Blog Type: Blog

In the rapidly evolving world of supply chain management, relying on outdated notions or oversimplified strategies can lead to significant missteps. That’s why adapting forecasting methods is essential to navigate the diverse and constantly changing demand patterns effectively.

In this article, we’ll explore why a dynamic forecasting approach is beneficial and necessary for today’s demand planners to maintain accuracy and efficiency. Join us as we dissect demand forecasting myths and highlight the importance of evolving forecasting techniques in an ever-changing business landscape:

  • Myth One – One Size Fits all In forecasting
  • Myth Two – Complexity Equals Inaccurate forecasting
  • Myth Three – Creating an Accurate Forecast Requires Years of Historical data

Lets dive in…

The Myth of One-Size-Fits-All Forecasts

Demand forecasting Myths

The notion that a single forecasting method is sufficient for a product’s entire lifecycle is a widespread misunderstanding in demand planning. This idea fails to recognize the ever-changing nature of market landscapes and the varied demands of different products.

Just as a product evolves through various stages in its lifecycle, each stage presents unique demands that require customized forecasting approaches. Using a single, unchanging method throughout a product’s journey is ineffective, much like trying to navigate a constantly changing terrain with an outdated map.

The key lies in embracing flexible and dynamic forecasting methods capable of adjusting to shifts in market trends, consumer behaviors, and economic factors. Such adaptability is beneficial and essential for businesses to maintain a competitive edge in a constantly evolving market.

Different forecasting methodologies are needed at each stage of a product’s life, from launch to maturity and eventual decline. It’s much like understanding the changing needs of a product over time. Recognizing and applying these varied methods is essential for accurately predicting demand, helping to prevent practical issues such as inventory mismanagement or missing out on sales opportunities. This approach ensures that forecasting strategies stay relevant and effective, keeping pace with the fluid dynamics of the market.

Forecasting in the Face of Volatility

Addressing the second of demand forecasting myths, we tackle the challenge of forecasting products with unpredictable, low and lumpy demand patterns. This scenario presents a complex puzzle for demand planners as these irregular patterns don’t align with the consistent trends most traditional forecasting methods are designed for. However, this complexity does not make accurate forecasting an impossibility.

Advanced techniques like the Modified Croston method are specifically developed for these unpredictable demand scenarios. This method excels in environments where sporadic or non-uniform patterns, including periods of no demand, mark demand history. By examining the intricacies of demand fluctuations—identifying peaks, valleys, and plateaus—the Modified Croston method can uncover hidden patterns or use averaged trends to predict future demands accurately. This approach is especially useful for products that do not exhibit a regular demand curve, helping businesses to better align their inventory and resource planning with actual market needs.

The Power of Data in Forecasting

Dispelling another of the many demand forecasting myths, we address the belief that creating an effective forecast requires several years of historical data. While extensive historical data can be beneficial, accurate forecasting is optional. In many cases, if analyzed correctly, short-term data can be just as insightful for demand planning.

This is where the technique of attribute-based modeling becomes critical, particularly for forecasting new products, seasonal offerings, or products approaching the end of their lifecycle. This method examines a range of demand profiles created based on various attributes—such as product characteristics, regional preferences, or seasonal influences.

Assigning these detailed profiles to individual products and adjusting them based on early market feedback allows for precise and adaptable forecasts. This approach proves that effective forecasting can be achieved even with limited historical data by concentrating on relevant product attributes and adapting forecasts to reflect current market signals and trends.

Stochastic Planning: A Game-Changer for Intermittent Demand

Stochastic planning is a key strategy for managing intermittent demand, a common challenge in service parts inventory and wholesale distribution sectors. This method shifts from traditional deterministic forecasting to a probabilistic model. Instead of merely trying to predict demand, it evaluates the likelihood of various demand scenarios, making it ideal for handling irregular demand patterns.

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Intermittent Demand: Using Stochastic Replenishment Planning

Companies face the perpetual challenge of managing items with intermittent demand patterns. Read more to gain a deeper understanding of Stochastic Replenishment Planning

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The approach encompasses three main areas: estimating the probability of demand occurrences, understanding lead-time distribution, and conducting a thorough risk analysis. It starts with assessing the likelihood of demand within a given replenishment lead time based on historical data, adapting to different demand patterns. This helps in preparing for varied demand scenarios.

Additionally, an accurate understanding of lead-time demand distribution is vital, which goes beyond customer demand variability to include supplier lead-time variation. Using statistical methods, planners can more accurately determine lead-time distribution, aiding in setting effective inventory policies.

Finally, stochastic planning requires evaluating risks associated with both understocking and overstocking, enabling planners to make informed decisions that balance inventory levels with demand, particularly for items with high margins or obsolescence risks.

Integrating DemandAI+ into Forecasting Strategies

Logility’s DemandAI+ is an advanced solution incorporating these forecasting techniques to address the complex needs of modern demand planning. Using artificial intelligence, DemandAI+ enhances and automates forecasting, particularly for products with varying demand patterns.

DemandAI+ effectively integrates stochastic planning into its forecasting system, allowing for more precise predictions, especially for products with intermittent demand. The AI-driven system continuously learns and adapts to market changes, maintaining the relevance and accuracy of forecasts.

DemandAI+ simplifies analyzing extensive data and multiple variables that impact demand. It automates the selection of suitable forecasting methods for different product lifecycle stages and market conditions. This improves forecast accuracy and saves time and effort for demand planners, enabling them to focus on strategic initiatives.

Embracing Advanced Forecasting for Future-Ready Demand Planning

Traditional demand planning methods are no longer adequate for the current, fast-paced market. Our examination of common forecasting myths underlines the need for more flexible, innovative, and adaptable forecasting techniques. Advanced methods, including stochastic planning and tools like DemandAI+, are undeniably important for effective demand planning.

The future of demand planning requires a commitment to adaptation, innovation, and ongoing learning. Don’t let the demand forecasting myths hold you back. Moving forward, the successful integration of state-of-the-art technology and a comprehensive grasp of complex demand patterns will be vital for effective supply chain management. Now is the time to evolve our forecasting strategies to respond to market changes and actively influence our market position.

Supply Chain AI has sparked considerable excitement, shock, and fear over the past year. From Generative AI innovations like ChatGPT to industry events, analysts, and mass media stories, every business leader is being tested – from their vision and trust in the technology to internal preparations and integration of artificial intelligence into supply chain operations.

The primary reason for such a range of emotions stems from not knowing how to adopt AI advancements. In one of our recent webinars, a poll showed 76% of attendees were in the educational stage of adopting Generative AI in their companies. In addition, another poll, just one month later showed that 31% of attendees were cited saying they are either developing proposals to begin or currently testing pilots that incorporate AI to their companies.

Considering where most companies are in their Supply Chain AI journey, the mix of excitement and apprehension is no surprise. This phase is often characterized by a range of questions that are challenging to answer, including:

  • How can we trust and validate the information Generative AI produces?
  • How can our organization best prepare for current and future AI capabilities?
  • How can we move forward when internal resources are limited – from planners to data scientists?

To truly overcome this common hurdle to adoption, business leaders must understand the difference between Generative AI and machine learning and know which use cases deliver the greatest-possible impact.

The differences between Generative AI and machine learning

Generative AI and machine learning are closely related within the broader realm of artificial intelligence. However, there are critical differences between the two: their primary objectives and outputs. Unlike machine learning, which is predominantly task-oriented, Generative AI is more about creating original content that doesn’t necessarily relate directly to specific input data but instead learns the underlying structure to produce new, similar results.

Machine learning is a branch that involves algorithms and statistical models enabling computers to improve their performance on a task through experience or data. The technology encompasses various techniques to learn from labeled or unstructured data to predict or classify based on given data, make decisions or inferences, and train models to perform tasks. The system then learns patterns and makes predictions or decisions based on the provided data, essentially focused on specific tasks such as classification, regression, or clustering.

Generative AI, on the other hand, is a subset of deep learning that deals with the creation of new content or data based on both labeled and unlabeled data. This field primarily focuses on creating new content – including images, text, audio, or videos – that may not have been part of the original dataset based on patterns and information learned and recreated from the input data.

Knowing these differences between the two technologies is pivotal for businesses aiming to leverage artificial intelligence effectively. But the complementary relationship between the two must also be acknowledged, especially as advancements in one ultimately benefit the other and contribute to the broader development of sophisticated applications.

Five Ways to Kick off Your Journey

A structured approach for embarking on a Supply Chain AI journey is essential to a smooth transition. Companies need to define objectives, acquire the right tools and technology, prepare the data infrastructure, implement AI models, and continuously improve the system.

Here are five pivotal use cases for AI adoption that can be achieved with DemandAI+ and InventoryAI+ solutions. These scenarios not only showcase the potential impact of AI-first supply chain planning but also demonstrate versatile and far-reaching applications across various business aspects.

1. Demand sensing

Real-time visibility and insights into shorter-term demand enable improved service levels and higher forecast accuracy. This capability translates market-based demand information to allow supply chain organizations to detect short-term buying patterns. New mathematical techniques and near real-time demand signals can then be leveraged to improve supply chain response to unplanned demand changes – a transformational opportunity for any company.

2. Causal forecasting

Built-on causal forecasting isolates actual demand signals from market “noise.” By combining machine learning with Generative AI techniques, it can uncover complex patterns that are often missed, helping supply chain professionals focus on data that matters most to their business, customers, and overall growth.

3. New product introductions

Forecasting new product introductions with no sales history can be challenging, but artificial intelligence can be a valuable tool to help make these predictions more accurate. Supply chain systems can learn from evolving demand in real time to produce a more precise forecast with significantly less effort. Then, over time, downstream supply chain plans become more specific – leading to higher profitability, more-satisfied customers, and better synchronization across supply chain partners.

4. Inventory optimization

Applying artificial intelligence to inventory management provides immediate insights into inventory performance against the plan. This form of intelligent analytics opens the door to more-profitable inventory positions while giving supply chain planners real-time alerts and instant visibility into exceptions and proposed resolution of issues.

Better yet, with intelligent scoring, the intelligent scenario applies economic prioritization to focus more on the most-important opportunities – ultimately reducing bloated inventories while increasing service levels and avoiding deficits. Moreover, inventory planning capabilities can automate the identification of the best inventory policy for each SKU at every stocking location based on the most current information. Such data includes demand, demand variability, supply variability, lead time between facilities, inventory held at alternate stocking locations, and the types of stock at each facility.

Supply chain planners can also leverage an AI-automated inventory policies feature to determine which products are experiencing sporadic or “lumpy” demand, such as wide size ranges, spare parts, or industrial equipment. This approach allows supply chains to apply a stochastic replenishment planning tactic that can result in service-level improvements between 9% and 27% while lowering inventory levels and logistics costs.

5. Network optimization

The complexities in today’s supply chain network flows not only impede effective monitoring and management of goods movements but also increase a supply chain’s vulnerability to natural disasters and geopolitical tensions that can further compound delivery interruptions.

An innovative approach to network optimization can address this common issue by swiftly creating a digital twin of a supply chain. By employing Generative AI to explore potential future scenarios, this strategy enables supply chain planners to analyze and assess various configurations that can effectively manage costs, improve service, and align with emission reduction objectives. In addition, planners can simulate and evaluate diverse scenarios, allowing for proactive decision-making and strategic planning to navigate the complexities of the modern supply chain landscape.

A smooth path to true AI-first business transformation

By recognizing the unique and complementary relationship between machine learning and Generative AI, supply chain organizations have a one-of-a-kind opportunity to usher their companies into a new era of data intelligence. They can not only navigate the complexities of modern supply chains but also improve efficiencies, manage costs, enhance service levels, and create a more sustainable future for their supply chain and overall business.

Revolutionizing Supply Chain Planning with ChatGPT and AI-First Forecasting

Webinar: 31% of supply chain leaders are either developing proposals to begin or currently testing pilots for incorporate AI into their business. See why in this webinar

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Sales and operations planning (S&OP) has been a longstanding practice for businesses across nearly every industry. However, this critical supply chain process has been under enormous stress lately from departmental silos, complex trading partner networks, and geopolitical turmoil. Meanwhile, planners skilled to handle these challenges are in short supply – especially as supply chain leaders retire in record numbers.

Yet, as supply chain innovators, we also know the rich history of applying technologies to continuously resolve these challenges and optimize S&OP outcomes. And fusing Generative AI with machine learning algorithms is quickly becoming part of that legacy – delivering a modern, more inclusive, and intuitive S&OP paradigm.

According to experts in our webinar, “How AI-Driven Insights Can Save Your S&OP Before it is Too Late ,” advanced AI technologies, such as ChatGPT and Narrow AI, have an incredible capacity for translating complexity into informed decisions that drive profit and growth. Additionally, these technologies can connect and align cross-functional teams – such as sales, marketing, finance, and supply chains – in ways that allow businesses to stay ahead of the constant flow of supply chain disruption.

Piet Buyck, Senior VP Solution Principal at Logility, “Whatever [businesses] do is driven by the plan they make. And the better the plan, the better we can be as a company in creating revenue, reducing market capital, and reducing costs. AI can help with that.”

The transformation of planning, sensing, and forecasting

A key area in the S&OP process where AI is particularly transformative is demand. By moving beyond conventional, backward-looking methods, planning can forecast demand and inventory at the speed of the market.

“Demand and S&OP is not only about repeating history. It’s also about understanding what’s going to happen, which is your forecast,” Buyck observed. “If you understand what drives your demand and which levers you can use to address market share or increase profitability, then you come to a true demand plan.”

While S&OP drivers and objectives may differ from business to business, the process is relatively the same. Planners strategically examine procurement and sourcing practices and align production and distribution to meet product demand. However, certain aspects of the process may take more priority than others depending on their strategic focus and evolving market dynamics, for example:

  • Cost leadership: Selling relevant products accurately is critical to maintain optimal stock levels and efficient production.
  • Customer intimacy: Ensuring all components are available when needed helps create comprehensive services that address customer needs.
  • Growth-oriented: Increasing investment in buffer inventory in specific markets may help support and scale expansion.

With AI-enabled S&OP planning solutions such as Logility’s DemandAI+, companies can take advantage of every peak and valley in their demand trends to make the right assumptions and best decisions and deliver optimal outcomes – with speed, agility, and confidence.

“You can now start from one common view, including the baseline and trends. The solution’s AI capabilities not only give the insight but also bring and use it as insight for the future,” Buyck said.

When leveraging these AI-powered capabilities of DemandAI+, Logility customers can overcome tough forecasting challenges and achieve supply chain performance improvements, such as:

  • 70% faster weekly planning cycles
  • 15-30% fewer forecast errors
  • 16-32% lower inventory costs

DemandAI+: A new paradigm for supply chain planning

Purpose-built for supply chain planning, Logility’s Demand AI+ shows every person across departments and roles what’s driving demand for their products or services. This shared understanding is based on a combination of insights on where and how growth can happen, which categories may decline, and the reasons behind either trend.

DemandAI+ also leverages the incredible speed of today’s AI algorithms. The solution accelerates the information-gathering part of the S&OP planning process from weeks to a continuous stream of real-time information – from market shifts to business impacts and everything in between.

Take, for example, collaboration features enabled by ChatGPT and Narrow AI. With all the information and the underlying assumptions digitally available, DemandAI+ can be queried to get the right data or report. On top of that, scenarios can be created to see how the overall organization and different departments are impacted.

This shift from analog processes and siloed analysis to digital understanding and enterprise-wide collaboration helps uncover demand drivers by translating real-world events, competition, and customer behavior into quantifiable data. 

Such access to up-to-date demand insights allows departments to collaborate seamlessly and make informed decisions. Plus, they can forecast and re-forecast demand with constant updates on drivers and leverage the latest insights proactively to take short-term actions and make long-term strategy adjustments. 

An AI-first era for supply chain management

DemandAI+ presents a groundbreaking solution to the intricate task of aligning sales, marketing, finance, and supply chain functions throughout the S&OP planning process. By leveraging the power of AI-first forecasting and Generative AI, the solution takes demand planning, sensing, and forecasting to a new level – moving beyond the limitations of traditional reliance on sales history.

Companies can predict and respond faster and more accurately to changes as they happen with algorithms that incorporate customer behavior data, market dynamics, and events into the forecasting process. In addition, they can improve the accuracy of their forecasting models by considering base demand, promotional lift, causal forecasts, and user insights into their overall analysis.

With the integration of ChatGPT and Narrow AI, this new paradigm in supply chain planning is further enriched with more-sophisticated insights and unparalleled visibility. Logility’s DemandAI+ can be tailored to tackle the toughest forecasting challenges, including promotions, seasonality, disruptions, and new product introductions – offering organizations a comprehensive and adaptable tool for strategic decision-making.

Explore how AI further transforms sales and operations planning

How AI-Driven Insights Can Save Your S&OP Before it is Too Late

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In a world characterized by market volatility and unpredictability, businesses constantly navigate supply chain disruptions, erratic demand patterns, and unexpected risks. While traditional forecasting methods have served us well, they often fall short when addressing the evolving challenges of today’s dynamic business landscape.

Enter Artificial Intelligence (AI) — a buzzword that’s more than just hype, as said in an Accenture survey, 86% of executives are actively investing in it. Within this vast world of AI, Generative AI models like ChatGPT are revolutionizing user adoption and redefining possibilities.

What happens when we intertwine Generative AI with machine learning in supply chain planning? We get a transformative shift that allows companies to plan demand and inventory at the sheer speed of the market. In this article, we’ll explore the fusion of AI-first forecasting with traditional models and its immense value to the supply chain.

AI-First Demand Planning

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Exploring the Power of Generative AI in Supply Chain

Generative AI stands out in its ability to create something entirely new, going beyond the structured data and delving into the vast expanse of unstructured data generated every moment in our digital world. This is particularly valuable for supply chain planners, enabling them to draw comprehensive insights across the organization and democratize access to crucial information.

In supply chain planning, this translates to a transformative shift. Companies are now equipped to plan demand and inventory with unparalleled speed, aligning their strategies more closely with real-time market demands. Generative AI and machine learning are coming together to offer a blend of the old and new, integrating AI-first forecasting with traditional models to unlock immense value for the supply chain.

Traditional vs. Generative AI

While AI in general refers to machines mimicking human intelligence and machine learning is a specialty within AI that includes systems that can learn from data, generative AI is a unique subset focused on creating new content, ranging from text and music to visual arts. For supply chain professionals, this distinction is vital. It reveals opportunities to tap into unstructured data, offering richer insights and enabling more informed decision-making.

AI in Action: Modern Supply Chain Planning

Forward-thinking companies have already begun leveraging the potential of AI in their supply chain planning.

For example, a global shipping enterprise harnessed  the power of AI to fine-tune its worldwide freight system. Considering elements like shipping volumes, vessel capacities, and port limits, the advanced machine learning model crafts the best shipping pathways, slashing transit durations by 20% and decreasing fuel usage by 15%.

The Shift to AI-first Forecasting

Moving away from the reactive nature of traditional statistical demand forecasting models, AI-first forecasting offers a more proactive and holistic approach. It considers not just historical data, but also real-time inputs, market dynamics, and customer sentiments, resulting in forecasts that are agile, accurate, and aligned with current market conditions. This approach ensures that companies are not just reacting to changes but actively anticipating and strategizing for them.

Insights from our webinar emphasizing the benefits of using ChatGPT and AI-first forecasting, reveal a trend toward AI adoption; 33% of attendees are already exploring specific AI applications, while a significant 77% are in the process of learning about the potentials of Generative AI. This signals a looming change in the industry, with a growing number of organizations moving from the educational phase to active implementation of AI-first solutions.

Realizing Value with ChatGPT and AI-First Forecasting

Watch how companies are using AI implementation to lead concrete cost reductions, minimize waste, and improve overall supply chain resilience in part two of this two part series.

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AI for Supply Chain Excellence

AI isn’t here to replace human roles; it’s here to augment them. By providing richer insights and enabling more precise strategy formulation, AI-first tools like ChatGPT empower executives and supply chain planners to make faster, more informed decisions, driving efficiency and profitability. Integration of AI across the supply chain ecosystem fosters a connected and intelligent environment, ensuring that businesses are well-equipped to respond to the needs of various stakeholders in real time.

  • For the Executive: Generative AI serves as a touchpoint for understanding the holistic health of the business without needing to rely on someone else to run queries against the data. For instance:
  • CSCO: What were the top 5 selling brands last January?
  • Gen AI: “Brand A with a sales figure of 122,345 units, Brand B with a sales figure of 112,230 units…”
  • For the Planner: AI can provide recommendations on optimal service levels, ensuring that resources are allocated efficiently.
  • For Sales and Customer Service: AI can give real-time updates on order statuses, ensuring customer queries are answered promptly and accurately.

The Power of Generative AI in Supply Chain Management

Generative AI is transforming supply chain management, delivering not just descriptive analytics but also predictive and prescriptive insights. For instance, when a Chief Supply Chain Officer queries about production issues, Generative AI quickly identifies a component shortage from a specific supplier, detailing its impacts across inventory and customer satisfaction.

When a component shortage occurs, the AI assistant doesn’t just identify the problem; it also recommends solutions, such as alternate suppliers, and provides insights on lead times, expedited order costs, and the overall impact on the supply chain. This capability ensures that everyone in the supply chain, from executives to ground operations, has the necessary tools and insights for optimal decision-making, agility, and improved customer satisfaction.

Looking Ahead

With the acquisition of Garvis and the subsequent creation of DemandAI+, Logility is actively reshaping the future of supply chain planning.

DemandAI+ answers the challenges and complexities of today’s market dynamics, translating them into accurate forecasts and actionable insights. By embedding Generative AI, advanced AI-driven algorithms, and machine learning into our Logility® Digital Supply Chain Platform, we provide a holistic, AI-first approach to demand forecasting.

DemandAI+ has demonstrated its robust capabilities across 70 implementations, yielding a 70% reduction in weekly planning time and a 15-30% decrease in forecast error. Clients are efficiently navigating market complexities, ensuring precise inventory management and responsive decision-making for even the most challenging product categories.

Logility is setting new industry standards, ensuring businesses are well-equipped to thrive in today’s dynamic market landscape with AI-first forecasting. Reach out today to learn how Logility can bring your supply chain planning to new heights with AI.

Industrial durables companies face the perpetual challenge of managing items with intermittent demand patterns. These items often exhibit sporadic and unpredictable fluctuations in demand, making traditional inventory management methods less effective. To tackle this issue, many industrial durable companies are turning to probabilistic inventory planning, a data-driven approach that offers practical solutions to the unique challenges of managing intermittent demand. Let’s define what probabilistic inventory planning is and explore why companies should embrace this approach.

What is Probabilistic Inventory Planning?

Probabilistic inventory planning is an advanced inventory management methodology that incorporates randomness and uncertainty into the decision-making process for items with intermittent demand patterns. It leverages stochastic processes and simulations to make informed decisions about when and how much to reorder. Unlike deterministic inventory models, stochastic replenishment planning acknowledges and quantifies the inherent variability and unpredictability in demand.

Probabilistic Forecasting:

Probabilistic (or stochastic) forecasting is a method that involves incorporating randomness or stochastic processes into models to generate forecasts. Probabilistic forecasting recognizes that various factors contributing to a forecast may have inherent uncertainty or variability, and it models this uncertainty by introducing randomness into the forecasting process. The key characteristics of Probabilistic forecasting are:

  • Utilizes random variables or stochastic processes to represent uncertainty.
  • Often used in simulations, Monte Carlo simulations, or Markov processes.
  • Allows for the exploration of different possible outcomes through repeated random sampling.
  • Suitable for modeling complex systems with inherent variability.

For example, in financial modeling, probabilistic forecasting might be used to simulate stock price movements over time, considering the random nature of market fluctuations. This approach generates a range of potential future price scenarios based on historical data and assumed stochastic processes.

Key Components of Probabilistic Inventory Planning:

Stochastic Models: Probabilistic Inventory planning utilizes probabilistic or stochastic models to represent the demand patterns. These models account for the randomness and variability in demand, allowing for more accurate forecasting and decision-making.

Monte Carlo Simulations: Probabilistic inventory planning often employs Monte Carlo simulations, a computational technique that generates multiple possible demand scenarios by repeatedly sampling from probability distributions. This enables companies to assess the range of possible outcomes and their associated risks.

Reorder Point and Order Quantity: Unlike deterministic methods that rely on fixed reorder points and order quantities, probabilistic inventory planning calculates these parameters probabilistically. It considers not only the average demand but also the entire distribution of potential demand values.

InventoryAI+: Transforming Chaos into Triumph

Watch this dynamic webinar to learn more about the cutting-edge solution designed to revolutionize the way supply chain professionals optimize inventory and maximize performance.

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Why Should Industrial Durable Companies Use Probabilistic Inventory Planning for Intermittent Demand?

Embracing Uncertainty

Intermittent demand items in the industrial durable sector are notorious for their unpredictable and sporadic fluctuations. Traditional inventory management methods often struggle to handle this inherent uncertainty, leading to either frequent stockouts or overstocking. Probabilistic inventory planning addresses this challenge by embracing uncertainty, modeling it, and factoring it into the decision-making process.

By incorporating probabilistic (or stochastic) processes and simulations, industrial durable companies can explore the various ways in which demand can unfold. This approach provides a more comprehensive understanding of the potential outcomes, enabling companies to make more informed and robust inventory decisions.

Accurate Forecasting

One of the primary advantages of probabilistic inventory planning is its ability to generate more accurate demand forecasts. Instead of relying on point estimates, this approach provides a range of possible outcomes, along with their associated probabilities. This enhanced forecasting accuracy allows industrial durable companies to better align their inventory levels with actual demand patterns, reducing the likelihood of stockouts or excess inventory.

For instance, a company that manufactures heavy machinery components may experience intermittent demand due to variations in construction projects. Probabilistic inventory planning helps this company generate a distribution of possible demand values, which are far more informative and reliable than a single-point estimate, ensuring that they maintain optimal inventory levels for each component.

Reduced Holding Costs

Holding costs are a significant concern for industrial durable companies. These costs encompass expenses such as warehousing, insurance, depreciation, and opportunity costs associated with inventory holding. When dealing with items exhibiting intermittent demand, the risk of holding excessive inventory is substantial.

Probabilistic inventory planning mitigates this risk by using probabilistic approaches to calculate optimal inventory levels. By considering the variability and randomness in demand, this approach ensures that safety stock levels are adequate to handle fluctuations, reducing the need for excessive inventory. As a result, industrial durable companies can significantly reduce holding costs, leading to improved profitability.

Minimizing Stockouts

Stockouts can lead to production delays and customer dissatisfaction in the industrial durable sector, where timely deliveries and project schedules are crucial. Probabilistic inventory planning is designed to minimize stockouts by considering the entire distribution of potential demand outcomes. This approach ensures there is a sufficient buffer of safety stock to handle unexpected demand spikes.

Consider a company that manufactures custom equipment for specialized industrial applications. Some components may exhibit intermittent demand patterns due to variations in custom orders. Probabilistic inventory planning helps the company calculate safety stock levels that are responsive to the inherent variability in demand, ensuring that customers receive the components they need on time and preventing production delays.

Efficient Resource Allocation

Probabilistic inventory planning aids industrial durable companies in efficiently allocating their resources. By accounting for uncertainty and modeling demand variations, companies can optimize their purchasing and production processes. This means they can strategically allocate resources, such as labor and raw materials, to match the expected demand scenarios more effectively.

For example, a company manufacturing custom steel structures may experience intermittent demand for various types of steel. Probabilistic inventory planning can help this company make informed decisions about when to order specific steel types and in what quantities, ensuring that resources are used efficiently and avoiding unnecessary idle capacity.

In the industrial durable sector, where intermittent demand and project-based manufacturing are common, probabilistic inventory planning offers a powerful tool to address inventory management challenges effectively. This approach embraces uncertainty, provides more accurate forecasting, reduces holding costs, minimizes stockouts, and optimizes resource allocation. Companies that implement probabilistic inventory planning can achieve enhanced forecasting accuracy, cost savings, and improved customer satisfaction.

Making the right decisions regarding inventory management is crucial. For companies dealing with intermittent demand items, probabilistic inventory planning offers a data-driven and practical solution to the persistent challenges of inventory optimization. By embracing stochastic processes and simulations, companies can unlock the full potential of their inventory management processes, leading to greater efficiency, profitability, and customer satisfaction in the industrial durable sector.

Why do you need Generative AI in your supply chain? Your voice assistant probably can’t tell you, but we can.

When mobile voice assistants launched, everyone marveled at how you could just ask a question out loud and get a response from your phone or other electronic devices. How cool was that! No logging in, no typing your question in a way the search engine wanted. Just “hey Siri” and you were sent off in the right direction even if it was just on your phone.  That was 2011 and things have changed.

Voice assistants are here, with Amazon Alexa and Google Assistant joining Siri and helping with day-to-day tasks, especially around the home. Turn on the lights! Open the garage door! This is great! They’ve come a long way and have helped us automate many things to make our lives easier.

Then came the introduction of ChatGPT and other generative AI products; they took things to a whole new level. They don’t just turn on lights or provide links, they figure out what you’re asking and provide a narrative to answer your questions. Want to know about your favorite baseball team? I asked Siri “Tell me about the New York Mets,” and Siri was nice enough to tell me that they were pummeled that day. (Yes, Siri actually used the word “pummeled” which, while accurate, is kind of a bummer for a fan to hear) When I asked ChatGPT the same question, it gave me a nice narrative about the history of the Mets, which stadiums they’ve played at over the years, team color, team mascot name (Mr. Met if you’re wondering), their two World Series wins, and other useful information. While it doesn’t have today’s score since it only has info through September 2021, I didn’t ask for the current score, I only asked about the Mets so that’s probably what I was looking for.

Wouldn’t it be great if generative AI were available in your supply chain planning system as well? Machine Learning and Artificial Intelligence have been part of supply chain planning for a long time and are part of the algorithms behind the best of breed forecasting and optimization applications, but there’s so much more that can be done with generative AI.

Instead of searching through the user interface, writing SQL, or wrangling data in Excel you could just ask a question and get the information needed. When your senior management asks, “What is the forecast for my top products?”, you can ask your supply chain software the same question and there it is. Your marketing department wants to know how their promotions are affecting the demand forecast for the company’s top product family, so you ask, “What is the promotion lift for the top product family?”

Boom – there’s the promotional lift for the top family without even having to specify which family it is. Want that as a chart? Just ask!

But why stop there? If your supply chain software can use large language models (part of the technology behind ChatGPT) to understand and respond to natural language questions from you, it can also answer them for everyone else as well. Your senior management can directly ask the question, and instead of the typical back and forth to answer their underlying question they can refine the questions themselves directly in the supply chain software. Provide marketing access and they can ask their questions as well. 

The potential of generative AI is nothing short of transformative, and integrating this technology into your supply chain planning system unlocks a new realm of possibilities.  In the webinar “How ChatGPT Changes All Rules of Supply Chain Planning,” 1,425 registered attendees understood this as well with 79% of respondents saying ChatGPT would have a high or very high impact on supply chain planning.

OnDemand Webinar: Realizing Value with ChatGPT in AI-First Forecasting

Realizing Value with ChatGPT in AI-First Forecasting

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Imagine being able to access and harness the wealth of data at your fingertips with nothing more than a natural language question. This is a fundamental shift from the traditional, sometimes cumbersome, methods of data retrieval and analysis. But the true power lies in its capacity to democratize data access.  More people can directly engage with the system, refining their questions and extracting insights, driving more informed decisions across your entire organization. Generative AI not only unlocks the latent value in your data but also positions your supply chain system as a dynamic source of answers rather than just raw data, making it an invaluable asset for your business.

“Generative AI not only unlocks the latent value in your data but also positions your supply chain system as a dynamic source of answers rather than just raw data, making it an invaluable asset for your business,” “It will revolutionize the way you navigate the complexities of the modern supply chain landscape and the data that drives it” – Steve Ungar

There is a tremendous value in the data that you maintain, and generative AI can be used to unlock it and provide across within the supply chain function as well as across your entire organization. The more your supply chain planning system can provide ANSWERS to QUESTIONS instead of just proving DATA, the more valuable it becomes.

There are several places to get more information on this topic. Have you seen our AI-first forecasting eBook? You can also get in touch with us, to learn more about Logility’s journey to offering AI-embedded solutions.

Why Network Optimization Is the Key to Consumer Packaged Goods Supply Chain Resiliency

It has been a few years since the onset of the pandemic exposed the tragic consequences of supply chain fragility within the consumer packaged goods (CPG) industry. Now that just enough progress has been made to rebuild and strengthen these operations, a sense of “normalcy” has been restored consumer access of desirable products. However, we might not be out of the weeds just yet.

Declaring “mission accomplished” is still too premature, based on research from the Organisation for Economic Co-operation and Development. Although inflation has eased in many countries over the past summer, CPG prices are still rising faster than real wages and showing no signs of leveling off in 2024. In addition, a considerable portion of freight transportation expenses continues to soar as diesel fuel costs surge by 20% year over year.

To gain control over their costs and pricing, supply chain leaders must capitalize on the recent stabilization of production capacity and inventory levels. This includes continuously adjusting the supply chain network design to reflect changes in demand, costs, capabilities, and corporate objectives.  In doing so, organizations will reduce costs, optimize operational efficiency, promote sustainable practices, and ultimately contribute to bottom-line improvements.

The strategic importance of network optimization

The quality of a supply chain network’s design directly influences everything from operational efficiency and consumer satisfaction to the ability to remain competitive and adapt to evolving market conditions. Yet, many supply chain leaders are caught between the need to pivot operations within shorter timeframes and decades-old modeling systems. They simply cannot keep pace with business dynamics.

Swift, factual, and actionable decision-making is essential when optimizing a network design to increase supply chain resiliency, profitability, and sustainability. Resulting outcomes not only yield cost reductions and safeguard revenue streams, but also provide the confidence to navigate crucial goals such as the following:

1) Alleviate cost pressures while expanding margins

The design of a supply chain network plays a central role in the cost structure of producing, storing, and distributing products. Through meticulous optimization, consumer packaged goods companies can curtail transportation expenses, minimize inventory holding costs, and reduce operational expenses. Achieving these cost efficiencies improves profitability and elevates long-term competitiveness.

2) Address demand patterns responsively

Consumer preferences, market demands, and competitive landscapes are in constant flux. An agile network empowers supply chain leaders, employees, and suppliers to swiftly adapt their operations to every change. In return, companies are better positioned to stay ahead of the curve and meet evolving consumer needs – whether launching new products, entering new markets, or adjusting production volumes.

3) Mitigate risk and respond to supply constraints

In a world prone to disruptions, the financial and reputational consequences of supply chain hiccups can be severe. An effective network design considers risk mitigation strategies – including redundancy, diversification of suppliers, and comprehensive disaster recovery plans – that ensure business continuity even in the face of unforeseen challenges.

4) Manage inventory and service times to maximize consumer satisfaction

Strategically locating distribution centers and storage facilities helps minimize excess inventory and reduce carrying costs while ensuring products are readily available to consumers. By reducing stockouts, shortening delivery times, and ensuring product availability where and when needed, this approach accelerates the delivery of exceptional services that delight consumers and earn their loyalty.

Across these four use cases for network optimization, it’s clear that the convergence of continuous modifications and planning fosters seamless coordination and decision-making across the supply chain. For example, inventory levels can be right-sized to outsmart unpredictable demand so that all consumers’ needs are fully met without the burden of excess carrying costs. By embracing these principles, supply chain leaders can turn supply chain network optimization into a powerful force that propels their company forward in a rapidly changing market.

Turbocharge your Supply Chain Network Design

Get access to our executive brief where we cover network design and optimization opportunities, explore how to answer what-if scenarios, and advanced modeling systems.

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Forward-looking insight and control calm the chaos

With the right technology, network optimization becomes the key to plotting a well-defined, yet flexible, course for supply chain management. CPG companies can lengthen or shorten their supply chain to pivot operations in ways that optimize the balance between safety stock values, supply continuity, and cost efficiency.

According to the Gartner report, “Market Guide for Supply Chain Network Design Tools,” “All network design tools must be able to answer the question of how many facilities should I have and where should they be to meet required service levels and optimize cost. This is in addition to the ability to run many what-if scenarios to determine the impact of constraints and costs on the network.”

Suppose a CPG brand decides to shift its manufacturing capacity from Rhode Island to a higher-cost, yet more-sustainable, alternative in Colorado. Adding an adaptive consumer packaged goods network optimization solution to the digital supply chain management platform, empowers the business to model any number of potential supply chain designs and close in on an optimum based on business goals and realities.

This real-time analysis evaluates trade-offs and guides data-driven decisions that reduce latency in modeling and executing the optimal product flow through planning systems. As a result, the CPG brand chooses a conservative plan that minimizes future risks. This decision resulted in tens of millions in savings, including a multimillion-dollar reduction in service times.

While more substantial savings are possible, this network optimization analysis proved that prioritizing the lower-risk and more-sustainable option is the best choice in the long run. This result plays out repeatedly for many companies as supply chain challenges grow more complex, particularly in mature product lines.

Moreover, leveraging a network optimization solution allows companies to undertake regular analysis and re-optimization of their supply chain every two to six months to address evolving factors proactively. Doing so enables decision-makers to anticipate challenges rather than firefighting in crisis mode.

A linchpin of agility and effectiveness

The significance of consumer packaged goods network optimization cannot be emphasized enough. In this fluid and unpredictable environment, the ability to anticipate and control future outcomes helps steer businesses toward a resilient and prosperous future.

With the right technology and a steadfast commitment to continuous analysis, the approach empowers brands to effectively mitigate risks early, address supply constraints strategically, and enhance consumer satisfaction innovatively. But more importantly, challenges can be turned into opportunities and operational order can be established to compete effectively in an increasingly chaotic world.

To learn more about network optimization of your supply chain operations, download our executive brief “How to Turbocharge Your Supply Chain Network Design.”

For the last decade supply chain leaders and CIO’s have been evaluating when and how to move their supply chain applications to the cloud. Many distinct factors influence the timing of their decision to make the move. And, for some organizations, understanding and realizing the full potential of the cloud and using cloud technology can seem out of reach.  

Let’s break down the trade-off between an on-premises and a cloud environment, what it means for your business, and how you can realize the optimal potential of a cloud investment.  

The key areas to consider when upgrading to the cloud include:  

  • The Value of Cloud Technology 
  • Cost Competitiveness
  • Performance Expectations 
  • Upgrades 
  • Responsiveness
  • Support Expertise & Response

The Value of Cloud Technology 

Cloud Technology

Which business drivers and advantages are motivating companies to shift to the cloud? 

The primary business drivers to moving to the cloud are: 

  • Leveraging a network of data and information – A cloud environment creates enormous value for the supply chain by leveraging a network of data and information to allow greater collaboration and continuous innovation. It scales the interchange of that intelligence to deliver value to clients, suppliers, buyers, and sellers with considerable speed, agility, and visibility and promotes greater business efficiency.  
  • Timely and accurate data – Digital planning platforms are driven by timely and accurate data. And those that leverage on-demand services provide secure access to scalable, high-performance environments available around the globe. This structure can be a huge win for your IT teams focused on managing their core business devices and services.
  • Integrations for modern and frequent data updates – The Logility® Digital Supply Chain Platform includes a data integration layer that enables your organization to build modern integrations that support frequent data updates. Integrations include cloud ERP systems, data warehouse solutions, or big data systems – such as Microsoft Azure Synapse and Snowflake – or can be driven by events or web services that make the platform more responsive.  

Cloud advantages include:  

  • Improved collaboration – a cloud-based supply chain platform enables better collaboration between supply chain partners, allowing for real-time communication and collaboration. 
  • Increased visibility – a cloud platform provides real-time visibility into critical supply chain data, such as inventory levels, which can help you make informed decisions. 
  • Scalability – a cloud platform can be quickly scaled up as needed, allowing you to adjust to changes in demand or business growth. 
  • Cost savings – a cloud platform reduces the need for you to invest in hardware, infrastructure, and maintenance. 
  • Enhanced security – cloud provides robust security measures and can help you better protect your supply chain data and systems. 
  • Disaster recovery – a cloud-based platform provides a backup and recovery solution, which can help you mitigate the impact of natural disasters or other disruptions. 

What do these drivers and advantages mean for my business? 

Your organization can easily communicate and share data across its global supplier network. This capability increases visibility into the most pertinent information to plan more effectively and onboard alternative suppliers when needed. But more importantly, your organization gains the flexibility and responsiveness to address opportunities, risks, and disruptions early on – taking pressure off the shoulders of supply chain planners and managers. 

  1. In a 2022 study conducted by Accenture entitled, “How the cloud boosts supply chain innovation”, they found that 60% of cloud leaders are outperforming their competitors in supply chain transformation. More than half (53%) say that cloud is enabling them to achieve greater resiliency and sustainability. And 41% say that putting their supply chain in the cloud gives them greater visibility What’s the true value of cloud for supply chain?”, Mansi Arora, Managing Director – Strategy & Consulting, Supply Chain & Operations, Sourcing and Procurement, Accenture, November 29,2022 

How does the Logility cloud platform ensure platform stability and scalability?  

On the stability side, the Logility platform is hosted in world-class cloud data centers with state-of-the-art power, cooling, and security systems. This network of data centers is available across more than 65 global regions – all of which are selected based on the locations of our clients and their data residency concerns. This approach helps ensure that users access the cloud platform with performance that feels “local.” 

On the scalability side, the platform enables tremendous flexibility. It operates on resources that can scale horizontally or vertically, which allows your organization to connect to new partners or suppliers rapidly. In addition, aspects of the platform – such as compute, memory, storage, or bandwidth resources – can be scaled to meet evolving needs and market conditions. 

The combination of stability and scalability drives greater supply chain resilience. In other words, your supply chain organization gains the extensive visibility necessary to anticipate and predict market changes and risks across your network. 

Cost Competitiveness 

How does the cost of a cloud-based platform – compared to on-premises software – make the cloud a more competitive choice? 

Organizations always weigh quality and advantages with costs in every purchase decision – and that reality is undoubtedly true for cloud application hosting. Many CIOs and IT leaders have established strategic initiatives to move to the cloud, but ultimately their choices must add value to their core business.  

Compared to traditional on-premises infrastructure, a cloud platform can offer cost advantages in several ways: 

  • Capital expenditure (CAPEX) vs. Operating expenditure (OPEX): On-premises infrastructure requires you to invest significant capital expenditures upfront to acquire and set up hardware and software, whereas a cloud platform operates on a subscription-based model that allows you to manage costs as operating expenses over time. 
  • Scalability: A cloud platform offers you the flexibility to scale resources on demand. This elasticity allows you to control costs better by paying for accurately sized resources, rather than having to invest in infrastructure that may remain underutilized. 
  • Maintenance and upgrades: Maintaining and upgrading on-premises infrastructure can be costly and time-consuming. In contrast, with the cloud platform, Logility and Microsoft manage and maintain the infrastructure, reducing the burden on your business and lowering your costs. 

Logility’s cloud platform provides more cost-effective options to acquire, operate, and maintain your supply chain IT infrastructure, allowing you to manage your IT costs better and free up resources to invest in other areas of the business. 

The comparison comes down to fully understanding the total cost of ownership for on-premises systems. The cloud includes budget categories such as hypervisors, servers, licenses, maintenance, support, IT and support staff, backup, disaster recovery, security, and audits. But even if on-premises investments are distributed across different cost centers, the total cost of the cloud, plus the opportunity cost involved, makes better sense for supporting long-term digital transformation. 

Does the cost include a sandbox for continuous technological innovation and experimentation? 

The default pricing for the Logility Digital Supply Chain Platform includes one non-production environment and one production environment. 

Performance Expectations 

What level of performance can my organization expect from a cloud platform? 

When migrating to the cloud, system performance is a big focus during an agile upgrade project. It is part of the criteria evaluated in preparation for a go-live to ensure satisfaction and optimal business performance. Such concerns may include anything from processing runtimes to web application latency.  

The Logility platform addresses that concern by sizing environments based on critical operating parameters that define your data scale and positioning the cloud environment regionally to minimize latency. 

Does the license or contract limit the number of available test environments? 

Both the non-production and production environments for the Logility platform can be scaled as required to provide additional environments to align with temporary or more permanent organizational needs.  

You can request additional environment capacity and timelines through Logility’s request management system. Logility’s cloud experts then provision the environment based on the agreed-upon terms. 

Upgrades 

How quickly can we shift to the Logility cloud? 

Migration time will vary depending on your needs and other initiatives. You can achieve a focused migration in a matter of weeks. If the project is part of a bigger business transformation with defined outcomes, it may take up to a few months. 

How does the cloud ease the ability to acquire new applications and digital capabilities or take advantage of enhancements and upgrades for existing digital investments? 

Software upgrades are less challenging in the cloud because you can test and validate them in the cloud environment quickly and with less risk and cost to your organization. 

For instance, Logility releases updates and enhancements up to six times annually. This allows you to adopt new software faster to benefit from the most up-to-date features, performance, and security improvements. Additionally, the cloud platform subscription includes flexible cloud resources deployed and installed by Logility experts. 

If we’re using an older version of an on-premises Logility solution, can our organization move directly to the cloud without upgrading to the latest version of the Logility Digital Supply Chain Platform? 

In this situation, Logility works directly with you to define a project to upgrade the existing system as you migrate it to the Logility cloud environment. Doing so ensures that your users get the full benefit of the latest and greatest technology available in the cloud. 

Security 

What are the security advantages of cloud-based technology and hosting models? 

The security of cloud systems and data can never be emphasized enough. The cloud based Logility® Digital Supply Chain Platform has made considerable investments in its talent and systems and continuously refine its security posture.  

Key security features include: 

  • Annual third-penetration testing 
  • 24x7x365 security operations center (SOC) 
  • Annual SOC 2 Type II audit and attestation 
  • Geographically isolated encrypted data backup 
  • Encryption by default for data in transit and at rest 
  • Ongoing code vulnerability scanning, both dynamic and static analysis 
  • Continuous security awareness education for business developers and teams 
  • Single sign-on to maintain control over user access password, multi-factor authentication, and conditional access policies 
  • True disaster recovery capabilities 
  • Extended detection and response (XDR) and security information and event management (SIEM) systems to continually ingest telemetry that monitors systems for malicious behavior  

Can we maintain a data security model that relies on many Active Directory security groups assigned to SQL server roles? 

It is common for Logility implementation teams to configure data access and other role-based access controls during migration and implementation. The goal is to retain the existing security model as much as possible while aligning with the cloud identity gateway and security systems. Taking this step helps ensure the cloud platform is effectively managed going forward. 

What level of access does my organization have to run SQL queries against the cloud database? 

Data can be exposed for analytics purposes through several different mechanisms. The first is integration back into the data reporting and warehousing system. Data can also be replicated from underlying planning databases into cloud-based SQL databases that can be queried interactively. Replicated data can be refreshed frequently and approached in real-time. 

How is the ability to interface data from an ERP and data warehouse impacted when moving from an on-premises landscape to a hosted environment? 

The cloud platform includes a data integration layer that provides flexible integration options to and from on-premises systems. They include ERP templates, relational database connections, secure file transfer options, and RESTful web services.  

Logility delivers on that promise by partnering with you during implementation to select the secure integration approach that best fits your systems and integration patterns. 

Support Expertise and Responsibilities 

What responsibilities do my IT teams retain after moving to the cloud? 

Your IT teams often play a critical role in administering user access through an identity provider, defining data integration sources and transformation rules, and enforcing data access controls. In addition, they monitor the availability of data sources exposed or feed the application integration. 

Which support activities are included in the cloud platform subscription? 

In Logility’s case, the subscription covers a range of services to sustain, monitor, and manage the digital supply chain platform. In addition to 24×7 platform and integration support, you can access continuous improvement experts who help tune and enable new features as well as dedicated client success managers who guide your entire journey to the cloud. 

Is Logility a trusted cloud service provider? 

Logility has been hosting the Logility Digital Supply Chain Platform in the cloud for over 10 years, helping hundreds of organizations worldwide modernize their supply chain platforms. Our clients benefit from expertise in tuning forecasts, adjusting planning parameters, optimizing the supply plan, and tracking key supply chain metrics. 

To maintain nearly 1,000 servers under management, we’ve built global teams of application, integration, operations, and security experts who are available 24×7 worldwide. In addition, Logility has dedicated the last decade toward developing and refining the systems, teams, and procedures required to deliver a cloud experience that meets clients’ standards and expectations. 

Every innovation effort is backed by consulting teams with deep experience in architecting and implementing cloud-based systems.  

Logility has the technology, teams, tools, and processes to help guide you through your digital transformation process. Contact us to find out how we can navigate your cloud supply chain journey together. 

For any organization, “Demand” fundamentally originates through the ebbs and flows shaped by consumer habits, competitor and customer behavior, and various events that are happening in the world. Traditional demand forecasting methods look solely into historical volumes and miss the impact of these drivers. Driver-based forecasting is meant to truly address this problem.

In today’s VUCA (volatile, uncertain, complex, and ambiguous) world, solely relying on history means that organizations are blind to the different reasons for demand changes. The goal with driver-based forecasting is to leverage AI and ML to identify anomalies in history and match with known events, promotions, and portfolio updates. Moreover, every department within the organization has some insight about future demand– e.g., sales is aware of customer dynamics, marketing has information about campaigns and new launches, finance has information about budgets and working capital conditions etc.

By using drivers, businesses can simplify their forecast model and make it more responsive to changes in their business environment. Businesses can also align their forecast with strategic goals and initiatives and communicate them clearly to their stakeholders. 

Here are some of the benefits of using driver-based forecasting: 

  • Increased accuracy: It helps businesses to improve the accuracy of their forecasts between 10-30% by focusing on the data that has the greatest impact on their financial results. This can be especially helpful in businesses that are subject to rapid and unpredictable changes. 
  • Improved efficiency: Driver-based forecasting can help businesses to improve the efficiency of their forecasting process by simplifying the model and making it more automated. This can free up time for businesses to focus on other important tasks, such as strategic planning and decision-making. 
  • Increased collaboration: Driver-based forecasting can help businesses to improve collaboration between different departments by providing a common framework for understanding the drivers of financial performance. This can help businesses to make better decisions that are aligned with their strategic goals. 
  • Enhanced insights for decision-making: Driver-based forecasting can help businesses to make better decisions by providing insights into the impact of different scenarios on their financial performance. This can help businesses to evaluate trade-offs and alternatives, and to make more informed decisions about their future. 
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Overall, driver-based forecasting can be a valuable tool for businesses of all sizes. It can help businesses to improve the accuracy, efficiency, and collaboration of their forecasting process, to understand the key levers that can be used to influence their sales and revenue, and to subsequently make better decisions about their future. 

Other benefits of driver-based forecasting: 

  • Better understanding of the business: Driver-based forecasting can help businesses to better understand the drivers of their financial performance. This can lead to improved decision-making and strategic planning. 
  • Increased transparency: Driver-based forecasting can help to increase transparency within a business. This can help to build trust and alignment between different departments. 
  • Improved communication: Driver-based forecasting can help to improve communication with stakeholders, such as investors, creditors, and customers. This can help to build relationships and secure funding. 

If you are looking for a way to improve your forecasting process, driver-based forecasting is a good option to consider. It can help you to improve the accuracy, efficiency, and collaboration of your forecasting process, and to make better decisions about your future. 

You can learn more about how AI-first demand planning and forecasting solutions can advance your business to the next level here.

Supply Chain Software Implementation: A Prescriptive Approach to Optimize ROI 

Drawing from lessons learned over the past few years, supply chain leaders are eager to find new opportunities to make faster, more accurate, and consistent decisions in real time. And it’s no wonder why: market demand is constantly evolving, and customer expectations are becoming increasingly diverse. 

In response, most companies hastily select supply chain software based solely on functionality, pricing, support services, and reputation. However, this path does not guarantee a high-impact, yet low-risk, implementation. When selecting supply chain solutions partners, delivering lasting business value starts long before the first vendor meeting. 

“The move to the cloud has amplified the importance of measuring the value to be achieved. But that also means businesses must understand that value at the beginning of the transformation journey. This step may sound simple, but it is one of the most overlooked,” Evan Flinn, Director of Solution Architecture at Logility said. 

Set the right foundation for lasting success 

So, what’s involved in doing well at the beginning of a supply chain software implementation? According to Flinn, the answer is “outside-in thinking and figuring out which outcomes are desirable for the business.” 

“Reflecting on our experience delivering and supporting more than 800 client implementations, nothing empowers business, supply chain, and technology decision-makers more than a collaborative process that encourages clear thinking and strategizing with expertise and accurate data from the beginning of the implementation project.” 

This approach allows decision-makers to define four critical success factors that supply chain and business leaders should keep in mind during their implementation process: 

  • Availability of required data 
  • Accessibility of required data 
  • Proven integration options 
  • Required IT involvement and support 

This outside-in thinking helps create a strong business case for acquiring stakeholder buy-in, obtaining funding approval, and evaluating supply chain software. As the transformation progresses in the weeks after deploying the new solution, the program management team would then have the foundation to communicate continuously with executives, stakeholders, and users. Such communications include tangible improvements, business outcomes, and organizational opportunities for additional growth. 

“As simple as it sounds, strong program management and good communication are essential best practices for fostering stakeholder buy-in and user adoption company-wide. They allow users to see the progress the new technology is making and become inspired to continue using it within their daily workflow,” Scott Abbate, Executive Vice President of Business and Solution Consulting at Logility suggested. 

Optimize software ROI with prescriptive guidance 

To achieve such a high level of success, supply chain organizations need more than a knowledgeable software provider that offers exceptional capabilities at an affordable price. They also need a trusted partner with a prescriptive approach to supply chain planning. 

With Logility, our customers receive a uniquely personalized methodology tailored to their business needs. By prioritizing early wins, deployment, success measurement, and continuous improvement in that order, they better understand how to optimize the value of their software investment. 

The prescriptive approach guides the implementation of a configurable digital supply chain platform with the support of industry experts – all delivered through a five-step Prescriptive Engagement Process: 

1. Outcomes Workshop 

Our customers can clearly define their desired outcomes and identify the inhibitors to achieving them. Doing so helps create KPIs by which to measure performance and what organizational complications or changes are needed to execute. 

2. Value Case Prioritization 

A set of use cases is defined to produce the highest level of value. While a customer may need 30 different capabilities, starting with the five most fundamental ones is most effective to gain the highest-value returns. 

3. Solution Alignment 

This step demonstrates how the software can deliver high value on each top-selected use case as a basis for choosing a partner. 

4. Business Case Development 

Based on the value identified in the first two steps, customers can define a business case with a financial game plan based on that value. This includes recognizing the potential to be achieved in your industry and providing industry benchmarking based on Logility’s expertise. 

5. Value Assurance Workshop 

This last step helps identify how to realize the value regarding resources, timeliness, data accessibility, operational readiness, alignment, and variability. By creating a deployment plan and timeline, customers can maximize ROI and minimize risk early in the project. 

“Initial alignment, identification of business needs, and matching them to software capabilities, and all the way through value assurance – this is all paramount in ensuring the overall success of a supply chain software implementation and the entire business,” Flinn adds. 

For more, sign up for the on-demand webinar: From Risk to Reward – Supply Chain Software Implementation Success. 

From Risk to Reward: Mastering Supply Chain Software Implementation for Assured Success

With over 800 client implementations, we’ve identified the critical success factors supply chain and business leaders should keep in mind during their digital transformation.

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