Why Your Supply Chain Needs to Know The Difference Between Demand Planning and Demand Forecasting

Imagine you are a supply chain manager who has done everything in your power to develop and sell new products that have now taken off in the market. However, the demand planning process you enacted for the product was forecasted inaccurately and now you’re running low on the inventory – which means you are missing out on a major revenue opportunity. This happened recently to Sony when the PlayStation5 was released late last year, costing the company millions of dollars in missed revenue. The problem with this situation and a problem that many companies face today, is that managers put a lot of emphasis on demand forecasting, but fail to successfully implement demand planning. 


Demand forecasting is the process of predicting demand based on historical data and patterns, while demand planning begins with forecasting but then goes a step further and takes into consideration many other aspects that are important in order to get an accurate prediction – like distribution, seasonality, where the inventory will be housed, where it will be sold, external factors like a global pandemic, etc. 


So What Exactly is Demand Planning? 


Demand planning is a supply chain management process of predicting or estimating how much inventory your customers will buy from you to help businesses meet customer demand for products while minimizing excess inventory. It involves creating a demand plan based on a statistical forecast that takes into consideration many factors that can influence demand – such as inventory levels, marketing strategies, buying trends, etc – and then details where to distribute the products in order to meet the anticipated demand.    


Why is it Important? 


Demand Planning is important because it can lead to an increase in profitability, customer satisfaction, efficiency and so much more. Projecting sales and planning for peaks in customer demand is crucial to keeping customers happy and maintaining a successful business. Poor planning can have far-reaching negative consequences on a business’s growth, operations and brand – and can even lead to a loss of customers. 


According to research by EnsembleIQ, 34% of supply chain professionalsreport a lack of demand forecasting and planning accuracy. This challenge is consistent across professionals in every sector. In fact, 6 in 10 retailers report that they are taking steps to improve their inventory management through the use of demand planning software. 


How Can AI Improve Demand planning? 


The main goal of demand planning is to maintain the right amount of inventory to meet customer demand without incurring shortages or wasting capital on producing and storing surplus inventory. A key element to ensure this is data collection. Advanced machine learning makes access to real-time data increasingly available, improving forecast accuracy and thus demand planning immensely. It also provides collaboration tools that enhance the ability of planners to share information amongst one another and react more quickly to changes that occur in supply and demand. 


Demand planning solutions are increasingly being used across a large variety of industries – healthcare, e-commerce, food & beverage, automotive, retail and many more. According to recent research, increasing demand planning accuracy can increase revenue by up to 3%. Additionally, according to research by the Aberdeen Group, companies that are able to produce accurate demand plans are 7.3% more likely to hit their quotas and are 10% more likely to make improvements to their bottom line. While implementing demand planning solutions such as AI-driven software may be an adjustment and investment for a business, owners can expect to see a return on investment in less than a year.


To discover how your supply chain business can improve profitability and efficiency through the use of AI-driven demand planning, contact info@conversight.aior request a demo

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

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

3 Ways Artificial Intelligence Addresses Critical Areas in Supply Chain Response Planning

Response planning enables businesses to accommodate changes in demand through making production adjustments. The COVID-19 crisis caused unanticipated shocks to the supply chain, triggering the need for stronger methods of response planning.


The use of traditional models, such as spreadsheets, has proven to be ineffective when adapting to the current needs of response planning. These models are unable to react to fast-moving data, causing lag in forecasting. Forecasting is used to improve decision making quality – therefore inaccuracies in forecasts lead to substandard decisions. Through the use of artificial intelligence, creating improvement in problem areas of response planning leads to better accuracy, effective use of limited resources, and more collaborative decisions.


According to Gartner, three areas of improvement in response planning through digital supply chain forecasting are:

  1. Variability and Bias – Using digital technology to avoid variability and reduce human bias
  2. Data – Using digital technology to eliminate missing, wrong, or out-dated information
  3. Model – Using digital technology to improve accuracy or scope of the current model being used


In order for businesses to improve operations and response planning as a whole, the implementation of AI is imperative to keep up with trends in today’s society.’s artificial intelligence business assistant, Athena, turns data into proactive insights to optimize the supply chain and give real-time visibility that ultimately creates more efficient routes to gathering critical information needed to make accurate and informed decisions.


Athena addresses the critical areas in supply chain response planning through her ability to:

  1. Flag inconsistencies in inventory which reduces variability and eliminates unnecessary costs
  2. Provide real-time reporting to reduce the usage of stale data
  3. Create recommendations based on the tracking of trends in the supply chain


As businesses implement artificial intelligence in their response planning methods, there will be a continuation of better accuracy, stronger decision-making, and smoother reaction times, which will ultimately reduce costs and drastically improve the function of the supply chain.


See how can provide actionable insights to improve response planning in your supply chain:

The Supply Chain Control Tower: Why They’re Critical in 2021

The COVID-19 crisis prompted the need for superior visibility and agility in the supply chain, due to unforeseen movements in demand, sales, and production. Now, more than ever, it is necessary for businesses to have access to a 360 degree view into all areas of the supply chain. With the the ability to track materials, know the number of items available, and see what items sell quickest, companies are able to stay on top of demand and improve end-to-end visibility. According to Gartner, the implementation of control towers in the supply chain leads to more transparency and better coordination, which results in lower costs and higher efficiency.


Supply chains today are being faced with mass amounts of data, increasing the need for stronger methods of analyzing, decluttering, and organizing the inflow of information. The power of artificial intelligence creates critical visibility that helps businesses predict and respond to fluctuations in demand.


Lack of visibility stems from a variety of challenges. Entrepreneur states that around 44 percent of businesses have not developed a clear strategy to deal with disruptions in supply chains. Common obstacles include issues such as lack of planning, integration, and execution, technological constraints limiting useful insights, and the inability to predict and prepare for future risks.’s AI business assistant, Athena, addresses this data problem by seamlessly delivering actionable insights. Equipped with an intelligent supply chain control tower, Athena improves a large variety of capabilities, such as:

  • Tracking end-to-end performance in one place
  • Delivering insights from a centralized data repository
  • Proactively developing automated smart reporting
  • Enabling complete visibility for the AI-driven supply chain


From SDC Executive, it is stated that AI technology has enabled a performance leap in control towers that is virtually impossible to achieve with traditional software. Instead of only being provided decision-support, businesses can now access autonomous control through actual decision-making.  By creating an AI-powered intelligent control tower, businesses are not only improving decision-making, but also attaining stronger automation and more knowledgeable teams.


To learn how your organization can gain more control and visibility of your supply chain, schedule a demo today or visit: