What is demand planning?

Think of demand planning as a balancing strategy, where the goal is to accurately forecast customer demand so that the right products are available at in the right place, in the right quantity, at the right time. To get there, demand planning relies on sales trends, market shifts, and lead times to keep inventory levels optimal.
Sounds complicated? It is. Supply chains are deeply interconnected, and a single miscalculation at any stage can set off a domino effect that will leave businesses scrambling or customers empty-handed.

Demand planning for inventory management

Whether literal or figurative, every product has an expiration date. Effective demand planning is necessary for knowing how much inventory to keep to meet customer needs, without the financial drain of overstocking or the lost revenue from stockouts.
Poor inventory management can have a massive financial impact: according to the global consulting company McKinsey, companies that can predict customer behavior more accurately reduce inventory levels by 20%, leading to significant reductions in inventory costs [1,2].

The importance of forecast accuracy

The value of accurate demand planning extends far beyond inventory management. Forecasting is also a strategic tool for financial planning. Critical business decisions such as pricing strategies, revenue projections, and capacity planning are directly tied to the demand management process. A precise forecast helps businesses make data-driven decisions that foster growth.

Collaborative planning for Supply Chain optimization

Supply chain efficiency is a team effort, where even the best forecasts can fall short if there is poor alignment between departments.
To understand why the demand management process is not an isolated task, let’s break down the complex web of a supply chain. It all starts with suppliers delivering raw materials to manufacturing facilities, where products are produced. From there, finished goods are sent to distribution centers, awaiting shipment to retail stores or directly to customers. Unexpected disruptions at any step of the chain can throw the entire system off balance. Imagine a scenario where the sales team anticipates high demand, but procurement fails to secure enough raw materials. Or if production increases, but warehouses are already at full capacity. Even identifying which finished products drive the highest profits through an ABC analysis is crucial for knowing which goods to prioritize at the very first step of the chain.
So, how do businesses navigate these challenges and build a more resilient approach to demand forecasting? By following industry best practices.

Best practices for implementing demand planning

At its core, forecasting relies on two types of information: certain data, such as known warehouse stock, and uncertain factors, like fluctuating customer demand. Success depends on estimating these uncertainties and making informed decisions to maintain the right stock levels. Identifying an issue in your demand planning is not enough – if the software used to make predictions takes too long to adjust, your business risks missing sales opportunities and becoming trapped in operational inefficiencies.
Let’s break down the steps to mastering demand planning:

Collecting and preparing data

Strong demand planning starts with high-quality data. This involves collecting information from different sources and formats, integrating it, and cleansing it for analysis. The faster and more adaptable your system, the better.

Defining process models

Should your business prioritize aggressive stockpiling or a lean inventory approach? Determining the best strategy is rarely straightforward. The same applies to choosing the right forecasting model for each product, as not all products behave in the same way! For example, printer cartridges tend to follow a stable and predictable pattern, while high-end smartphones see sudden demand spikes and shorter life cycles.

Choosing the right software

Invest in demand planning software with advanced AI and ML algorithms, as well as other capabilities that enhance forecast accuracy, decision-making, and allow for quick adjustments. A robust platform should support real-time recalculations and generate multiple demand forecasts based on specific products, adjusting to different assumptions and probabilities.

Implementing and monitoring

Use automated workflows to test multiple forecasting models, selecting the most effective one, and check if it’s still grounded in reality by monitoring sales and new external variables. Even better if the solution can be integrated with other supply chain tools already in-use.

Demand planning software: what Rulex Platform can do

Rulex Platform is an all-in-one decision intelligence software that allows companies to seamlessly manage every aspect of demand forecasting – orchestrating data, improving and supporting decisions, and automating processes. All with the flexibility to easily integrate into the existing infrastructure, thanks to capabilities like APIs. Designed with a drag-and-drop interface, Rulex Platform allows non-technical users to mix and match the advanced technologies of its toolkit to create tailored solutions. As a plus, it also includes an integrated dashboarding and custom UI component, providing a smart way to visually interact with underlying data and solutions.
From mitigation sources planning, to replenishment optimization and simultaneous production and work shift planning, Rulex Platform is trusted by companies and supply chains worldwide in their digital transformation journey (you can explore some success stories in our Supply Chain case studies e-book). But first, let’s examine a practical example of demand planning in action.

A real-life demand planning example

Recently, a global pharmaceutical company turned to Rulex Platform to improve demand forecasting for several flagship products based on both historical sales and external variables such as temporal trends, the diffusion of seasonal viruses, and geographic information. Using AutoML, Rulex’s decision intelligence software automatically selected the best forecasting model, allowing for parallel testing of multiple strategies. The company’s domain experts could check what was going on and make adjustments at each point of the process, thanks to the inherent transparency of Rulex’s technology.
The solution delivered demand forecasts over a three-month forecasting horizon, updated dynamically to incorporate new data and ensure that the latest trends were always factored in. The results spoke for themselves: accurate forecasts (average error below 10%), smooth integration into inventory and logistics systems, and a process that helped the company make smarter decisions.

But what about demand forecasting for newly launched products with no sales history? In another real-world case, a furniture company used Rulex Platform’s advanced analytics and clustering tools to tackle this challenge. The software analyzed key attributes of new items, such as category, brand, and size, to find similarities with existing products that had sales data. By comparing these attributes, the company was able to generate an initial demand forecast, even in the absence of historical sales records.

Once you’ve perfected demand forecasting, the next step is inventory optimization – if that’s something you’re exploring, we’ve got an article on it too.

The future of demand planning

Companies investing in advanced forecasting solutions, such as AI-driven machine learning and predictive analytics, will stay ahead of market shifts, contributing to a global economic impact of $9.2 trillion by 2029 [3].

Is your demand planning strategy ready for what’s next? Get in touch today to find the right solution for your business.

Sources

[1] Harnessing the power of AI in distribution operations
[2] Autonomous supply chain planning for consumer goods companies
[3] Retail Inventory Distortion Report

What is Advanced Analytics?

Recent advancements in computer science have led to a new generation of analytics techniques that allow stakeholders to automatically uncover deeper patterns and make more accurate predictions. These techniques include machine learning, artificial intelligence (AI), natural language processing, and statistical modeling. Advanced analytics provides the means not only to understand what has happened but also to forecast future trends, optimize processes, and simulate various business scenarios.

For example, while traditional analytics might show that sales dropped in the last quarter, advanced analytics can use machine learning algorithms to predict sales for the upcoming quarter, identifying the key factors that might influence future outcomes. This capability is vital in industries such as finance, healthcare, and supply chain management, where predicting future trends (and the reasons behind them) can significantly impact decision-making.

Different analytics for different questions

Regardless of the techniques used to analyze data, decision-making requires answering a series of increasingly complex questions:

• What happened?
• Why did it happen?
• What is going to happen?
• What should I do?

Each question corresponds to a deeper level of insight into the data, allowing for progressively more informed decision-making. Accordingly, different levels of analytics can be identified, each aligned with these questions.

• Descriptive analytics is the simplest form of analytics, aimed at answering the question: “What happened?”. Its goal consists in summarizing and describing historical data using simple techniques like data aggregation, basic statistics and reporting. For example, reviewing last quarter’s sales figures is a typical descriptive analytics task.
• Starting from descriptive analytics insights, diagnostic analytics addresses the question: “Why did it happen?”. Its objective is to understand the underlying causes of events and trends identified by descriptive analytics, using techniques such as drill-down and correlation analysis. A typical diagnostic task involves identifying events that are correlated with malfunctions in a plant.
• The objective of predictive analytics is to forecast events that are likely to happen in the future based on historical data. It aims to answer the question “What is going to happen?” using techniques such as regression analysis, machine learning, and time series forecasting. For example, companies might use predictive analytics to forecast future sales based on past trends and other information.
• Prescriptive analytics is the most advanced type of analytics, focusing on recommending actions to optimize business performance. It answers the question: “What should we do?”. Prescriptive analytics often combines predictive models with optimization algorithms to propose the best course of action. For instance, a logistics company might use prescriptive analytics to optimize its delivery routes based on traffic patterns, demand prediction, and delivery schedules.

Visual platforms for analytics and programming languages

Several tools can support analytics at different levels. Visual platforms, for instance, help make data insights accessible to a broader audience through customized interactive dashboards, charts, and reports. The ability to visualize data helps users, even those without a technical background, to comprehend complex data sets and make informed decisions. The goal of these tools is to hide complexity from the end user, showing only the relevant information.

On the other hand, programming languages like Python and R offer numerous libraries that enable the implementation of virtually every state-of-the-art analytics technique. Their goal is to provide a wide range of methods and maximum flexibility to combine them according to the user’s needs. In-depth technical skills are however required to master the complexity of programming languages.

Performing analytics on Rulex Platform

Rulex Platform was designed to overcome the limitations of both visual software and programming languages, offering the ease of the former and the flexibility of the latter. Rulex is a no-code platform that integrates several analytics tools to address the needs of different types of users.

Decision makers can use Rulex Studio to get a bird’s-eye view of their business data. This platform component allows users to create interactive dashboards and custom UIs where they can consult graphs and tables, applying the necessary filters and aggregations. They can also interact with the underlying data, launching computations, providing feedback on results, and even updating and synchronising information in seconds.

Data scientists can make the most of a wide range of data management and artificial intelligence tools offered by Rulex Factory. This platform component, where flows are built in a drag-and-drop environment, can replace the tools commonly used to manipulate data and extract models, such as Pandas and TensorFlow, improving development speed and solution usability.

It provides a comprehensive portfolio of machine learning methods, ranging from unsupervised learning (clustering, anomaly detection) to supervised learning (classification, regression), and even includes proprietary algorithms, such as the eXplainable AI Logic Learning Machine. Coding enthusiasts can also integrate their Python or R code into Rulex flows, reducing the need to switch data back and forth.

Data engineers and data integration specialists use Rulex Factory to seamlessly merge data from different sources, bringing them together in a single view. The platform supports the integration of almost any data source, from common relational databases to everyday file types such as Excel and PDF. Additionally, users can connect to data stored in remote locations, including cloud storage or HTTP servers.

How Rulex can answer your questions

Regardless of the team you’re in or the data job you have, Rulex Platform can help answer all your analytics questions.

By using data management tools and building interactive dashboards, you can better understand what happened (and what is happening) to your business. Performing statistical correlations and extracting explainable machine learning models from your historical data can help you identify the root causes of your business trends, understanding why they happened. Inferring from machine learning models trained on past data allows you to predict what is more likely to happen in the future. Last but not least, several tools can support you in understanding the best action you can take.

Optimization plays a pivotal role in providing a targeted action plan, as it finds the configuration that maximizes desired results (expressed in an objective function) while fulfilling all the operational constraints. Nonetheless, optimization has two well-known drawbacks: (i) its implementation is quite complex, especially in presence of several constraints, and (ii) the computational effort to find the optimal solution can grow exponentially.

Rulex Platform has solutions for both these issues with its mathematical optimization task, Build & Solve. The task dramatically simplifies the definition of linear programming problems, speeding up the setup phase (i), and implements specific algorithms able to find a feasible solution in minutes, or retrieve sub-optimal alternatives when the optimal solution cannot be retrieved within a reasonable time-frame (ii).

Hands-on analytics: the supply chain department of a CPG manufacturer

Let’s get practical and explore how Rulex Platform can assist the major players in a manufacturing company with their everyday tasks.

In the supply chain department of a large CPG (Consumer Packaged Goods) manufacturer, managers face the challenging task of ensuring that goods are sent to several distribution centers while fulfilling all requests and reducing delivery costs. Their daily responsibilities involve managing multiple datasets stored in different locations, including production data, orders, carrier information, and delivery costs. To gain a comprehensive overview of their business performance, they can use Rulex Platform to integrate all this data and create a dashboard containing relevant insights. Experts can rely on this dashboard to access historical data about orders, production, and transportation, updating it with fresh information as needed.

The company’s data scientists, in turn, can employ Rulex’s statistical functions and machine learning techniques to analyze past behaviors and predict future outcomes. For instance, they can investigate why a shortage occurred by using the Logic Learning Machine, which generates if-then rules correlating the outcome (e.g., the shortage) with its potential causes. They can also forecast the demand for various items from logistics centers in the coming days, weeks, or months using time series models like ARIMA in the Auto Regressive task.

Finally, and most crucially, Rulex Platform can recommend specific actions to reach target goals. After using Rulex to process data and forecast missing variables (such as demand), planners can use production and demand insights to determine what, when, and where to ship.

Rulex’s prescriptive tools, such as optimization tasks, guide the distribution of goods. Rulex Platform’s Network Optimizer and Transport Optimizer tasks are specifically designed to address this challenge. Their outcome is a list of goods together with the quantity to be sent in each location and the optimal configuration to perform the deliveries reducing costs and environmental impact.