Blog

Redesigned Navigation

New Product Management Page

New Promo Tool Page

Interface Localization

New Levels of Business Aggregation

Optimization of Core Computational Processes

Adaptive Data Display

How Companies Lose Money Due to Poor Inventory Management

Stop Wasting Money: Why Overstock Is a Major Profit Killer for Retailers

Out-of-Stock: Why It’s Dangerous for Business

How Retailers Can Optimize Inventory Levels and Stop Losing Money

Using AI to Analyze Historical Sales, Seasonality, and Trends

How AI in the Retail Industry Helps Businesses Reduce Waste

How the SMART Demand Forecast Solution Helped the McDonald’s Georgia Team Improve Forecast Accuracy

• 83% forecast accuracy for each restaurant based on weekly data aggregation over a 4-week period.
• 80% forecast accuracy for each restaurant based on weekly data aggregation over a 12-week period.
• An average forecast deviation of no more than 5%, which aligns with global business standards.

How to Make Data Work for Your Business

The Data-Driven Approach in Retail

AI in Retail: Pros and Cons

Myth 1: Good analysts can forecast better than AI

Myth 2: The implementation of AI in retail may lead to mass layoffs

Myth 3: AI in retail is costly, and its benefits are hard to measure

Why Time-Tested Demand Forecasting Methods No Longer Work

• Regression models — a statistical tool that helps predict future demand based on relationships between variables. Simply put, the model analyzes how changes in certain factors (such as price, seasonality, advertising, or consumer income levels) affect others – particularly the demand for a product or service. This method provides a quantitative estimate and allows for the consideration of multiple factors at once but struggles with sudden data changes.
• Trend analysis — a method based on identifying long-term changes in data. It helps uncover the overall direction: whether demand for a product is rising, falling, or remaining stable over time. This analysis is useful for strategic planning and is often applied in predictable environments. However, trend models don’t account for seasonal fluctuations or short-term spikes, potentially offering an oversimplified view of reality.
• Expert judgement — a forecasting method based on professional assumptions, experience, and intuition. It involves insights from sales managers, marketers, or external analysts who understand the market’s specifics. This method is quick and accessible, especially for small businesses, but its main drawback is subjectivity: forecasts can be biased or based on incomplete information.
• Moving average — a simple statistical method used to smooth out data fluctuations. It calculates the average of previous periods to identify a baseline trend. For example, a three-month moving average considers the past three months to calculate each new point. The method is effective under stable conditions but insensitive to rapid changes.
• Exponential smoothing — an enhanced version of the previous method, where more weight is given to recent data. As a result, the model responds more quickly to changes in demand. It’s used for short-term forecasting, particularly when fast response to fluctuations is required. However, this model also doesn’t take causal relationships into account and may be unreliable during sharp market shifts.
• Seasonal forecasting — a method that factors in recurring demand fluctuations during specific times of the year (holidays, seasons, events, etc.). It helps isolate and neutralize the seasonal component to more accurately assess the overall trend. This approach is useful in retail, tourism, and the FMCG (fast-moving consumer goods) sector. However, seasonal forecasting doesn’t work outside of established cycles and doesn’t account for unpredictable changes.

What Has Changed with the Emergence of AI: Obvious and Hidden Advantages of Artificial Intelligence in Demand Forecasting

• Macroeconomic indicators: inflation rates, currency fluctuations, market trends.
• Climatic and seasonal factors: weather changes, natural events, etc.
• E-commerce data: user behavior, impact of advertising (including on social media).
• Regulatory changes: new taxes, regulatory restrictions, and more.

Comparing Traditional Forecasting and AI Across Specific Criteria

Seamless Transformation – When and How to Integrate AI Forecasting Without Risking Your Business

How to Successfully Implement AI in Demand Forecasting – Tips and Best Practices:

1. Choose a Reliable Vendor: Vendors usually provide full technical support, adaptation to specific business processes, and guidance at every stage of integration – as SMART business does when implementing the SMART Demand Forecast
2. Ensure Data Quality: Without high-quality, structured historical data, AI cannot function effectively. The first step is auditing your data sources, types, and quality. Ensure regular updates, standardization, unified formats, and the elimination of duplicates. If you’re unsure how to do this best – consult your vendor. For example, SMART business agrees on a unified data structure with the client as early as the diagnostic stage and provides ready-to-use templates.
3. Prepare Your Technical Infrastructure: This may involve an internal computing base or connection to cloud platforms that provide scalability and speed. Make sure your technical capabilities align with the requirements of your chosen solution – your vendor can also help with this.
4. Launch a Pilot Project: Start by rolling out the system in one region, SKU, or distribution channel. This allows you to test the solution with minimal risk and evaluate its business value, helping you decide whether to scale the innovation across your entire company.
5. Build a Cross-Functional Team: AI forecasting doesn’t just concern IT. Depending on your industry, logistics, marketing, and operations teams may be involved. Bring together employees who will be directly engaged in the forecasting process.
6. Train the Team: Explain how the new system works and train employees to use it. Encourage feedback during integration and be responsive to it.
7. Involve Analysts: Their role goes beyond interpreting results – they also ensure that forecast accuracy KPIs are met. AI is, first and foremost, a tool in human hands, not a replacement. It handles the routine and frees up your team for higher-priority tasks.
8. Extra Tip: If you’re unsure about implementing process changes, introduce the solution gradually. Initially run AI models in parallel with your existing methods in test mode, and compare the results.

How the Use of AI in Supply Chain Management Helps Businesses Respond Quickly to Market Changes and Make Strategic Decisions

Key Challenges in Supply Chain Identified by Experts:

1. Market Volatility – Economic crises, pandemics, and war create significant fluctuations in demand. Supply chains need support to manage these disruptions effectively. When demand changes instantly and unpredictably, companies must have flexible and adaptive systems. AI for Supply Chain Management becomes a key tool for timely adjustments in supply, production, and logistics planning, helping to minimize losses and optimize resources.
2. Forecasting Issues – Traditional manual demand forecasting methods, based on historical data and linear models, fail to deliver accurate results in dynamic conditions. Demand for products and services shifts faster than ever before, often unpredictably. It is no longer enough to simply observe and react afterwards; businesses need a proactive approach that includes scenario planning for various possible developments. Companies must learn to act based on demand forecasts rather than merely responding to unexpected changes.
3. Logistics Flexibility – Overloaded border checkpoints, delays, container shortages, labor shortages (including a lack of drivers and warehouse workers), and rising logistics costs all pose serious obstacles to efficient supply chain operations. These challenges not only increase delivery times but also raise costs, affecting overall company profitability. AI in logistics and supply chain management helps overcome these hurdles.
4. Lack of Transparency – One of the major issues in modern supply chains is the lack of transparency in the systems used to manage supply operations. The more complex and branched these systems become, the harder it is to maintain a clear view of the entire supply chain. Difficulties in tracking each stage of the supply process lead to risks such as delays, errors, and even losses. To ensure smooth operations, businesses must implement more transparent, integrated systems that provide real-time monitoring and control at every stage of the supply chain.

Business Benefits of Digitalization

• Automation of Key Processes – Demand forecasting involves processing vast amounts of Big Data. A crucial methodology in this context is Sales Execution, the process of implementing a sales strategy. It relies on real-time decision-making based on up-to-date data. However, for this methodology to be effective, businesses need modern systems with high computational power, as analyzing and processing large datasets in real time is impossible without them.
• Information Aggregation – Digitalization significantly enhances this area as well. For example, AI and IoT in Supply Chain unlock new possibilities for logistics management. IoT (Internet of Things) technology enables real-time tracking of cargo locations, while AI helps analyze this data, predict delays, and optimize routes. This allows businesses to monitor shipments, identify causes of delays (such as customs clearance issues), and respond promptly to any disruptions or route changes. Information aggregation improves supply chain control, enhances planning, and enables more informed decision-making.
• Big Data Analytics and Demand Forecasting in Supply Chains – Digitalization provides extensive capabilities for prediction, forecasting, and data utilization. This applies not only to internal Big Data collected and used by businesses but also to external data sources such as social media activity, website clicks, and other digital interactions. Modern systems can efficiently gather and process this information in real time, providing valuable insights for businesses.
• Cloud Solutions – Enable access to synchronized data from anywhere in the world. In the Supply Chain context, cloud-based solutions empower internationally operating companies to effectively manage logistics and supply processes through data synchronization, real-time monitoring, and remote management. Cross-functional teams and business partners can collaborate on data, negotiate deals, adjust orders, or plan delivery routes – regardless of their physical location, whether in different cities or countries.

The Benefits of Using Supply Chain AI

Comparison of Methodologies for Supply Chain Optimization: S&OP, S&OE, IBP, DDMRP, and Others. When and Which Ones Should Be Used to Maximize Supply Chain Efficiency?​

Overview of the Sales & Operations Planning Methodology

• Product Review involves analyzing the product portfolio, new product plans, and competitors.
• Demand Review includes pricing strategy formation, promotional activity planning, and demand forecasting.
• Supply Review focuses on resource analysis, supply scenario development, and inventory optimization.
• Financial Review is an essential stage where demand and supply plans are aligned, and financial forecasts are developed.
• Management Review concludes the process, involving a review of key performance indicators and strategy adjustments.

Overview of the Sales & Operations Execution Methodology

Overview of the IBP Methodology

Overview of the DRP Methodology

Overview of the MRP Methodology

Overview of the DDMRP Methodology​

Seasonality, Anomalies, and Unpredictable Events: How the Use of Artificial Intelligence in Supply Chain Management Helps Businesses Adapt – Practical Tips for Managing Unexpected Situations​

• Increase in demand forecasting accuracy by 20-30%,
• Reduction of stock levels after peak periods by 15-25%,
• Optimization of inventory and release of working capital from frozen stock.

Example of AI Usage in the Food Supply Chain for McDonald’s Georgia

1. Inaccurate forecasting at the individual restaurant level, leading to either shortages or surpluses.
2. Increased operational costs due to the need for quick responses to demand.
3. Excessive staff workload due to manual demand analysis.

1. An increase in sales forecast accuracy to 83.1% (based on 4-week data),
2. A forecast deviation rate of up to 5%, which is considered the standard in the global business community,
3. Better inventory management, avoiding losses.

• A sudden increase in demand due to a viral trend on social media (TikTok effect),
• A drop in sales due to geopolitical risks, economic crises, etc.

• Accounting for anomalous trends related to exceptional events.
• Using external sources to predict changes in demand and adapt the product range.
• Minimizing product shortages through more accurate supply planning.
• Reducing losses from excessive product discounts and decreasing write-offs.
• Increasing average sales by 15-20% due to optimal pricing and effective inventory management.

Example of AI Use in Demand Forecasting Within the Pharmaceutical Industry

• Distributors can flexibly manage inventory,
• The risk of shortages and expired goods is reduced,
• Warehouse storage costs are cut.

Key Takeaways That Will Benefit Businesses and Help Understand the Role of AI in the Supply Chain

• There is no perfect universal approach – the demand forecasting strategy must be tailored to the specific business, operational scale, and level of uncertainty.
• AI does not replace people – it helps them make more accurate decisions. Analysts can focus on strategic tasks rather than routine calculations.
• Focus on forecast accuracy, as it directly impacts logistics, procurement, and the financial flows of the company.
• Work proactively, not reactively – use AI not just to respond to changes, but to predict them and build flexible strategies.
• Integrate AI into your Supply Chain and other key processes with SMART Demand Forecast – a comprehensive tool that helps companies stay one step ahead by adapting supply chains to the real challenges of the market.

• • Explored and Implemented Lazy Loading for Interface Rendering Optimization Lazy Loading allows critical elements of the page to load first, while non-critical elements load gradually, which is particularly important for users with slow network connections.
  • Optimized Page Performance We reduced the number of elements on the page and limited API requests during the execution of main business processes. Pages now only display the necessary system performance indicators, and API requests are made after important processes have been completed. This reduces the load on the system, which is important for users with less powerful devices or poor network connections.
  • Convenient Settings Navigation Panel Added We added a navigation panel for main pages within the interface. This update simplifies and speeds up navigation within the system, improving the user experience.
  • Improved Interface Interactivity Enhancements have been made to user interaction with the system, making the interface more user-friendly, dynamic, and appealing. This includes improvements to the system elements users interact with daily – making all necessary tools more accessible.

• Optimized Anomaly Calculation and Upload Process The database entry for anomalies has sped up by 30–40%. Thanks to process modifications, calculations are faster and handling large datasets is more efficient. This enables the business to respond quickly to unusual situations and improve forecast accuracy.
• Improved STL and Autoencoder Algorithms This helps better identify deviations and provide more accurate forecasts. As a result, you can more effectively manage inventory, optimize supply processes, and reduce financial risks for your business.
• Optimized Default Period Calculation on the “Anomaly Processing” Page A check for the first sales day has been introduced to avoid empty columns appearing in the tables. Now, period selection is more intuitive, and data display is more accurate.

The Tasks of Demand Forecasting:

1. Optimizing inventory levels,
2. Minimizing surplus or stock shortages,
3. Ensuring stability in supply chain processes,
4. Planning production and marketing activities based on accurate data.

So, What is Demand Forecasting?

Challenges in Demand Forecasting Faced by Businesses

1. Insufficient or low-quality data – If demand-related information is incomplete or inaccurate, forecasts may be misleading.
2. Demand fluctuations – Seasonality, economic trends, legislative changes, and other external factors can make predictions less reliable.
3. Integration difficulties – Incorporating forecast data into daily operations requires a holistic approach, a high level of expertise, and seamless collaboration among cross-functional teams.
4. Reliance on outdated manual forecasting methods – Traditional spreadsheets, a lack of automation, and simple demand forecasting formulas fail to account for a wide range of variables, making it difficult to adapt to new data quickly and reducing forecast accuracy.

How Forecasting Works and Key Steps to Building Accurate Demand Forecast

High-Quality Data Collection

Analysis of Factors Affecting Demand

Selecting the Right Demand Forecasting Method

Building the Demand Forecasting Model

Testing and Optimizing the Forecasting Model

• Applying the forecast to key business processes – In the final stage, the forecast is integrated into critical business operations such as inventory management, production planning, and marketing strategies, providing tangible value to the company.

Benefits of Demand Forecasting:

• Optimized Supply Chain Processes: Accurate product demand forecasting helps businesses manage inventory more efficiently, minimizing the risks of out-of-stocking or overstocking. This reduces storage costs and prevents lost sales due to unavailable products or services. By basing supply chain operations on reliable demand forecasts, companies can ensure smooth product movement along the supply chain, minimizing emergency shipments and delays.
• Better Production Planning: With precise demand forecasts, manufacturing teams can optimize resource utilization, ensuring the timely availability of raw materials and maintaining stable production flows.
• More Effective Marketing Campaigns: Understanding when and which products or services will be in high demand allows marketers to launch well-timed campaigns, maximizing sales potential.
• Cost Reduction and P&L Impact: Accurate demand forecasting helps lower costs by optimizing key business processes such as storage, production, marketing, procurement, and supply chain management. This helps align demand with available resources, positively affecting the company’s P&L since cost reduction and boosting the efficiency of these processes leads to the increase in both gross profit and net revenue.
• Enhanced Customer Service: Reliable forecasts allow businesses to plan resources efficiently. For example, knowing future demand helps ensure the right number of employees and products are available at specific locations to meet customer needs.

What Factors Influence Demand Forecasting Accuracy?

Factors Affecting Demand Forecasting

• External Factors and Social Events: Think back to the pandemic, when everyone was rushing to buy protective masks and sanitizers. Trend analysis is a powerful tool for predicting significant and sudden changes in customer preferences.
• Pricing Policy: Discounts, promotions, or even a slight price increase can drastically impact your sales. People will always look for the best price-to-quality ratio. Understanding this pattern enables businesses to more accurately predict how demand will shift.
• Seasonality: Imagine an ice cream shop in winter versus summer. Obviously, demand drops in cold weather and surges in the heat. Understanding seasonal fluctuations and having precise demand forecasting for inventory helps businesses plan production capacity and stock levels, avoiding the pitfalls of overproduction or shortages.
• Economic Factors: Consumer purchasing power is shaped by income levels, inflation, and currency fluctuations.
• Competition: The actions of your competitors also play a crucial role. Analyzing the competitive landscape allows businesses to timely adapt their own strategies to maintain an advantage.

What Is Short-Term and Long-Term Demand Forecasting, and Which Approach to Choose?

Short-Term Demand Forecasting

Long-Term Demand Forecasting

What Are Quantitative and Qualitative Demand Forecasting Methods, and When Should They Be Used?

Quantitative Demand Forecasting Methods

Qualitative Demand Forecasting Methods

What Demand Forecasting Models Exist?

What Are Demand Forecasting Techniques, and Which Ones Best Meet the Needs of Modern Businesses?

AI-Powered Demand Forecasting: Advantages and Challenges

AI-Powered Forecasting of Seasonal Demand Fluctuations and Promotions

How to Use AI for Forecasting Demand for New Products?

Machine Learning in Demand Forecasting

What ML Models Are Used for Demand Forecasting?

• Linear Regression – A simple yet effective model that analyzes the relationship between demand and key influencing factors.
• Random Forest – An ensemble model that works well with many variables and complex interdependencies.
• Gradient Boosting (XGBoost, LightGBM) – Ensures high forecasting accuracy by considering multiple factors simultaneously.
• Clustering Methods (K-means) – Helps group data to identify similar patterns and trends.
• Deep Neural Networks (LSTM – Long Short-Term Memory) – Particularly effective for time-series forecasting, making them useful for dynamic markets with rapid demand fluctuations.
• Transfer Learning Methods – Applied in cases where historical data is insufficient, such as when entering new markets or launching new products.

Enterprise Demand Forecasting – Your Key to Efficiency and Seamless Supply Chain Operations

Demand Forecasting in Inventory Management and Stock Optimization

Demand Forecasting for Finished Goods

How Can Demand Forecasting Be Useful in Various Business Industries?

• In retail – product demand forecasting helps set optimal inventory levels to meet customer needs without overstocking. Additionally, accurate forecasts allow for effective assortment and stock planning for each individual store, considering location-specific demand.
• In the apparel industry – accurate demand forecasts allow for better production planning, optimized raw material purchasing, and inventory management, reducing material costs and minimizing the risks of overproduction or product shortages.
• In the automotive industry – it helps optimize production lines, which is crucial to avoid delays in car deliveries and spare parts.
• In the aviation industry – with precise forecasting, airlines can better plan their transport volumes and workforce requirements, reducing fuel and personnel costs.
• In the fashion industry – a key aspect in this sector is accounting for seasonality and trends. Accurate demand forecasting for seasonal collections helps companies plan purchasing and production volumes more effectively, minimizing unsold stock, avoiding prolonged sales, and lowering marketing campaign costs.
• In the food industry – forecasting helps predict demand fluctuations, preventing costs from waste, surpluses, or ingredient shortages, ensuring timely and quality production.
• In manufacturing (industrial) sectors – demand forecasting allows companies to optimize production and raw material purchases according to market needs, increasing the efficiency of key business processes and reducing costs.
• In the pharmaceutical industry – demand forecasting helps manage pharmaceutical inventory, considering regional needs and seasonal disease fluctuations.
• In the service sector – forecasting aids in optimizing and efficiently distributing labor resources, ensuring quality customer service.
• In the tourism industry – accurate demand forecasting allows travel companies to better plan tours, offering optimal pricing to customers and reducing the risk of overloading popular destinations.
• In the hospitality industry (HoReCa) – demand forecasting helps manage service levels, ensuring high-quality service for guests and maximizing hotel profits. For restaurants, forecasting helps set up efficient product ordering, reducing ingredient purchasing costs and preventing waste due to overstock or shortages.

• Customer service is in the lead. More and more companies from all over the world trust AI to provide a high level of service. Various recommendation systems operate on its basis, using the analysis of previous consumer preferences to generate advice on improving their satisfaction; chatbots and virtual assistants that answer frequently asked customer questions 24/7. According to a McKinsey study, in North America, about half of customer requests in the banking, telecommunications and software companies are closed by machines, including those based on AI. While employees can focus on solving service issues that require more thorough processing by a specialist.
• In supply chain management, in particular for demand forecasting and in logistics, for optimizing delivery routes.
• In marketing for creating personalized advertising, that is, the most relevant offers and advertisements for a specific consumer.
• In financial analysis – to improve credit risk analysis and identify fraudulent activities with a high degree of accuracy. In particular, with AI, financial institutions can automate the process of assessing creditworthiness of clients and instantly identify suspicious transactions, reducing risks and minimizing possible losses.
• In healthcare – to diagnose diseases. The experience of PathAI, a company that creates technologies based on machine learning for analyzing tissue samples for pathology, is interesting. And pharmacists from Atomwise use deep learning technologies to speed up the process of inventing new drugs. Here, AI analyzes billions of possible chemical compounds in search of promising areas for research.
• In manufacturing – to automate production lines, predict inventory needs and optimize the workflow.
• To protect against “phone pranksters”. For example, Motorola Solutions specialists use AI to recognize voice commands and transcribe calls to the 911 service in real time. This helps to provide the necessary information faster and more accurately in crisis situations by filtering out “phone pranksters”. It is based on an accurate assessment of the emotional state of callers
• In HR and recruiting – for CV analysis, forecasting potential candidates for vacancies, conducting initial interviews and reskilling and upskilling of personnel. For example, the educational technology company Skillsoft has developed Conversation AI Simulator (CAISY). The program allows users to practice conducting business discussions, acquire skills in providing business proposals, and resolving conflict situations in the process of customer service.

IT ecosystem of AI-based solutions from SMART business

• SMART Demand Forecast is a demand forecasting system based on machine learning and AI. Provides comprehensive forecasting for regular and promotional sales at different levels of granularity.
• SMART Price Insights is an AI-based pricing solution that fully automates the pricing process and creates a single space for price management. Thanks to dynamic pricing, you can always meet market demands, and an adaptive strategy settings system will allow you to quickly and effectively respond to business challenges.
• SMART Personal Engagement is a system for optimizing marketing processes and personal interaction with clients. It is a kind of target audience designer. It allows you to segment huge amounts of client data and personalize advertising campaigns. An indispensable assistant when it comes to finding the optimal communication strategy.
• SMART Flexi Scheduler is a workforce management system for flexible management of teams’ working hours. AI algorithms analyze historical data and predict future needs, ensuring efficient use of personnel and reducing costs. The solution allows you to accurately predict the need to attract personnel (taking into account qualification requirements and restrictions).

SMART Demand Forecast – your “sixth sense” in building forecasts

• 83% accuracy of sales forecasting for each restaurant based on weekly data aggregation for a period of 4 weeks,
• 80% accuracy of sales forecasting for each restaurant based on weekly data aggregation for a period of 12 weeks,
• Deviations on average up to 5% from forecast fulfillment, which is the norm among the global business community.

What you need to know about SMART Demand Forecast

• reduce the workload of cross-functional teams,
• reduce the amount of stock to the optimal level,
• improve the level of service due to more accurate response to consumer requests and ensuring a higher level of product availability,
• reduce the number of write-offs,
• make informed management decisions,
• quickly receive reports.

How do AI algorithms analyze data, forecast demand, and help build effective sales strategies?

How to Fulfill a Sales Plan with the “Sales Plan = Demand * Strategy” formula?

Step 1: Demand

• The total volume of the organic cosmetics market in the region is 10 million units of goods.
• The company occupies 10% of the market, therefore, their share is 1 million units of goods.
• Demand is predicted to increase by 15%, which gives 1.15 million units of potential demand.

Step 2: Strategy

• Range Expansion: it is planned to add a new line of hair care products that meet market demands.
• Marketing Investments: it is planned to increase the budget for marketing campaigns by 30%, including the use of social networks, involving influencers, developing partnership programs, etc.
• Distribution Expansion: the company plans to enter new online platforms and physical stores to increase the number of points of sale by 25%.
• Promotions and Discounts: it is planned to stimulate demand during seasonal promotions and attract new customers through great deals.

Step 3: Sales Plan

• Projected demand: 1.15 million units.
• Strategic activities (range expansion, marketing, distribution): through these activities, the company plans to increase its market share by 20%.
• Expected Result: Sales Plan = 1.15 million (projected demand) * 1.2 (market share increase strategy) = 1.38 million units of goods.

1. Accurate demand forecasting: unlike manual estimates and assumptions, the solution will provide data based on real scenarios. Therefore, instead of simply forecasting a 15% increase in demand, SMART Demand Forecast can consider more granular factors, such as economic fluctuations, competitors’ behavior, seasonality (but not just an increase in demand in the summer for skin care products, but also weather conditions, tourist seasons and vacations, etc.). The system will allow you to get a more accurate forecast of regular and promotional sales.
2. Assortment and new product planning: thanks to the use of AI algorithms, the system can detect hidden patterns. The solution analyzes not only the company’s internal data, but also external factors, quickly processing large amounts of data. Therefore, the system can suggest that the demand for natural shampoos with certain ingredients is growing, while conditioners with other components are losing popularity. Thanks to this, the Company will be able to direct resources to products with the greatest sales potential.
3. Inventory and distribution management: one of the key aspects of effective demand forecasting is inventory management. Thanks to an accurate forecast that can be transferred to inventory management systems, the company minimizes the risk of product shortages (out-of-stock).
4. Performance analysis and strategy adjustment: SMART Demand Forecast provides tools for forecasting regular and promotional sales compared to the plan, which will help the company adjust its strategy in real time. For example, if the demand for certain products does not match the forecasts, the solution will help adjust management decisions to achieve goals or change the marketing strategy according to one of the scenarios.

Why do promotions not work and how to solve this with the help of scenario sales forecasting?

1. Flexibility and adaptability: manual forecasting methods are often based on linear models or fixed assumptions that do not consider dynamic market changes. Scenario forecasting allows you to model different developments.
2. Accounting for uncertainties: scenario forecasting allows you to work with several scenarios – from optimistic to pessimistic. This ability allows you to be prepared for different challenges and think through backup plans.
3. Proactive planning: scenario forecasting allows businesses to act ahead of the curve, responding to possible risks and challenges before they affect results. Thanks to this, companies can better prepare for possible changes, developing appropriate strategies in advance.
4. Integration of modern technologies: scenario forecasting effectively uses modern tools with AI technologies. This allows you to create more accurate and deeper forecasts based on huge amounts of data that traditional methods cannot process with the same efficiency.