
Why is it important to start implementing S&OP with Demand Planning?
9 April 2025
Webinar transcript ‘Is your company ready for the AI revolution in supply chain?’
30 April 2025Demand Planning, or demand planning, is a key element in the production and inventory management process in Make To Stock (MTS) manufacturing companies. In this production model, goods are manufactured based on anticipated demand and then stocked in anticipation of sales. While this strategy allows for a rapid response to customer needs, it brings with it a number of challenges that can hinder effective process management. Over coffee today, we outline the key issues faced by companies implementing Demand Planning in the MTS production model.
1. Accuracy of demand forecasts
One of the biggest challenges in MTS is the need to accurately predict future demand. Forecasts based on historical data can be inadequate, especially in the case of dynamically changing market trends or seasonality of sales. Inaccurate forecasts lead to two main problems:
- Surplus stock, which generates storage costs and the risk of overdue products.
- Stock shortages that result in loss of potential revenue and customer dissatisfaction.
2. Lack of cooperation between departments
Effective demand planning requires close cooperation between different departments of the company, such as sales, marketing, logistics, production and finance. A lack of coordination can lead to discrepancies in the objectives and priorities of the different teams:
- Information flow problems: inadequate communication between departments results in an incomplete picture of the market situation.
- Divergence in strategies: individual departments may make decisions that are inconsistent with the assumptions of the ‘agreed’ plan, making it difficult to achieve business objectives.
3. Data management
Demand planning is based on the analysis of large amounts of data on sales, market trends and customer preferences. Data management challenges include:
- Complexity of analysis: companies often find it difficult to select appropriate predictive models and integrate data from different sources.
- Poor data quality: Incomplete or outdated data can lead to wrong decisions.
4. Seasonality and market variation
MTS companies often operate in sectors where there are cyclical ups and downs in demand. These changes can be difficult to predict:
- Risk of overproduction: during periods of low demand, overproduction of products increases operating costs.
- Risk of shortages: sudden increases in demand can lead to delays in order fulfilment.
5. Technological challenges
Traditional ERP (Enterprise Resource Planning) systems often do not offer sufficiently sophisticated forecasting model algorithms. Companies need to invest in dedicated technology solutions to support Demand Planning, e.g. based on artificial intelligence. This gives a more accurate forecast, but involves the need for systems integration.
Summary
Implementing Demand Planning in Make To Stock manufacturing companies is a process that requires both analytical precision and effective collaboration between departments. Key challenges include forecast accuracy, data management and aligning technology with company needs. Investing in advanced demand forecasting systems and developing an organisational culture based on collaboration and transparency can be a solution to these challenges. This enables companies to optimise production processes, minimising costs and maximising customer satisfaction.
DATURE ENTERPRISE software uses artificial intelligence and machine learning in the process of demand forecasting and inventory optimization. The Dature system allows information about the supply chain to be collected and analysed and used to calculate optimal stock control parameters dal various scenarios of supply chain constraints and costs. The system provides indicators that measure the effectiveness of stock management from various points of view, enabling a proper assessment of the current and future situation of the company.
The system provides methods for forecasting seasonal demand and demand influenced by calendar days. Inventory management methods allow for both pre-season inventory building approaches, dynamic safety stock control and JIT.
TheDATURE application ENTERPRISE can also use expertise in the demand forecasting process. Authorized users can enter expert forecasts and adjust statistical forecasts with them. The process is fully auditable in terms of who changed the forecast when and how. This makes it possible to track the accuracy of both statistical and expert forecasts. As a result, the organization learns how to forecast more accurately and improve process efficiency.