Augmented Experiences are Shaping the New Supply Chain

As supply chains become more complex, they create the need for more streamlined approaches for reporting and analyzing. As businesses react and evolve to the long-term effects of the COVID-19 pandemic, efficiencies in operations and analytics are brought into focus revealing an overwhelming lack of timeliness, cost-efficiency, and organization across supply chains. Gartner predicts that by 2023, “overall analytics adoption will increase from 35% to 50%, driven by vertical- and domain-specific augmented analytics solutions.” Read on to learn how augmentation, a performance of artificial intelligence, is a front running technology in aiding supply chains in their digital transformation.


Today’s traditional reporting software is unable to keep up with the demands of fast-paced operations. Most reporting tools require a developer to configure reports, which leads to added costs and unnecessary time in waiting for clear and succinct results. With the introduction of augmentation in these processes, supply chains are able to automate the supply chain to develop reports, automate time consuming, repetitive tasks and eliminate countless hours and costly resources.


Supply chain planners typically analyze business’ inventory, as well as supply and demand lead times, to make more informed, educated decisions. Artificial intelligence agents, like’s Athena, augments this analysis to provide recommendations on purchase orders, inventory levels and more.


Once reports are developed, decision makers are able to easily and quickly to gather insights and summarized information on their supply chain to answer user questions such as:

  • “Did my price increase?”
  • “How many orders did my customers purchase?”
  • “What are our daily sales?”


Additionally, the augmentation of business user’s analytics allows for proactive monitoring of specific inquiries and data. Issues or anomalies are caught early on before turning into costly, long-tail errors.


Human reporting is becoming a tool of the past, as the automation and augmentation of the supply chain through artificial intelligence creates more efficient, smoother transitions for the process of reporting, analyzing, and recommending. Additional research by Gartner, states “organizations that seize the opportunities presented by the newly catalyzed market will be able to dramatically hasten their analytics-related maturation, which could potentially enable them to make competitive breakthroughs, in comparison with slower-maturing rivals.” How does your supply chain shape up to the defined status quo?


To learn more about ways your business can improve reporting times and optimize operations, visit

Conversational Intelligence | What You Need To Know

Last week, Microsoft took on a $16 billion dollar acquisition of Nuance, a speech recognition company. Conversational AI has the ability to transform the way people interact with technology, as the adoption of this speech-recognition software encourages the streamlining of data management.


Strategic acquisitions such as these are especially significant in the world of speech and language recognition software and its impact on the businesses across verticals and industries. As larger-scale companies, such as Microsoft, begin to implement speech recognition software into their own companies, it points to greater growth and development within the industry. Not only will the use of AI continue to increase in big-name businesses, but attention will continue to be drawn to the exponential power of conversational AI and its applicability to the everyday user across businesses of all sizes.


Conversational AI interfaces understand the context and intent of business questions by leveraging machine learning, data science, knowledge graphs and cognitive techniques. Enterprises are then able to create advanced dialogue systems that utilize memory, personal preferences and contextual understanding to deliver a realistic and engaging conversation with data sets.


Success in deploying conversational AI in the business needs key skills, not just technical, but mostly business domain & subject matter knowledge. Conversational AI bots need to understand the domain and speak the domain, not just English. That’s where the domain-focused business platforms like, which is empowering Supply Chain leaders, are emerging.’s powered intelligent business companion, Athena, understands user behavior, context and intent of the conversation to deliver personalized insights and action. There are two primary technology components to the Athena platform 1) the data ingestion and knowledge creation and 2) the process by which end-user queries are answered.’s characteristics includes:

  • Connect to multiple data sources, both structured and unstructured
  • Integrate with online applications through web services and APIs
  • Access to insights through narratives and questions (e.g., supply chain metrics)
  • Uncover data anomalies through data science
  • Provide personalized proactive-insights
  • Retrieve information (e.g., product searches)


To discover how your supply chain business can improve productivity and efficiency through the use of speech and language recognition, contact or request a demo. Receives New Patent for Contextual and Intent Based Natural Language Processing System and Method

As businesses gather vast amounts of data, they suffer with the problem of being able to extract value from the available information. Artificial intelligence-driven solutions have the ability to understand, organize, and comprehend this data that ultimately increases usability for the customer. has developed a contextual and intent based natural language processing system and method, patented this month, to enable businesses to optimize their supply chain through conversational AI.


While most businesses have successfully connected to their data, the challenge still lies in accessing it and truly leveraging it for decision making. This patent is a huge step in for creating an accessible, user-friendly interface with data, allowing businesses to glean insights like never before using the words and channels they are most comfortable within.


ConverSight’s patent accomplishes three main tasks in understanding and processing natural language questions:


  1. Ability to learn and understand in a zero shot or few shot learning setup.
  2. Ability to use layered knowledge to derive new knowledge while interacting with users to learn users behaviour pattern and provide personalization
  3. Ability to understanding the user’s intent and context from previous interaction to  provide insights through contextual learning centered around specified metrics


So what does all of this mean?’s natural language processing system connects to businesses’ data sources, then transfers the data to’s built in AI system where it can be understood by simply loading it in. The AI system picks up on the vocabulary used throughout the data, making note of it for future use.


Following the connection to the data source,’s AI technology extracts the critical parts of the data, creating charts and graphs depicting the information. These knowledge graphs allow users to have access to easy-to-understand data, without the struggle of mining the data and correlating insights themselves.


After extraction and usage of data,’s natural language processing system then uses this information to make future predictions and recommendations to the user (i.e. proactive recommendations, suggested search queries, proposed next actions). The technology is able to understand common metrics that the customer desires, and uses contextual learning to propose crucial data for future use – a capability that has not previously been successfully developed in conjunction with natural language processing.


While most businesses have successfully connected to their data, the challenge still lies in accessing it and truly leveraging it for decision making. This patent is a huge step in creating an accessible, user-friendly interface with data, allowing businesses to glean insights like never before using the words and channels they are most comfortable with.


To learn more about the patent or, contact or request a demo.

How to Manage the Shift from Purchase Order-Driven Demand to Digital Demand

As the supply chain continues to become more complex, the current methods and tools to monitor and plan the business is insufficient.  With unplanned fluctuations in demand experienced during the COVID-19 crisis, the demand driven supply chain is becoming a critical aspect of maintaining a sustainable and responsive flow of operations.


Increases in e-commerce demand and contact-less shopping lead to less inventory at the retailer side, adding more pressure for manufacturing companies to deliver in a very short cycle time. DigitalCommerce360 reports that COVID-19 boosted e-commerce sales an additional $174.87 billion, allowing it to reach $861.12 billion, which was not predicted to be met until 2022. In order to manage this new demand in the supply chain, responsiveness and agility are key.


So, what is a demand driven supply chain? Imagine a network that reacts to real-time demand, rather than remaining reliant on historical patterns and stale forecasted data. The use of real-time data allows for quicker, agile, and more effective reaction times, which is critical when unexpected events occur.


Companies who have adopted demand-driven methods of planning have seen increased revenues of 20-30%, as well as the cut of logistics costs from 5-25%, based on research from SupplyChainBrain. Demand driven supply chains are focused on 3 fundamental things:


  • Creating a system of faster, streamlined information sharing, based on the usage of demand signals from customer demand
  • Interconnecting operations throughout the supply chain, allowing for information to move quicker and speeding responsiveness
  • Automating repetitive tasks and reporting with artificial intelligence to create their own demand driven operations.


Forbes predicts that in 2021, “we’ll continue to see a greater shift to more resilient digital supply chain models as businesses focus on expanding or transforming capabilities to increase flexibility, visibility and control.” Supply chains are better able to address e-commerce driven demand changes as they shift toward a focus on better resilience.


Context based hypothesis and forecasting delivers dynamic demand and supply forecasts, in addition to automating the procurement process to maintain optimal inventory.’s contextual decision intelligence platform uses machine learning and artificial intelligence to navigate planned and unplanned demand with data captured in real-time and continuously – allowing manufacturers to accurately forecast and plan like never before.


Artificial intelligence is a foundational capability of this shift, connecting data throughout the supply chain, and creates relationships through data monitoring for  personalized insights. With a more attainable implementation of AI, businesses are able to create a demand driven supply chain, allowing for better accuracy, stronger decision-making, and smoother reaction times, which ultimately reduces costs and drastically improves the function of the supply chain.

Top 3 Trends in Data & Analytics that your Supply Chain Needs to Know

Progressing forward into 2021, the involvement of data and analytics (D&A) in the supply chain is quickly moving away from the backburner, towards becoming a core business function. With more emphasis than ever on building resilience to better manage disruption, access to data and analytics comes into focus. In fact, according to Gartner, in comparison to IT, a higher proportion of businesses have increased spending on data and analytics due to the COVID-19 crisis.


The use of D&A has the potential to address disruption and digital transformation, leading to the optimization of business processes. Many popular D&A trends that were already gaining recognition, such as the connection between AI and decision intelligence, have only increased in relevance due to the pandemic. These innovations continue to push for new changes in D&A, and continue to be accelerated. Read on to learn the top 3 things about data and analytics (D&A) that are going mainstream across supply chains.


The Smart Supply Chain


In order to accelerate change, businesses must take on AI systems that prove to be scalable, as well as smart. The availability of information in the supply chain continues to grow, and the implementation of AI allows businesses to adapt to constant changes in inventory, demand and supply. Shorter cycle times to evaluate value is an important concept in the growth of AI, as it quickens the application of AI and leads to greater accessibility to a wide-audience of users across the organization.


Generating Improved Business Value


Businesses are going to continue searching and pushing toward innovative methods of creating value with the hope of developing faster actions, higher-level visibility, and having access to more insightful data. In order to achieve this, decision-making must be taken into account, as 65% of decisions made are more complex today than they were two years ago. Engineering stronger methods of decision intelligence into a business helps combat these complex choices and streamlines the decision-making process, leading to improved business value.


Data Sharing


Information or data sharing is critical to drawing the connection between data and insights, as well as utilizing the right channel to relay this information to the necessary audience. Graphing technologies are able to converge the gaps between data, and conveys information in a manner that can be easily distributed across users, teams and departments. Gartner predicts that by 2025, graph technologies will be used in at least 80% of data and analytics innovations. This adaptation provides businesses with better access to their most critical insights through easy-to-read data visualization.


Interested in learning how your supply chain can be on its way to positioning data and analytics as the core of your business? Visit to learn more.

Decision Intelligence: The Future of Supply Chain Decision Making

In the growing world of supply chain, industry leaders must look to innovative methods of development to advance the decision making process in their businesses. Decision Intelligence combines elements of decision management and decision support with artificial intelligence to create more efficiency, promote sustainability, and improve predictability and alignment. With the shakeup of 2020, decision intelligence has come into focus as supply chain leaders’ decisions become ineffective due to their execution time – taking too long (Gartner). With the infusion of artificial intelligence, we are able to create stronger connections throughout the actual process of making a decision leading to a better overall outcome.


Concerns with current decision making models


In a survey of 1,200 business leaders, those in the C-Suite said they spend more than 30 percent of their working time on decision making. With greater emphasis on urgency than ever, decision making speed is no exception. By constructing higher level decision intelligence, businesses are able to combat the ineffective points of the decision making process such as:

  • The time span endured to execute a decision, as it can often prolong to a point where it becomes detrimental and data expires
  • Unpreparedness for the outcomes of decisions being made
  • Disconnect between the steps of the decision-making process, leading to combatting results


The basis of decision intelligence


When implementing decision intelligence, there are three principles to follow as outlined by Gartner: relevance, transparency, and resilience. These principles go hand-in-hand with the decision intelligence model created by Gartner, and lead to more sustainable decisions that are likely to lead to more success.


Relevance refers to ensuring that every decision being made has a contribution to the final goal of the process. When decisions remain relevant to the goal, it can help combat the issue of decision-making process lasting longer than anticipated, and delaying final results. Relevant decisions lead to more timely actions.


Transparency involves creating clarity in the outcomes of actions, as well as clarity between those involved in the decisions. With the use of artificial intelligence, it allows for clearer predictions and implementations of decisions being made.


Resilience is immensely important in any decision-making process, especially in today’s society, as we have seen how unpredictable events can affect businesses and the supply chain. Creating decisions that are resilient allow for more security, and therefore more intelligence.


How supply chain leaders can make better decisions


As decision intelligence expands, AI Multiple predicts that businesses will be impacted as:

  • Managers will be better supported through the power of AI so they can make improved decisions with better insights and more accurate results
  • “AI agents can make decisions of their own,” which leads to better timeliness imposes stronger decision intelligence on the supply chain through an AI assistant, Athena, who understands user behavior, context and intent to offer personalized insights and actions. The insights provided are used in the decision-making process to increase intelligence and create sound outcomes.


Regarding transparency and the automation of data sharing, has an option to provide the insights embedded in a webpage, and can be shared with anyone inside or outside the organization through a simple conversation.


As a response to the unprecedented disruption brought on my unpredictable changes in customer demand, launched a COVID-19 initiative to directly address these challenges and increase resilience and relevance through:

  • Decluttering and achieving visibility across distributed data sets to see inventory levels, price fluctuations, customer demand and stock levels
  • Identifying and adjusting supply and demand to optimize cash flow


To learn how your supply chain can begin growing decision intelligence, visit