Navigating the complexities of demand planning requires a blend of traditional wisdom and modern tools. This article, informed by B EYE’s hands-on experience with Anaplan, breaks down the essentials of demand planning.
We’ll explore its core concepts, showcase the transformative impact of AI, and highlight the unmatched benefits of platforms like Anaplan. Whether you’re new to the field or aiming to refine your strategies, this guide offers insights for every business.
Table of Contents
Demand Planning Methods: Traditional vs. ML & AI
Demand Planning and Demand Forecasting: What Is the Difference?
Why Is Demand Planning Important?
Benefits of Effective Demand Planning
Elements of the Demand Planning Process
15 Best Practices for Accurate Demand Planning
10 Ways AI Can Improve Demand Planning
Generative AI in Demand Planning
Anaplan: The Best Demand Planning Software
Optimize Your Demand Planning with Anaplan and B EYE
What Is Demand Planning?
Demand planning is the process of anticipating and calculating the future demand for a product or service through analysis of historical data, market trends, customer behavior, among other factors. A strong demand planning strategy is essential in supply chain management, allowing businesses to adjust their inventory, set production timelines, streamline procurement, and improve overall operations. It provides retailers with insights to estimate future product demands and to devise inventory strategies accordingly. Typically, demand planning merges elements of sales projections, inventory management, and supply chain operations to produce the most accurate forecasts possible.
Demand Planning Methods: Traditional vs. ML & AI
Demand forecasting requires a mix of quantitative and qualitative techniques. Quantitative techniques comprise statistical models such as time-series, regression, and machine learning (ML). These approaches draw on historical sales data and market trends to project future demand. On the flip side, qualitative techniques depend on expert insights and assessments, often derived from market research or surveys.
In terms of accuracy, ML forecasting outshines the conventional statistical approach.
With AI integration, a company can monitor each segment of its supply chain, from sales volume to stock depletion predictions (using SKU details). AI also identifies underperforming products that increase storage expenses and observes sales variations depending on seasons or specific times of the year.
In fact, research indicates that AI and ML can enhance demand forecast precision by 10 to 20 percent.
In a different report, McKinsey states that using AI for demand forecasting can reduce supply chain errors by up to 50%.
ML-driven forecasts use a broader spectrum of past and external information, offering sharper predictions. In contrast, traditional models tend to focus narrowly on specific segments of past internal data, which might lead to off-mark forecasts.
What’s more, AI and ML’s capacity to continuously learn from new datasets means they offer businesses increasingly precise forecasts coupled with insightful explanations, aiding decision-making.
While the conventional forecasting model often references historical data from 24 to 26 months back, its narrow lens and lack of real-time data inclusion diminish its accuracy (60/75%). In comparison, AI-fueled forecasting, tapping into real-time data and ML algorithms, outperforms the traditional methods. By considering factors like economic trends, market shifts, weather condition, and others, AI models can consistently hit an accuracy range of 95 to 98%. This empowers enterprises to rely on data-backed choices and maintain adaptability amid dynamic market changes.
Demand Planning and Forecasting: What Is the Difference?
Demand planning and forecasting share similarities, but still have unique characteristics.
Demand forecasting focuses on predicting consumer demand by analyzing historical sales data and customer buying patterns. Such forecasts also factor in seasonality and supply chain disruptions and their potential impact on future demand.
On the other hand, demand planning goes beyond prediction. It ensures that adequate resources, such as time, materials, or workforce, are in place to fully meet the demand.
However, it’s worth noting that many often use „demand forecasting“ and „demand planning“ interchangeably in discussions about demand management.
Keep Exploring: Forecasting and Annual Operational Planning
Why Is Demand Planning Important?
The market can change rapidly, and demand plans need to adapt just as swiftly. Without agile demand plans, companies risk facing stock-outs, leading to disappointed customers or, conversely, overstocked warehouses, stressed finance teams, and wasted capital.
Ideally, demand planners should stay ahead of the curve, making decisions based on current market data rather than only historical figures. The emergence of cloud-based planning platforms has made this proactive approach more feasible.
Benefits of Effective Demand Planning
Demand planning isn’t just a beneficial strategy—it’s a foundation for sustained growth and customer satisfaction. Proper demand planning touches every aspect of a business, from inventory management to customer experience. Here are some of the pronounced benefits.
Enhanced Resource Management
Demand planning allows direct-to-consumer (DTC) sellers to match inventory with varying demand trends.
This adaptability not only ensures efficient spending by reducing excess stock but also frees up time previously spent on unsold inventory management. This time can be better utilized to grow the brand and enhance customer experiences.
Improved Production and Labor Scheduling
By accurately predicting sales timings, manufacturing organizations can plan production, warehousing, and shipping more effectively.
Proper estimation of staffing needs ensures optimized task allocation and better resource utilization.
Stockout Prevention
No one benefits from stockouts; they upset customers and result in lost sales opportunities.
Demand planning ensures you order sufficient inventory at the right moment, maintaining stock levels and avoiding the dreaded stock-out scenario. This leads to timely order fulfillment and increased customer satisfaction.
Reduced Backorders
Backordering can be a strategic move during high demand periods. It allows sales to continue even with no stock on hand.
However, most brands would prefer consistent stock availability. Demand planning helps to achieve this balance, ensuring prompt stock replenishments and minimizing stockouts and backorders.
Minimized Excess Inventory
Overstocking can lead to increased holding costs and potential wastage, especially with perishable items.
Demand planning, by aligning sales with forecasts, helps in making informed inventory decisions, reducing the chances of overstocking.
Optimized Supply Chain and Logistics
Proper planning can mitigate the ‘bullwhip effect’, where small retail demand changes cause larger shifts up the supply chain, potentially disrupting inventory levels.
Accurate demand predictions also allow for efficient shipping, minimizing transportation costs.
Meeting Customer Expectations
Modern customers expect efficient services from brands. They appreciate seamless online experiences and swift product availability.
Proper demand planning ensures products are always available. A report highlighted that delivery time influences more than 87% of customers, impacting their buying decision. Therefore, having the desired products available can significantly boost customer retention.
Case Study: How B EYE Saved Over 2 Weeks of Manual Work Per Month with Integrated Business Planning
Elements of the Demand Planning Process
Navigating the complexities of modern markets requires more than intuition; it demands structured and strategic processes. Demand planning faces this challenge by balancing historical data with forward-thinking strategies. Here are the crucial elements that frame an effective demand planning process.
Trade Promotion Management
Trade promotions are strategies used primarily in retail to generate in-store demand through methods like discounts, giveaways, and in-store promotions. This management approach aims to make brands stand out via well-orchestrated promotional activities, fostering deeper relationships with retailers.
The objective is collaborative planning at multiple levels to tweak products and campaigns promptly. This ensures an efficient trade promotion spending plan that considers feedback from distributors and customers across all durations and regions.
A comprehensive analysis encompasses profit and loss data, pinpointing ineffective promotions and optimizing return on investment through extensive data utilization.
Product Portfolio Management
This process oversees every part of a product’s lifecycle, from its introduction to its eventual phase-out. The objective is to get a panoramic view of all products, understanding their interconnections and dependencies.
It includes planning for new product integration, understanding the effect of new introductions on existing products, and analyzing attachment rates. Proper management enables planners to comprehend the impact of one product line on another, maximizing profitability and market share.
Effective product portfolio management accelerates the journey of a product from conception to market. In collaborative settings, real-time detailed forecasting discerns potential market segments for new products, linking this process with sales, operations, production, and allocation planning.
Statistical Forecasting
This technique uses historical data and advanced algorithms to produce supply chain predictions, ensuring data-driven decisions that satisfy customers while preventing stock imbalances.
Demand planners can evaluate various algorithms to select the most accurate one for each product category.
Incorporating a forecasting dashboard helps tailor algorithm assumptions and measure accuracy metrics like mean absolute percentage error. It also allows for swift outlier detection based on user-set parameters, such as standard deviation.
Considering seasonality is vital in demand planning. Challenges like sudden weather changes or holiday shopping spikes require readiness. With statistical forecasting, these challenges are met by running multiple simulations, capturing factors like demand variability, pricing, and discounts.
AI-Driven Predictive Analytics
Unlike traditional methods which rely primarily on historical data, AI systems are continuously learning. They adapt to new patterns, behaviors, and anomalies in real-time, making the forecasting model more agile and responsive to unforeseen market changes.
AI can process vast and diverse datasets that were previously untapped in demand planning—like social media sentiment, online reviews, or even global events and news. This allows for a richer, multi-dimensional view of potential market influencers.
What’s more, AI can run thousands of predictive scenarios in a fraction of the time it takes traditional models. This helps businesses visualize a range of potential outcomes and devise strategies for each, enhancing preparedness.
With the power of neural networks and deep learning, AI models can improve forecast accuracy, minimizing the gap between predictions and actual outcomes.
AI systems can also automatically identify and alert planners about anomalies or outliers in the data, ensuring that potential issues are addressed before they impact the supply chain.
Finally, as the Internet of Things (IoT) continues to expand, AI can process real-time data from connected devices, from store traffic sensors to smart shelves. This real-time data influx can significantly enhance demand forecasting precision.
15 Best Practices for Accurate Demand Planning
Effective demand planning combines historical understanding, market awareness, technological integration, and cross-departmental collaboration. Establishing these best practices are crucial for businesses aiming for sustained growth and resilience. Here’s a closer look at these practices and their importance.
Historical Data Analysis
Understanding past sales trends, seasonality, and historical demand patterns forms the foundation for predicting future demands.
Use of Accurate Forecasting Methods
Utilizing statistical methods, machine learning algorithms, or other proven forecasting techniques that suit the industry and product type.
Understanding Market Trends
Being aware of economic factors, industry trends, market conditions, and competitive landscape helps in predicting demand changes.
Collaboration with Different Departments
Sales, marketing, production, and finance teams should work together to ensure that all relevant information is considered.
Customer Insights
Regular communication with key customers and understanding their needs and preferences can provide valuable information for demand planning.
Monitoring External Factors
Keeping an eye on external factors like regulatory changes, weather patterns, or geopolitical issues that might impact demand.
Regular Review and Adjustment
The demand plan should be flexible and regularly reviewed and adjusted to accommodate changes in the market or unforeseen events.
Use of AI Technology and Tools
Implementing advanced, AI-driven software that integrates various data sources and applies sophisticated algorithms can make the forecasting process more accurate and efficient.
Incorporating Promotions and Marketing Efforts
Understanding the impact of planned promotions, marketing campaigns, or discounts on demand is essential.
Managing Product Life Cycle
Recognizing where products are in their life cycle can help in more precise demand planning.
Consideration of Supply Chain Constraints
Taking into account the lead times, production capacities, and logistical constraints ensures that the demand plan is achievable.
Expert Opinion and Judgmental Forecasting
Sometimes, the insights of experienced industry professionals can be invaluable, especially when there’s a lack of hard data.
Clear Communication
Effective communication across the organization ensures that everyone understands and works towards the same goals.
Measuring Forecast Accuracy and Continuous Improvement
Regularly comparing forecasts with actual sales and learning from discrepancies can help in improving the accuracy of future predictions.
Risk Management
Building scenarios for different demand outcomes and having contingency plans in place.
Success Story: How B EYE Improved the Demand and Financial Planning Processes at a Multinational Pharmaceutical Company
10 Ways AI Can Improve Demand Planning
AI-driven tools present a clear advantage with at least ten distinct benefits over conventional methods. Simply put, they diminish manual interventions, slash errors born from intricate supply chain dynamics, and guide supply chain managers in procuring the optimal product quantity precisely when needed. Let’s take a better look at these advantages for a clearer understanding.
Workflow Automation and Streamlined Data Management
Supply chain professionals in mid-size to large enterprises often deal with numerous Excel files to process data from their ERP systems. This manual approach is time-consuming and often results in teams working with outdated information. AI tools, however, can seamlessly integrate with all data sources, offering real-time updates by consistently suggesting purchase, transfer, and manufacturing orders to the ERP system.
Dynamic Updates to Ordering Plans
Supply chain managers constantly wonder how sales will trend, what their current inventory should look like, or how potential delays influence their supply chain. AI-powered tools provide instant insights by simulating how different schedules intersect. They automatically refine your ordering plans, accounting for various demand and supply factors, ensuring your replenishment strategies are current and feasible.
Addressing Supplier Inconsistencies
Stable inventory management heavily relies on consistent supplier lead times. However, recent disruptions mean lead times can fluctuate dramatically. AI tools can swiftly adapt to these changes, ensuring ordering plans remain accurate despite supplier unpredictability.
Forecasting for Products Lacking Sales History
Predicting demand accurately necessitates reliable historical sales data. For new products lacking this, traditional methods falter. AI software, in contrast, can apply forecasting models from similar products and adjust these predictions as fresh data emerges.
Balancing Inventory Levels
The ultimate goal in inventory management is ensuring enough stock to meet demand without overstocking or facing stockouts. AI solutions excel here, perpetually aligning demand with supply by analyzing all pertinent data and constraints. This results in a constantly refined purchasing plan, primed for implementation.
Enhanced Scenario Planning
AI-driven tools allow for rapid simulation of various demand scenarios based on a multitude of factors, helping businesses to prepare for a wider range of potential outcomes.
Real-time Response to Market Changes
With AI’s ability to process data in real-time, businesses can adjust their strategies instantaneously in response to real-time market changes, ensuring they are always a step ahead.
Predictive Analytics for Preemptive Actions
Beyond just reacting to current market situations, AI can provide predictive analytics that allow businesses to take preemptive actions, potentially turning challenges into opportunities.
Personalized Customer Experiences
AI can help businesses understand individual customer behaviors and preferences in greater depth. This can lead to more personalized demand planning tailored to specific customer segments, enhancing customer satisfaction and loyalty.
Resource Optimization
By providing more accurate demand forecasts, AI ensures that resources (from raw materials to human labor) are utilized in the most efficient manner, leading to cost savings and reduced waste.
Generative AI in Demand Planning
Generative AI goes beyond traditional predictive models. By harnessing historical data, recognizing market trends, and considering various impactful factors like seasonality, promotions, and prevailing economic conditions, it crafts detailed and highly accurate future demand patterns. Coupled with established AI techniques, this innovative approach is empowering businesses to finetune their production timelines, diminish both overstocking and understocking challenges, and drastically reduce expensive supply chain disruptions. This evolution in demand planning brought about by Generative AI offers companies an edge in today’s complex market dynamics.
Anaplan: The Best Demand Planning Software
In the dynamic and ever-changing landscape of modern businesses, a robust, flexible, and precise demand planning solution becomes indispensable. Anaplan Demand Planning Software emerges as a powerful tool in this arena. It offers an exhaustive perspective on data and trends, adeptly sensing, shaping, and coordinating demand.
What sets Anaplan apart is its prowess in assimilating data from diverse functions. This empowers planners to devise meticulous forecasts for new product rollouts, strategize discontinuance based on performance insights, and recalibrate their projections swiftly when faced with unexpected variables, be it trade promotions or unpredictable challenges like natural events or labor issues.
The real power of Anaplan lies in its connected planning platform. It promises a unified approach to demand planning, enabling businesses to:
- Facilitate quicker and streamlined collaboration across teams and clientele, fortified by an enhanced user experience and adaptable modeling.
- Gain a deeper comprehension of forecast alterations and their root causes via forecast analytics.
- Achieve rapid implementation courtesy of a cloud-backed, ready-to-use application that can be tweaked as per business-specific requirements.
You May Also Like: Anaplan vs. Excel: Why Connected Planning Outshines Spreadsheets
Taking the platform’s capabilities up a notch is Anaplan PlanIQ. This advanced tool integrates the intelligence of Amazon’s top-tier forecast engine with Anaplan’s in-house algorithms like MVLR. Its objective? To furnish businesses with more insightful predictions while economizing resources. Astonishingly, in 70% of projections, forecasts rooted in machine learning or statistical methodologies either match or outperform expert-derived forecasts.
One of the standout features of PlanIQ is its adaptability. While it requires only a singular data type for forecasting, it can incorporate up to three, refining its learning curve to output predictions fine-tuned to specific circumstances. Its inherent intelligence ensures constant learning and fine-tuning of forecast models, negating the necessity for periodic retraining. This feature-rich offering empowers decision-makers to leverage the Anaplan platform nested within PlanIQ, facilitating a profound understanding of future determinants, predictions, and the ramifications of hypothetical scenarios. Additionally, PlanIQ empowers planners with the ability to craft varied scenarios for future demand spanning diverse sectors and applications, accelerates analyses, refines plans, and dissolves decision-making silos—all without necessitating proficiency in data science.
In an age where market dynamics are in constant flux, ascertaining precise demand for products becomes a herculean task. Erroneous projections can lead to resource wastage and dwindling profit margins. PlanIQ stands out by linking vast volumes of historical data with internal insights to fine-tune product demand forecasts, minimizing wastage. The provision for on-the-fly analysis of pivotal drivers ensures demand planners fine-tune their strategies to resonate with overarching business objectives.
Demand Planning FAQs
1. What is demand planning?
Demand planning is the systematic process of predicting the future demand for a product or service. It involves analyzing historical data, market trends, customer behavior, and other influential factors to estimate the quantity of a product or service that consumers will purchase in a future time period. By understanding and anticipating demand, businesses can make informed decisions about inventory levels, production schedules, and procurement strategies, ensuring they can meet customer needs while optimizing operational efficiency and cost.
2. What is demand planning in supply chain?
In the realm of supply chain management, demand planning plays a major role in aligning inventory levels with anticipated customer demand. It’s a crucial bridge between operational planning and the market’s demand, ensuring that businesses produce and stock the right amount of products. By accurately estimating demand, companies can mitigate the risks of stockouts (where demand exceeds supply) or overstock (where supply exceeds demand), both of which can have significant financial implications. This way, proper demand planning helps increase customer satisfaction, reduce costs, and improve cash flow.
3. How to do demand planning?
Demand planning typically follows a structured process:
Data Collection: Gather historical sales data, inventory levels, market intelligence, and any other relevant metrics.
Analysis: Analyze this data to identify patterns, trends, and seasonality.
Forecasting: Utilize statistical methods, machine learning algorithms, or other forecasting techniques to predict future demand.
Collaboration: Engage cross-functional teams – from sales and marketing to production and finance – to gather insights and refine the forecast.
Plan Implementation: Develop strategies to align production, procurement, and inventory management with the forecast.
Review & Adjust: Regularly compare forecasts against actual outcomes, understand discrepancies, and adjust processes and predictions accordingly.
4. What is demand planning software?
Demand planning software is a digital tool or system designed to assist businesses in forecasting future product or service demand. These software solutions typically provide functionalities to collect and analyze historical data, incorporate market trends, and run simulations based on different scenarios. Many modern demand planning software tools also integrate machine learning and AI capabilities, allowing for more accurate and dynamic forecasting. Moreover, they often offer collaborative features, enabling various departments within an organization to input and share data seamlessly, ensuring a more holistic and informed demand plan.
5. What is the best demand planning tool?
Choosing the „best“ demand planning tool often depends on a company’s specific needs, industry, size, and existing infrastructure. However, in terms of versatility, advanced capabilities, and ease of use, Anaplan emerges as a frontrunner. Anaplan’s platform not only provides comprehensive forecasting tools but also integrates state-of-the-art AI capabilities, ensuring dynamic and highly accurate demand predictions. Its user-friendly interface combined with its powerful backend makes it a top choice for businesses aiming for both precision and scalability in their demand planning.
Other notable tools in the market include Oracle Demand Planning, SAP Integrated Business Planning (IBP), and Logility. While each of these tools brings its unique set of features to the table, Anaplan’s holistic approach to demand planning, coupled with its cutting-edge technological advancements, positions it a cut above the rest for many businesses.
Optimize Your Demand Planning with Anaplan and B EYE
Anaplan is a leading solution in demand planning, offering precision and advanced capabilities. By leveraging Anaplan’s tools through B EYE—a dedicated Anaplan partner—you’re positioning your business for enhanced accuracy and efficiency in forecasting. Our B EYE team deeply understands Anaplan’s platform and can optimize it for your specific business requirements, ensuring straightforward yet impactful demand planning outcomes. Ready to redefine your demand planning strategy? Book a consultation with our experts today and embark on a journey toward operational excellence and heightened profitability.
Book a Consultation
