Levels of Autonomy in Production Logistics: Terminology and Framework

The increasing demand of flexibility in production systems influences the organisation of production logistics and enhances the role of autonomous resources for logistic tasks. In the current state of the art, there exists neither a common definition of the term “autonomy” in the production logistics context nor a generalised approach regarding the classification of autonomous resources depending on their characteristics as well as their skills. Due to this lack, difficulties appear when intending to integrate autonomous resources - that are implemented for logistic tasks - in the superior production control processes which aim to meet the key performance indicators of the production system. This paper analyses in a first step the current use of terminology regarding autonomy and related terms like automation and self-x approaches in production logistics. Based on these results, a definition of “autonomy” for production logistics and a universal framework for classifying autonomous resources regarding their level of autonomy can be proposed. This allows to specify afterwards the appropriate level of autonomy in production logistics for a specific production system

Towards robustness of production planning and control against supply chain disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control

Towards Robustness Of Production Planning And Control Against Supply Chain Disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control

Levels of autonomy in production logistics: terminology and framework

The increasing demand of flexibility in production systems influences the organisation of production logistics and enhances the role of autonomous resources for logistic tasks. In the current state of the art, there exists neither a common definition of the term “autonomy” in the production logistics context nor a generalised approach regarding the classification of autonomous resources depending on their characteristics as well as their skills. Due to this lack, difficulties appear when intending to integrate autonomous resources - that are implemented for logistic tasks - in the superior production control processes which aim to meet the key performance indicators of the production system. This paper analyses in a first step the current use of terminology regarding autonomy and related terms like automation and self-x approaches in production logistics. Based on these results, a definition of “autonomy” for production logistics and a universal framework for classifying autonomous resources regarding their level of autonomy can be proposed. This allows to specify afterwards the appropriate level of autonomy in production logistics for a specific production system