Highlights

Efficient coordination between Supply Planning and Demand Planning is essential for optimizing operations, mitigating risks, and maximizing customer satisfaction in the S&OP process. By integrating these two functions within the framework of Integrated Business Planning (IBP), organizations can achieve a holistic view of their operations, balance supply and demand effectively, and drive sustainable growth. This good practice has a significant impact on service levels, lost sales, optimal stock levels, equilibrium in S&OP KPIs, and collaboration in the Demand Planning process, ultimately enabling organizations to achieve their strategic objectives and gain a competitive edge in the marketplace.

AI and Machine Learning are transforming FP&A by providing advanced insights, improving efficiency, and driving better business outcomes. By harnessing the power of integrated platforms like Pyplan, organizations can leverage AI-driven analytics to detect anomalies, automate reporting, facilitate real-time close processes, streamline collections, and drive more accurate revenue recognition. This evolution in FP&A enables organizations to achieve greater agility, accuracy, and competitiveness in today’s dynamic business environment.

In this blog we summarize some of the best practices for demand planning in pharmaceuticals, highlighting the importance of data-driven forecasting, collaboration with stakeholders, inventory optimization, and flexibility in response to market dynamics.

These are some of the Technological Advancement in Demand Planning. Since the realm of demand planning is evolving rapidly, driven by technological advancements and a shifting global landscape. Let’s delve into the exciting trends that will shape demand planning strategies in the coming year.

Pyplan partners with ENEL two years ago when the group began to unify margin management criteria between distributors to support decision-making and generate risk and opportunity analyses.

The initial goal was to implement a solution that met the needs of each distributor in a single system.

To develop the tool they required Pyplan integrated different processes in order to achieve a specific goal: creating a collaborative system where employees can create margin simulations and centralize information from the distributors to speed up the flow of information.

A collaborative solution was developed to efficiently support various stages of the process. The selected tool facilitated the uploading and consolidation of information from customer orders to Puma, as well as the creation, negotiation and uploading of purchase orders from Puma Mexico to the corporation.

In addition, solid support was implemented for tracking and updating logistics, including dates of arrival of purchase orders at the corporate office. This integrated approach aimed not only to streamline operational processes, but also to improve coordination and efficiency in Puma Mexico’s supply chain.

Pirelli was using a combination of different tools and spreadsheets that made the financial planning process complicated, with many manual tasks and projection models that could not be extended to match the complexity of the business.

The company was looking for a tool to optimize the Financial Planning and Analysis process. Their need was to automate import and update data processes, improve the projection models and facilitate the display of reports at different hierarchical levels, including the head office in Milan (Italy).

Until now, no tool had the flexibility and speed that Nestlé required to plan the Demand and Production processes in an integral way and within the same environment.

Production planning is essential for optimizing the overall process, considering the products, resources and processes involved, as well as the expected demand for each product line.

Nestlé sought to find a tool that automates and optimizes the Demand and Production Planning processes, in a way that would allow users to have full visibility of both the hierarchy of its large product portfolio (by segment, family, brand, SKU, among others) and the logistics point, factories and production lines.

The application of advanced analytics and artificial intelligence in demand planning offers significant opportunities for organizations to improve forecast accuracy, analyze large volumes of data, and incorporate diverse data sources to drive informed decision-making. By leveraging these technologies, businesses can enhance operational efficiency, optimize inventory management, and meet customer demand effectively in today’s dynamic and competitive marketplace.