data manually, allowing transformations to all take place on-platform, enabling real-time analytics and granular insights. Integrating data from different sources produces better business results. Compatibility with cloud-based architecture: 90% of advanced analytics and innovation will be carried out in the cloud by 2022. Cloud-native technologies give businesses the flexibility to grow and adapt to changing conditions quickly. Data pipelines will become even more critical as new technologies emerge on the edge. The Future of Data Pipelines As businesses place greater demands on their pipelines, their construction and deployment will become easier. While designing a data pipeline architecture today requires assembling separate tools for data integration, transformation, quality and governance, the industry is rapidly moving to a scenario where everything can be bundled into an integrated pipeline platform for corrective action without the need for intervention. At Valiance, our best practices have helped scores of clients leverage the benefits of data pipelines and  realize tangible benefits. Get in touch with us today to find out how we can use our AI and Analytics expertise to design the ideal pipeline architecture for your business environment.

Every year, supply chain disruptions cost businesses an average of $200 million. The present world situation, be it the COVID-19 pandemic or the Suez Canal choking in 2021 to the ongoing political war, has exponentially increased the risks of these disruptions. Market volatility, supplier inconsistency due to political and geographical barriers around the world, COVID and the war-hit workforce, and working in a new standard setup have hindered the regular flow of supply chains. This situation demands the supply chain to be more agile and well connected as fast as possible. The rising demand squeezes the scope of any errors and needs more accurate demand forecasting to lower the loss rate. In recent years, most leading companies have started implementing AI/ML to leverage speedy decision making, accurate demand forecasting, better inventory management, speed in operations, dynamic logistic system, and delivery control. In a survey, Gartner said that the usability of AI and ML would double in the next five years in the supply chain. Again, as per another study, Global spending on IIoT Platforms is expected to rise from $1.67 billion in 2018 to $12.44 billion in 2024, representing a 40% compound annual growth rate (CAGR) over the next seven years. Considering the vastness and increasing complexity of the supply chain network, it’s the need of the hour to implement an automated system to manage the entire network better. This article will briefly discuss the change in the supply chain landscape after the pandemic, the implementation of AI/ML in the supply chain and their benefits and challenges, and the last use case of AI implemented in the Supply Chain. Aftermath Of Supply Chain Landscape Post COVID Pandemic COVID-19 pandemic has exacerbated the pre-existing challenges of the logistic and supply-chain industry and added a few more to that list. It changed the fundamental consumer behavior and demanded the adoption of agile ways of working.  In a survey by EY USA conducted in 2020, 97% of industrial companies revealed that the pandemic had a severe negative effect. Few sectors of the industries, e.g., the life sciences sector, did report a positive impact on their businesses during the pandemic. For instance, 11% said that their customer demand increased by 71%, and the rate of launching new products to the market increased by 57%. But the challenge is that these companies had to increase their essential product creation, e.g., COVID 19 vaccine, twice as much as before. Source: EY USA So overall, the pandemic demanded more resource power, intense inventory management, and, more importantly, accurate predictive analysis of the market. Next Step for Supply Chains:  From the EY conducted survey, 60% of the executives conveyed that the pandemic catalyzed their strategic needs for the supply chain. The future supply chain demands agile, flexible, efficient, resilient, and digitally networked. The pandemic has pushed many sectors to the digital platform and workforce to work remotely. On the other hand, it made onsite resources mandatory to adopt the COVID-19 norms and work in the new normal. The survey showed an increase in automation and AI and machine learning investments, with 37% already deploying these technologies and 36% planning to do so soon. Moreover, digital and autonomous technologies will assist in making people’s jobs more accessible and the supply chain more efficient and optimized. How Can AI / ML Help ? Implementing AI/ML in the supply chain has numerous benefits. Some of the significant benefits are: Predictive Analysis: Demand Forecasting uses the power of automation to analyze all the data that the organization can collect, from demographics to price changes to consumer sentiment, and make sense of it against the sales history. Companies can use machine learning models to enjoy the perks of Predictive Analysis for demand forecasting. These patterns analyze the historical data to identify the patterns. So, in the supply chain, the models can be used to find the issues before any disruption is caused. A solid supply chain forecasting system means that the company has the resources and intelligence to respond to emerging issues and threats. Furthermore, the effectiveness of the response grows in direct proportion to how quickly the business can respond to problems. Optimized Inventory Management: An appropriate AI/ML model helps any company manage the over and understocking problem, thus improving inventory management. It can analyze the customer and market demand from the survey data and enable continuous improvement in a company’s efforts to meet the desired level of customer service at the lowest cost. You can also use AI and ML to analyze large data sets much faster and avoid human errors in a typical scenario. Avoid Forecast Errors: The AI/ML algorithm helps organizations deal with large data sets. The data processing is done with tremendous variety and variability. IoT devices, Intelligent Transportation Systems, and other powerful technologies enable the supply chain to gather massive data. The subsequent model helps companies have better insights and achieve more accurate forecasting, preventing enormous disruption or loss. According to a survey by McKinsey, AI and ML-based supply chains can reduce forecasting error by 50%. Improve Supply Chain Responsiveness: To minimize the cost of improved customer experience, most B2C companies are implementing AI/ML models. The AI/ML model induces automatic responses; AI chatbots help serve the customers promptly and thus handle demand-to-supply imbalances. The data analysis power of the ML model from the historical data helps the supply chain managers to understand the customer demands and also helps in better planning of vehicle routes and goods delivery. Thus it reduces the driving time and cost and enhances productivity. Challenges To Implementing AI/ML In Supply Chain: COVID-19 pandemic has exacerbated the pre-existing challenges of the logistic and supply-chain industry and added a few more to that list. It changed the fundamental consumer behavior and demanded the adoption of agile ways of working.  In a survey by EY USA conducted in 2020, 97% of industrial companies revealed that the pandemic had a severe negative effect. Few sectors of the industries, e.g., the life sciences sector, did report a positive impact on their