While optimizing production processes has always been a challenge for companies, particularly in the manufacturing sector, today it has become an indispensable practice. In fact, avoiding waste, limiting downtime and generally making the entire supply chain more efficient allows costs to be contained and productivity to be improved. A supply chain planning process is undoubtedly the best ally for making production more uniform and continuous, ensuring a consistently adequate flow of raw materials inbound and the timely and systematic delivery of products outbound.

Supply chain planning: definition

When we talk about Supply Chain Planning we mean a planning process that involves coordinating resources to optimize the delivery of goods, services and information from supplier to customer by balancing supply and demand. Among the aspects that a supply chain planning system must consider are the most important ones:

  • Willingness and capacity for commitment;
  • Integrated business planning of sales and production;
  • Coordinated and collaborative planning, including sales forecasting and replenishment;
  • Demand planning;
  • Inventory planning and management;
  • Production scheduling and management

As we can see, Supply Chain Planning introduces layers of complexity and can greatly benefit from the use of the most advanced information tools. An integrated SCP management solution can, through a transactional system and access to business data, help provide planning, hypothetical scenario analysis capabilities, and management of demand commitments in real time, considering constraints, raw material availability, and contingency.

With an advanced system involving the use of data and advanced mathematical models, the scope of supply chain management can be expanded to include additional areas and improve its effectiveness.

Digital supply chain planning: data analysis and mathematical models

The idea of optimizing the supply chain is not new per se-it has been a theme in the industry since at least the 1990s. Some years later,  some experts argued that the competition had shifted from “company vs. company to supply chain vs. supply chain.” For this reason, many analysts over the years have worked to create increasingly advanced and effective mathematical models for managing and planning the supply chain.

The topic overlaps strongly with that of digitization and especially with that of data collection and analysis capabilities advocated by Industry 4.0 best practices. For example, the use of predictive models ondemand trends can, within a smart factory, contribute to supply chain management. This is especially valid in today’s environment, where the ability to adapt to the market almost in real time is strategic.

By implementing data collection throughout the supply chain and including all departments in the process, it is possible to extend the benefits of supply chain planning to other areas. For example:

  • Event planning such as product promotion or lifecycle management, which may affect orders or spare parts management;
  • Production planning and scheduling, including and especially in the case of multi-plant supply chains and the presence of multiple production plants;
  • Distribution planning, strategic network management, inventory and supply planning and optimization.

Demand planning is the starting point

The use of data and the adoption of advanced mathematical models, with the support of an appropriate information system, can enable the evolution of supply chain management from a traditional model based on demand planning to an evolved and innovative model , in which analytical tools are a support and enhancement of efficiency and competitiveness practices that have already proven their effectiveness in the field.

Once again, digital transformation leads the company to the acquisition of new assets that improve its competitiveness.