The ongoing globalization of the retail and industry sectors is providing expanded sales markets and cost advantages within manufacturing. At the same time, however, it is leading to a steadily growing dependence on networked and dynamic supply chains. For example, during the production of vehicles and machinery it can be necessary to use components from different, sometimes far-flung, economic areas. It’s not unusual for the retail sector to have to use long and complex delivery routes for goods such as toys, clothing and food because of constraints such as “just-in-time” production, packaging standards, shelf life limits or fixed sales deadlines and this requires smooth interaction with suppliers and service providers.
What are the supply chain risks?
It’s the task of any company’s internal Supply Chain Management (SCM) to guarantee this. The planning and control of work organization, financing, transport and warehousing converge along the entire value and supply chain. Even under normal circumstances, SCM has to adapt to rapidly changing external influences such as bad weather or labor disputes. Truly disruptive events such as the Corona pandemic or the closure of the Suez Canal represent a worldwide and simultaneous stress test for the supply chains of virtually every global company. In such a crisis situation it’s crucial to know the interrelationships between the individual components of the supply chain to quickly gain an overall view of what’s happening and to be able to identify any possible alternatives. A crucial solution for this is the utilization of DI, which is a component of the innovative tools from paretos.
Anticipate risks and manage crises with DI
This Artificial Intelligence (AI)-powered, data-centric method of decision-making is designed from the ground up to examine business processes holistically. It generates predictions and produces suggested actions by using the company’s internal metrics and other data from external influences. The results can be evaluated by human decision-makers and then directly translated into actions or delegated to DI for autonomous execution.
The system is scalable and achieves meaningful results even from small data sets. It’s totally adaptive by incorporating both historical precedents and the current process of decision-making and it automatically increases its dataset, which results in higher quality predictions.
How does DI help a specific case?
For the optimization of SCM, DI’s decisive advantages over existing methods plus improved inventory and demand planning are second to none. The DI forecasting models detect critical trends, such as shortage of transportation containers, impending raw material bottlenecks or peaks in demand, at an early stage and can suggest any necessary countermeasures or even initiate them themselves. In the event that a crisis situation has already occurred, this data-driven approach will increase your company’s reaction speed.
When predicting risks or an event of acute crisis, a DI approach can propose a wide variety of optimized solutions that follow different predefined priorities. For example, critical components can be transported on alternative routes at higher costs or important customers can be supplied preferentially by air freight. In this way, utilizing DI in the management of your supply chain creates a flexibility that strengthens the resilience of your company during any threat of current or future disruptions.