Bullwhip effect attenuation in supply chain management via control-theoretic tools and short-term forecasts: A preliminary study with an application to perishable inventories

Abstract

Supply chain management and inventory control provide most exciting examples of control systems with delays.Here, Smith predictors, model-free control and new time series forecasting techniques are mixed in order to derive an efficient control synthesis. Perishable inventories are also taken into account. The most intriguing "bullwhip effect" is explained and attenuated, at least in some important situations. Numerous convincing computer simulations are presented and discussed.

An alternative to proportional‐integral and proportional‐integral‐derivative regulators: Intelligent proportional‐derivative regulators

“…The regulation via PIDs of systems with delays is far from being fully understood (see, e.g., References 58,59). It would be rewarding to extend, if possible, the time series setting in 60 for supply chain management. The approach to machine learning sketched in Reference 9 should benefit from the unicity of the feedback loop advocated here. See also Reference 56 …”

“…• The regulation via PIDs of systems with delays is far from being fully understood (see, e.g., References 58,59). It would be rewarding to extend, if possible, the time series setting in 60 for supply chain management.…”

An alternative to proportional-integral and proportional-integral-derivative regulators: Intelligent proportional-derivative regulators

“…2 • The regulation via PIDs of systems with delays is far from being fully understood (see, e.g., [37], [50]). It would be rewarding to extend, if possible, the time series setting in [24] for supply chain management. • The approach to machine learning sketched in [17] should benefit from the unicity of the feedback loop advocated here.…”

Model-free based control of a HIV/AIDS prevention model

“…This control law can be considered as an alternative to PI and PID controllers [11] and the performances are really satisfactory taking into account that the control is calculated based only on the information provided by the controlled input and the measured output signal of the controlled systems. This control law has been extensively and successfully applied to control many nonlinear processes: see, e.g., [3,10,12] and the references therein. In particular, some applications have been dedicated to the control of chemistry and biological processes [2,3,13,17,26], including the development of an artificial pancreas [16].…”