In today’s rapidly changing world, supply chain management, demand forecasting and production process optimisation have become more complex than ever before. Companies that want to remain competitive have to deal with increasing volatility, uncertainty and complexity in their business processes. One of the latest tools that can help solve these challenges is the concept of Digital Twins. Over coffee today, we will discuss how Digital Twins can be used in the Sales and Operations Planning (S&OP) process to improve decision-making, increase operational efficiency and improve forecasting.

The role of Digital Twins in the S&OP process

The Sales and Operations Planning (S&OP) process is a key element of supply chain management that combines sales forecasting with production and inventory planning. The aim of S&OP is to achieve a balanced plan that allows the company to optimise its production processes, minimise costs and ensure adequate inventory that meets actual demand.

Integrating Digital Twins into the S&OP process allows for significant improvements to the process, enabling better forecasting, monitoring and real-time adjustments to operational plans. Here are some key areas where Digital Twins can support S&OP:

1. Improving the accuracy of demand forecasts

One of the most difficult aspects of the S&OP process is accurately predicting future demand. Traditionally, companies rely on historical data and statistical models, but these methods do not always cope with sudden changes in the market or unexpected events. Digital Twins allow for more precise forecasts because the virtual model is fed with real-time data from the market, production and supply chain. This allows demand forecasts to be adjusted more flexibly, taking into account changing factors such as changing consumer preferences, seasonal fluctuations or supply disruptions.

2. Production and stock optimisation

Digital Twins enable the creation of virtual copies of production facilities and production processes, allowing them to be monitored and optimised in real time. This enables companies to better understand how various variables affect production – such as raw material availability, machine throughput, lead times or changes in demand. Virtual prediction of production and inventory patterns allows for early detection of potential problems and rapid adjustment of production plans as part of the S&OP process.

3. Risk management

Supply chain risk management challenges, such as demand unpredictability, supply disruptions or regulatory changes, can seriously affect S&OP processes. Digital Twins allow different scenarios to be simulated, enabling better preparation for potential disruptions. By using data from multiple sources and realistic simulations, companies can create alternative plans that take into account different crisis situations, such as supply delays or equipment failures. This allows them to react faster to changes and minimise risks.

4. Real-time collaboration and decision-making

S&OP is a process that requires the collaboration of many departments within a company, from sales to production and finance. Digital Twins facilitate this collaboration by giving every team access to the same, real-time updated data. Rather than acting on outdated information, managers can make decisions based on accurate forecasts and models, allowing different departments to better align their activities with common goals.

5. Improving supply chain management

Digital Twins also help to monitor the entire supply chain, enabling more accurate forecasting of stock, orders and delivery times. With data collected in real time, bottlenecks and potential problems in supply can be detected early, allowing faster response and preventive action.

Summary

Digital Twins is a powerful tool that can revolutionise the S&OP process, introducing new capabilities for forecasting, production optimisation, risk management and decision-making. With accurate, real-time updated data, companies can operate more flexibly, respond quickly to changes and better integrate different departments within the organisation. With the increasing complexity and volatility of the market, the use of Digital Twins is becoming a key component of supply chain management strategies and operational process optimisation.

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.