Blog Type: Blog

Even the most seasoned demand planners have struggled against unprecedented challenges in recent years. An increasingly accessible global supply chain, massive increases in the availability of consumer data, and other continued complications have brought rapid change to the process of delivering products to consumers.

To meet the changing demands and continue to thrive, organizations need to find a way to align themselves with the permanent changes in the world and acquire the capability to translate them into insights. Unlocking the human planners’ full potential and continuing to provide the best service for companies’ users is only possible by putting these planners in control of the latest evolutions in AI and machine learning (ML).

Opening the Black Box for Demand Planners 

The limited capabilities of the current demand planning, forecasting tools & processes unnecessarily restrict planners. Without adequate technical support, they do not have the bandwidth to effectively apply their knowledge and intuition to meet today’s supply chain demands. Moreover, when the results are black box and non-explainable, they become difficult to be executed given within the ownership and accountabilities of the responsibilities of different parts of the organization.

Meanwhile, the latest evolutions in AI and ML have unlocked previously unheard-of data-processing and forecasting capabilities. However, without proper application and human guidance, there is no way to utilize these insights within an organization effectively.

The solution lies within the union of experienced, adaptable demand planners to user-friendly AI/ML systems to approach demand planning. This technological revolution will allow organizations to push beyond static and inflexible forecasting processes that rely too heavily on a now irrelevant past, and remove the need for implementation by handing the planners to power and capability to adjust data, processes and algorithms to align with the changes at hand.

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Taming Supply Chain Bullwhips with DemandAI+

Bullwhip effect is a common term used in supply chain to describe how a small change in consumer purchasing pattern or a change in any demand pattern that can cause significantly large impacts to supply. It is an analogy that transcends from the pattern in which a cowboy slowly turns the whip until it gathers enough momentum to crackle and emit a sonic boom, which ‘tames’ the cattle it is intended for.

A turbulent climate for global supply chain

The world has had many more examples of classic supply chain bullwhips in a short time compared to any other time in recent history. We had the infamous toilet paper outage that began with consumers detecting small shortages of paper rolls, which triggered a paper shortage at the manufacturing companies. This led to hoarding behaviors that exacerbating the shortage and signaled manufacturers to produce more. The paper shortage recovered quickly within 2-3 months of the start of pandemic, but the nationwide hoarding behaviors lasted half a year into the pandemic triggering excess production and at the end, a huge surplus of toilet paper rolls in the United States.

There is a similar problem brewing beneath the surface right now, with the over-abundance of global freight containers and slowing demand of freight trucks. Retailers across the world have surplus inventory of nearly every kind of consumer goods – clothing, electronics, housewares. So much so that some big retailers are allowing customers to keep their returns instead of adding to the already excess inventories. With the higher oil prices and softening demand for freight trucks, the high demand for freight containers across global and local supply chains is waning. This is from the bullwhip effect of the pandemic when consumers limited purchases to essentials. With all the ensuing demand once vaccines became available, manufacturers overproduced inventory, causing excess stock. All of this is happening at a time when consumer sentiment is not very positive from the speculation of impending recession. Each one of these events has a ripple effect, contributing to the bullwhip.

Early detection of anomalies with DemandAI+

Stepping away from the granularity of these supply chain breakdowns, what can we do to prevent, or better control these bullwhip effects in the future? Adoption of artificial intelligence to build demand forecasts is a very powerful, less explored solution to control the bullwhip from ballooning to proportions hard to manage and costly to control. AI models have evolved significantly since relatively inexpensive cloud data storage became more available. That makes it possible for organizations to collect, process, and store useful data at micro levels. Excel-based tools lack these capabilities. These models can detect subtle anomalies in consumer sales data a lot faster and with improved accuracy. Therefore, if the demand for toilet paper ever goes up unusually again, AI will make it possible to trigger demand forecasts that consider the short-term effects of hoarding behaviors. Longer term demand enables more predictions of realistic consumption, which can prevent over-production, and over-stocking.

Traditional demand forecasting uses historic sales data to model future sales. Historic data, is full of exceptions, misleading signals, and more volatility. To factor in historic sales, without carefully interpreting short-term consumer behaviors, or orders being placed by B2B businesses would be irresponsible and short-sighted. DemandAI+ is a powerful platform that can help companies build proactive supply-chains.

Key Takeaways: AI for planners and supply chain professionals

In summary, AI-first in demand forecasting can help significantly by:

  1. Catching macro & micro-trends in consumer behaviors quickly and accurately
  2. Removing the sole dependency on historical sales in the face of volatile business landscape
  3. Reducing reaction time to adapt to drivers of change in demand, therefore directly impacting the bottom line of organizations

There are proven AI solutions in the market right now that consistently raise the bar for planning functions. If you have not explored them yet, no time is better than now. Ready to get started? Contact us.

In today’s fast-paced world, demand planning has become increasingly complex, and traditional demand planning systems have struggled to keep up.  

SAP IBP is a powerful tool that brings together all the elements of large organizations, including supply chain management, sales, finance, and operations, into one integrated platform. 

By consolidating data from multiple sources, SAP IBP allows organizations to gain a clear understanding of their supply chain and demand planning processes. This, in turn, enables organizations to make informed decisions about inventory management, production planning, and resource allocation. 

However, while SAP IBP is an excellent solution, its demand planning component has been underserved. This is where Logility’s DemandAI+ comes in. Logility is an AI-based supply chain planning platform that provides organizations with the intelligence and insights needed to improve their demand planning processes.

One of the key benefits of Logility is its ability to break down the demand into its component parts. By analyzing historical sales data and external factors such as economic indicators, Logility can identify the underlying drivers of demand. This, in turn, enables organizations to make more accurate forecasts and better understand the impact of different factors on demand. 

Planners love an intuitive user interface. By allowing planners to easily tag outliers and interact with the data directly, Logility enables organizations to leverage the expertise of their planning teams while still benefiting from the power of AI. This approach allows organizations to improve their demand planning processes without having to completely overhaul their existing workflows.

Perhaps most importantly, Logility is designed to easily integrate with SAP IBP.

This means organizations can easily add Logility on top of their existing SAP IBP platform, allowing them to enhance their demand planning capabilities without having to invest in a completely new system. This approach not only saves organizations time and money, but also ensures that they can continue to leverage the power of SAP IBP while still benefiting from the added intelligence and insights provided by Logility. And to add to it, Logility can be implemented in a day if the data is available and without the need for an army of consultants.

Need more? Get a deeper understanding on our newest solutions and thought leadership in our executive briefs.

When Logility embarked upon the journey with AI-first planning and DemandAI+ we were on a mission to solve some very pertinent but complex set of problems. The founders had been in the enterprise software space and business planning space for decades and had come to the painful realization that technology was truly holding back this domain. The supply chain technology landscape was rife with decades old code; month/year long projects and IT integrators were the norm to make the tiniest changes in the systems or parameters.

None of the user-friendliness and AI that has become common in the consumer space had made its way to enterprise business planning. How long does it take to get used to your latest iPhone or Android device or use a new app? Compare that to how long it takes to onboard to a supply chain planning tool!

AI-first planning

Problems and Solutions

We knew the problems well – history is not always the best predictor of the future, but every forecasting tool out there could do little more than project historical trends and seasonality forward. There was simply no way to do some very basic things in current tools. A few examples:

  1. Adding planner knowledge about events or unique business scenarios to the forecasting process in an easy way (yes, manually restating history is not the best way!)
  2. Test assumptions about the future and understand impacts in terms of volume/value – what if we have another covid wave in the winter? What if consumer prices rise by 10% owing to fuel shortages? What if we invest $100K into our advertising campaign?
  3. Make sense of current information that’s already available in internal and external systems – Customers place orders every day. Does the forecasting tool use this info to calculate demand? Almost none do. End consumers are placing orders on Amazon and are shifting buying behavior. Again, hardly any B2B forecasting tools can even take sell out, point of sales, and channel inventory as inputs. What about sales inputs? There is a ton of sales intelligence in your CRM tool or in the heads of your sales force and that the forecasting system doesn’t even bother with.
  4. And last but the most important of all – get a simple but effective understanding of what makes up the forecast. OK, the forecast tells me I will ship 1000 units next week, but what is the baseline component? What is the uplift of the promotion that I did? What about my customer’s sell-out, and how much does that drive my sell-in?

So how did we use DemandAI+ to change the status quo? We went on to create an AI-first forecasting environment – potential clients come to us with some very basic data, tell us about your business objectives and the very next day you are live with a ton of insights into your demand. Then it’s a matter of testing assumptions, adding additional data and insights and creating a rich picture of not just what will happen, but also get a much better sense of why it will happen. You need to know the risks and opportunities that you are faced with, and more importantly – (because you know the components of demand) what can you do as a business to shift and shape your demand. And you don’t need to run implementation projects to do any of these, you have the power and control over the numbers, and you feel confident to explain it to others in your organization.

Welcome to the new world of DemandAI+! Did you catch our webinars on this? If not, sign up for your free, on-demand recording to dive into the details.

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Supply Chain Planning: The Gratifying Career Choice

This is that time of the year when many people around me are getting ready to send their children off to college. That, combined with the last two years of massive supply chain breakdowns keep raising a question in my mind from time-to-time. Why don’t more kids want to grow up and solve the complexity of global supply chains? Why are young adults not opting to make a career out of optimizing the world’s supply of pretty much everything? The more I think about it, it boils down to the lack of awareness about what it takes to tread the supply-chain path.

Supply Chain Planning Career: An Increasingly Complex Field

It is quite natural for a youngster to draw inspiration from a childhood physician, or a news headline about a rocket launch or a classroom visit by an army vet to think that he/she would like to become a doctor or a scientist or a soldier. However, much of the work to lay out supply chains happens behind the scenes. All one sees is food on the table, the next new gadget, trending fashion – all of these have much to do beyond the farmer who grew the produce or the assembly line worker who put the gadgets together or the machines/people who cut/knit/sewed the fabric. There are many disciplines, like science/engineering, marketing, finance, and operations, that come together to put the end-product in the hands of users. Add that to some of the immediate needs-of-the-hour, i.e., making our planet greener, to incorporate sustainable practices so the world’s population does not deplete resources.

Supply chain problems are far more complex today than they used to be even twenty years ago when the world was more localized, there were a billion fewer people on earth, and trading across geographic boundaries was a lot more prohibitive.

Over the last decade, technology has made noticeable strides in many aspects of our lives. A good example is accelerated adoption of automation, whether we know it or not. We buy our necessities online twice as much as we did before, we no longer need travel agents to book our trips, and our money transactions do not need a trip to the bank anymore. However, most of the complex web of global supply chains are still planned through excel spreadsheets. What gives? Much of it has to do with not enough awareness among talented college students and young adults about how their math, science, finance, or social skills can be applied to this much-needed area of our lives. In the US, there are only around 150 colleges that offer supply chain coursework, including certificate programs. By comparison, that number is around 500 for engineering and marketing programs each, for bachelor’s degrees alone.

Technologies like AI Upgrade the Role of Supply Chain Professionals

Supply chains are fun to explain and game-like to navigate. There are not many career functions that bring together complex mathematical operations/optimizations with business concepts and soft skills. The role of a supply-chain planner is one such function, even if lesser known or pursued.

A supply or demand planner is central to the bottom line of organizations because their analysis and input are key to what and how much a company sells. The new world technology eco-system has paved the way for many solutions that planners use. Most of those solutions are no longer grounded in excel spreadsheets, but use state-of-art AI models, complex business insights, and scalable data solutions that make decision making for planners a lot more strategic than it used to be. It is immensely gratifying to be able to explain the complexity of everyday problems to just about anyone, because most people can relate to any current supply crisis in the world. What is lacking is adequate entry of young, high potential professionals in the field due to a lack of awareness of the possibilities of a supply chain planning career.

Engaging students to build innovative supply chains of the future

Where does the awareness begin? Well, nothing in the past has provided as many real-life examples of supply-chain breakdowns as did pandemic years. No one was left untouched by shortages of essentials during this period in one form or the other. This is the perfect time to explain the “why”.

As we dive into each scarcity we have witnessed around us in recent years, the fascinating world of supply opens up quite clearly. Some elementary and secondary school programs are starting to incorporate principles of supply chains in school curriculums through concepts like “The Lemonade Stand” that make it fun and easily explainable to children. Examples of unsustainable and irresponsible supply chains are all around us and make great yet simple thought-provoking studies. But they are not nearly adequately integrated into our educational curriculums yet.

To engage more of the younger generation in building innovative supply chains of the future:

  • More supply chain publications should be made geared towards younger school-age children.
  • Elementary and secondary schools should include programs in their curriculum to help students simulate real-life supply situations.
  • More college and university offerings should be available for blended coursework that prepare students for supply-chain careers.
  • Industries whose bottom-lines depend on supply chain efficiency should invest in funding supply chain programs in colleges to build a robust internship and training programs for new graduates.

There is a glaring shortage of strong supply-chain professionals in the world today, despite advances in technology adoption in how organizations navigate this complex domain. This poses one of many risks to the future of supply chains. Raising awareness and breaking the myth of outdated supply-chain systems is the responsibility of everyone who navigates this space as well as educators and organizations that thrive on lean supply chains.

Need more? Our eBooks, executive briefs, and whitepapers give a more in depth look at the complexities of the supply chain and different solutions to solve them. Get free access here.

When the United States had a massive infant formula shortage, families were under duress, especially those with babies under one. Anyone who has provided care to an infant knows that baby formula is as much an essential as toilet paper. The need for more resilient supply chains and better demand forecasting was becoming more essential.

With the rise of companies looking to implement AI into their supply chains, more accurate forecasts are within reach. But how did we get here in the first place?

How did the infant formula supply chain crisis happen?

Remember the toilet paper crisis a few years ago? The baby-formula shortage is perhaps only more dire for families directly impacted. In a crunch, the brand of toilet paper hardly matters to most, but the same cannot be said of baby formula. A crucial product meant to substitute a mother’s milk that has a unique composition that is significantly difficult to substitute and risky to replicate at home. This shortage is cause a frenzy among parents in the US, some driving for hours only to find empty shelves in different neighborhoods.

The FDA closed a manufacturing facility in 2022 due to bacteria-related infant deaths and recalled products. This added to the already lingering short-supply of baby formula that started at the beginning of the pandemic when parents of infants hoarded cans of formula, just like others did with toilet paper. Finally, US trade policies, including high import tariffs and stringent labeling requirements, are prohibitive towards increasing the imports of baby formula to the US in the short horizon. Even imports from neighboring Canada are subject to tariffs as high as 17.5% under the Rock-A-Bye trade restrictions.

What can supply chain planning professionals learn from this situation?

Much has been said about over-reliance on global supply chains and the need to build more self-reliant localized supply sources. But the baby formula crisis in the US built a strong case for more flexible supply chains, especially for essential goods. Over-reliance on local supply can wreak significant havoc if not built to sustain under stress. This is a moment of truth where we see that historical demand forecasting models built upon past data do not work under rapidly changing global factors. Planners need AI based tools that can overlay ground realities on to historic data. In hindsight, the baby-formula crisis was brewing for a while. Smart use of POS sales data would have shown parents’ hoarding behaviors for baby formula and helped eliminate false peaks and toughs in demand. The ability to segment demand by source of supply would have immediately caught the impact of a plant closure.

Planners’ visibility to trade policies may have helped with alternate sourcing plans ahead of plant closure. In today’s uber-connected world where data is omni-present, we need to build outside-in forecasting tools to prevent organizations from being caught off-guard when supply-chain interruptions are staring them in the face.
What did we learn from the baby formula shortage?

  1. Planners need visibility into critical supply decisions and policies and AI based tools that can help them build scenarios that impact category level demand of these essentials like baby formula.
  2. Planners need scalable and standardized mechanisms to overlay ground realities with history in order to hedge against supply disruptions at the network level. There is a need for more resilient and transparent supply chain for essentials.
  3. Organizations need to push for more liberal trade policies for essential goods such as baby formula, or at a minimum, exception clauses that loosen tariffs and label requirements at times of crisis. 

The ecosystem in which US families with young children live is already under duress due to rising costs, unavailability of childcare, unaffordable healthcare. About half the baby formula consumption in the United States is by low-income families. This can surely be one less worry for struggling parents with more deliberate forecasting and policy measures which can be elastic between local and global supply chains.

Planners need visibility into critical supply decisions and policies and AI based solutions that can help them build scenarios that impact category level demand of these essentials like baby formula.

Logility’s AI-first demand forecasting solution, DemandAI+ can help to avoid such drastic situations using more than just historic data. Read more about end-to-end supply chain planning on our website. You can also access longer form, in depth content in our executive briefs.

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What is Driver-Based Forecasting for Demand Planning? Find out in this blog from one of our experts

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Staying in control with Transparent AI

Uncertain supply chain conditions due to change and disruptions in supply and demand mark a new responsibility from the planner to leave the status quo and take the leadership to embrace technological innovations and more comprehensive data to guide their organization through this volatile environment.

This is why Logility, in every step of the development process, keeps in mind that the planner has to be the one in control, not the AI model. We understand that a seamless interaction between the user and the technology is essential to achieving this goal. We call this approach AI-first forecasting with a communication layer as the foundation that allows strengthening the interaction with transparent AI in a bi-direction way.

The planner explains events as he understands them to the system. For example, tell which sales spikes are related to which events and the system in return will also question for info on other spikes that could represent a similar event or could have a new unrelated cause to learn from.

The system tracks the decisions, learns from them, and makes them transparent, thus allowing planners to explain to other stakeholders what the decisions are built on.

New Features

In the past few months we have been building on this foundation, providing users with a clear view of the aspects that impact historical demand and the forecast:

  • How has the sales baseline evolved over time?
  • What is the impact of seasonality?
  • What are the effects of promotions and events?

We have made these effects plain to see so planners can derive and act on the causal impact.

World events, competitor actions as well as promotional campaigns are run in different ways, and therefore, to capture intelligence that impacts demand succinctly, planners need to be able to interact. In this latest release, planners can add events at any level, to train the system and respond faster to -and more accurately predict the impact of- future disruptions and volatility.

An expanded performance screen allows you to compare the final forecast against your benchmark and user input.

Learn more about the latest updates to our solutions here. You can also soak up more in depth knowledge about digital supply chain planning solutions in our executive briefs.

In a recent webinar, How ChatGPT Changes All Rules of Supply Chain Planning, we were joined by JDE’s IBP Director, Tineke Kok, and Martijn Lofvers from Supply Chain Media. We surveyed 1425 registered attendees to understand their perceptions of the impact of ChatGPT on Supply Chain Planning.

According to the survey, 79% of the respondents perceived a high or very high impact of ChatGPT on Supply Chain Planning. This highlights the growing interest and importance of Artificial Intelligence and Large Language Models in Supply Chain Planning.

With the integration of ChatGPT into the traditional planning processes, organizations can move from reactive to proactive planning, and from manual to automated processes.

The benefits of this transition include improved accuracy of forecasts, leading to optimized inventory levels to reduce excess stock or stockouts, but also an increased understanding of the demand for anyone in the organization.

The combination of insight-based planning with ChatGPT will be very powerful. Read more about insight-based planning here or watch the recording of our webinar on ChatGPT for Supply Chain Planning.

Explore more on our website about how Logility incorporates AI into the fast moving and everchanging supply chain or get in touch with our team to discuss.

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In the realm of demand planning, it is essential to understand the differences between statistical forecasts, machine learning (ML) methods, and artificial intelligence (AI). These terms are often misused, leading to confusion and obscuring the unique differences. This article aims to demystify the terminology by shedding light on the crucial distinctions and highlighting how each approach contributes to the field of demand planning.  

The Foundations: Statistical Forecasts  

Statistical forecasts form the backbone of demand planning. They rely on historical data and utilize statistical techniques to predict future demand patterns. Statistical models provide insights into historical trends, seasonality, and other relevant factors. However, they have limitations when it comes to adapting to changing circumstances and incorporating dynamic inputs. Statistical forecasts are typically reliant on human intervention and lack the ability to learn and improve automatically.  

Evolutionary Leap: Machine Learning in Demand Planning  

Machine learning methods have introduced a significant leap forward in demand planning. ML algorithms utilize historical data to identify complex patterns and relationships that may not be obvious to traditional statistical models. By automatically learning from experience without explicit programming, ML algorithms can improve forecast accuracy. ML-based demand planning systems can process large volumes of data quickly, making them valuable tools for identifying trends, detecting anomalies, and generating forecasts.  

Not all ML algorithms out-of-the-box can be applied to Demand Planning. Therefore, in some cases, these algorithms could even give worse results than statistical models. That’s why it’s crucial that these models can work in the context of the business and can be trained to understand the real business drivers and different scenarios. Otherwise, these models are prone to under or overfitting, generating large swings in forecasts, or to making recommendations that may fit the model but are infeasible or unactionable. Therefore, it requires a lot of special product & business understanding, a process we call semantic ontology, to describe the business scenarios to the models so that they are able to generate accurate, current, and consistent results for the business.   

Beyond Boundaries: Artificial Intelligence in Demand Planning  

Artificial intelligence represents the next frontier in demand planning, encompassing broader capabilities that extend beyond statistical forecasts and ML methods. AI enables machines to perform tasks that traditionally required human intelligence, such as understanding language, recognizing patterns, and making decisions. In the context of demand planning, AI solutions can revolutionize the way forecasts are generated, analyzed, and utilized by not just Demand Planners but break silos in the organization and find uses for sales, finance, marketing, or operations departments.  

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The Distinctive Features of AI in Demand Planning  Software

Automation and Adaptability  

Unlike statistical models and ML methods, AI-based demand planning solutions can be implemented quickly without the need for extensive manual configuration. These systems can adapt to changes in demand plans and dynamically update models without human intervention. This adaptability ensures that forecasts remain accurate and up-to-date, even in rapidly changing business environments.  

Continuous Learning  

AI systems can learn from additional inputs, whether from sales teams, external sources, or real-time data feeds. This continuous learning enables AI-based demand planning solutions to improve forecast accuracy over time. By incorporating new information, AI systems can better capture market dynamics, identify emerging trends, and make more precise predictions.  

Clear Insights  

AI-based demand planning software provide in-depth insights into the drivers of demand. They go beyond surface-level analysis by understanding the relationships and interactions between various components and events. This deeper understanding empowers planners to communicate effectively with other departments, identify root causes of issues, and collaborate on demand-shaping strategies.  

Enhanced Forecast Accuracy  

AI-driven demand planning solutions have demonstrated substantial improvements in forecast accuracy. By leveraging advanced algorithms, historical data, and external signals, these solutions can achieve forecast accuracy improvements ranging from 10-15% based on historical data and planner input. When incorporating additional external signals and order data for Demand Sensing, the accuracy improvements can reach up to 30-40%.  

Differentiating True AI from Misused Terminology  

It is essential to distinguish genuine AI solutions from those that merely claim to utilize AI or ML capabilities. Many companies misuse these terms, creating confusion and diluting the transformative potential of true AI. True AI demand planning solutions, characterized by their ability to automate processes, continuously learn, provide comprehensive insights, and significantly enhance forecast accuracy, offer substantial advantages over statistical forecasts and limited ML methods.  

Heuristics to make the distinction  

A few heuristics to decide whether a technology is built on old methods or data scientists manually tuning the models vs. actually on AI:   

  • Speed of implementation: it doesn’t take months for ML models to be parametrized  
  • An actual increase in accuracy  
  • Deals with outliers in a smart manner rather than just deleting them: able to use these to create causal relationships looking ahead  
  • Adjusts based on changing assumptions  

Conclusion  

Understanding the distinctions between statistics, ML, and AI in demand planning software is crucial for organizations seeking to optimize their forecasting capabilities. While statistical forecasts provide historical insights and ML methods enhance predictive accuracy, true AI-based solutions revolutionize demand planning by automating processes, continuously learning, providing comprehensive insights, and significantly improving forecast accuracy. By leveraging the power of AI, demand planners can navigate the complexities of the marketplace more effectively, anticipate trends, and make informed decisions that drive business success.  

If you’d like to know more about how we have created AI-First Demand Planning solutions, we’re happy to deep-dive into the details with you.  Contact us today.

How AI powered demand planning solutions revolutionize the role of demand planners

In today’s fast-paced and competitive business landscape, organizations are constantly seeking ways to optimize their operations and improve their supply chain. A critical component of this process is demand planning, which involves forecasting future demand for products or services whilst taking into account the trade-offs within the business. 

Traditionally, demand planners relied on average methods to create baseline statistical forecasts and participated in sporadic meetings to understand the influence of demand on various departments. However, with the advent of advanced technologies like Artificial Intelligence, the role of demand planners has evolved significantly.  

The Traditional Role of Demand Planners  

Historically, demand planners focused on data analysis, cleaning outliers, and selecting appropriate forecasting models. Their primary responsibility was to generate accurate forecasts based on historical data and collaborate with other departments during sales and operations planning (S&OP) meetings. While they played an essential role in predicting demand, their interaction with other teams was relatively limited. They didn’t really collaborate on what could be done to still shift the demand in whatever preferable way.   

The Shift Towards Strategic Collaboration  

Now, with the emergence of true AI-driven Demand Planning Solutions, demand planners have become more empowered to transition into strategic roles. By automating the task of outlier identification and model selection, AI allows demand planners to focus on interpreting the organization’s demand drivers to navigate the changing market conditions. Having this deeper understanding allows planners to shift from being a data entry analyst to “the conversation owner”, helping sales, marketing, finance, supply chain, and management to align the demand plan with the organization’s strategic goals.   

Harnessing the Power of AI for Demand Planners  

Reduced Forecast Error and Improved Inventory Management  

By leveraging AI algorithms, Logility significantly reduces forecast errors, leading to more accurate demand predictions. This enhanced accuracy helps organizations optimize inventory levels, resulting in a reduction in excess stock and minimized stockouts. With precise forecasts, organizations can align their production, procurement, and distribution processes, ensuring optimal inventory levels at all times.  

Insights into Demand Drivers  

Transparent AI empowers demand planners to gain comprehensive insights into the factors influencing demand. By utilizing AI’s ability to analyze vast amounts of data, it is possible to identify the precise impact of external factors such as market trends, customer behavior, economic indicators, and promotional activities. Armed with this knowledge, demand planners can collaborate with sales and marketing teams to align strategies and proactively address potential demand fluctuations.  

Enhanced Collaboration and Communication  

Demand planners become the central figures in translating external factors into actionable insights for the organization. By providing clear visibility into the impact of different variables on demand, demand planners facilitate effective communication between departments. Sales and marketing teams can provide relevant inputs, such as price changes, promotions, or expected large orders, which Logility incorporates into the demand plan. This collaboration ensures that demand planners, salespeople, and marketers are on the same page, working towards common goals.  

Strategic Decision-Making  

With the burden of manual data analysis and model selection lifted, demand planners can focus on strategic decision-making. They play a pivotal role in understanding and interpreting demand patterns, identifying growth opportunities, and aligning the demand plan with the organization’s broader strategic objectives. By leveraging the insights provided by Logility, demand planners can make data-driven decisions that drive revenue growth and optimize operational efficiency.  

Conclusion  

The role of demand planners has undergone a significant transformation with the advent of AI-driven demand planning solutions like Logility. While traditional demand planners primarily focused on statistical forecasts and limited collaboration, the integration of AI empowers them to become strategic decision-makers and facilitators of cross-departmental communication. By leveraging Logility’s capabilities, demand planners can harness the power of AI to gain insights into demand drivers and collaborate effectively with sales and marketing teams. This evolution enables organizations to achieve higher forecast accuracy, reduce stockouts and excess stock, and drive overall business growth. With Logility, demand planners have the tools they need to thrive in the dynamic and competitive landscape of demand planning.