Downstream demand inference (DDI) emerged in the supply chain theory, allowing an upstream actor to infer the demand occurring at his formal downstream actor without need of information sharing. Literature showed that simultaneously minimizing the average inventory level and the bullwhip effect isn't possible. In this paper, the authors show that demand inference is not only possible between direct supply chain links, but also at any downstream level. The authors propose a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm. Simulation results show that bullwhip effect can be reduced highly if specific configurations are selected from the Pareto frontier. Numerical results show that demand's time-series structure, lead-times, holding and shortage costs, don't affect the behaviour of the bullwhip effect indicator. Moreover, the sensitivity analysis show that the optimization approach is robust when faced to varied initializations. Finally, the authors conclude the paper with managerial implications in multi-level supply chains.

Digitalization consists of the modification of processes using digital tools. Digitalization is a powerful way for regaining strong short-term to long-term competitiveness. The digital supply chain was born from the fourth industrial revolution. The evolution of the supply chain now involves the automation of processes and the expansion of information. Among the advantages of digitization, the authors note an accurate forecasting and planning, effective communication of internal function and external actors of the supply chains, and assuring the security. Fast digital transformation is reforming supply chains and changing the present models inside companies. In this case, purchasing as one of the important functions of supply chain is in the way of the transformation, by adapting new digital solutions such as blockchain, data analytics, robotics, internet of goods, and smart contracts. This chapter aims to explore the possibilities of these technologies in intelligent purchasing and the main obstacles against their adaption.