More efficient supply chains through digital twins

Digitization and automation of supply chains is inevitable in many industries. Digital twins could help make global supply chains more efficient, environmentally friendly and value-oriented.

The COVID 19 pandemic has shown that digitization and automation of supply chains is urgently needed in many industries in order to be able to operate reliably. Especially in lagging sectors like logistics, new technologies are needed for supply chain management.

Increase visibility, react in time

The first step to increasing flexibility and reducing risk is to move away from outdated and often highly manual procedures and processes. Modern track-and-trace solutions give brands and carriers an accurate overview and enable them to respond to disruptions in a timely manner. Through digital platforms, companies can access the “completion date” of orders, allowing them to forecast inventory levels or find alternative transportation methods when needed.

Another technology gaining popularity in the fight against the lack of transparency and high error rate in logistics is digital twins.

Digital twins

A digital twin is a virtual representation of an object or system. This representation spans its entire lifecycle, is updated from real-time data, and employs simulation, machine learning, and reasoning to support decision-making. Two of the best-known examples of its use are weather forecasting and air traffic control.

When digital twins are applied to supply chains, accurate insights and deep knowledge can be gained about all aspects of the global movement of goods. The more accurate the digital twin, the more it can help manage the cost, inventory, and environmental impact of supply chains and best respond to emerging issues.

Using AI in logistics

Artificial intelligence (AI) and machine learning (ML) can make digital twins even more valuable through their predictive capabilities. Through such decision support but also through automation, AI can revolutionize the logistics industry.

However, expertise is required, especially in ML, as such capabilities are difficult to integrate and apply. In addition to skilled employees or partners who can deploy ML, it should also be noted that one of the key elements to the success of ML models is the quality of the data sets used to train the models. If ML is used properly, it can lead to unprecedented optimization of inventory, carbon footprint and cost reduction.

Changing job profiles

As the preceding remarks show, in the future some of the more mundane tasks and decisions could also be transferred to machines in the logistics sector, and data and scenarios could be made available for human consideration. This would allow logistics professionals to more actively use their experience, knowledge and cognitive skills to drive strategies and add value to their business.

Creating acceptance and trust

Acceptance is a critical success factor for adding value to new technologies. Its success usually depends on the cognitive load the product places on its users and the method by which it is introduced into the organization. For example, the introduction of change management processes, especially for large teams and companies, can make all the difference.

In the specific case, it is also important to consider the complex landscape of supply chains and the fact that many different partners must come together to move goods around the world. These companies and individuals typically have different needs, technical expertise, languages and cultures. Therefore, a trust-based model for working with them should be based on awareness, understanding and adaptation to the diversity of the ecosystem.

Source: VentureBeat