Now showing 1 - 2 of 2
  • Publication
    Improvement of value stream mapping by integrating a Monte Carlo simulation: a conceptual model
    (Polish Academy of Sciences, 2023)
    Alaa Salahuddin Araibi
    ;
    ;
    Muhanad Hatem Shadhar
    Value stream mapping (VSM) is a well-known lean analytical tool in identifying wastes, value, value stream, and flow of materials and information. However, process variability is a waste that traditional VSM cannot define or measure since it is considered as a static tool. For that, a new model named Variable Value Stream Mapping (V-VSM) was developed in this study to integrate VSM with risk management (RM) using Monte Carlo simulation. This model is capable of generating performance statistics to define, analyze, and show the impact of variability within VSM. The platform of this integration is under Deming’s Plan-Do-Check-Act (PDCA) cycle to systematically implement and conduct V-VSM model. The model has been developed and designed through literature investigation and reports that lead in defining the main four concepts named as; Continuous Improvement, Data Variability, Decision-Making, and Data Estimation. These concepts can be considered as connecting points between VSM, RM and PDCA.
  • Publication
    Advanced value stream mapping: development of a conceptual model considering variability in production processes
    (SAE International, 2023)
    Alaa Salahuddin Araibi
    ;
    ;
    Muhanad Hatem Shadhar
    Recently, lean manufacturing (LM) practices are being combined with tools and techniques that belong to other areas of knowledge such as risk management (RM). Value stream mapping (VSM) is a well-known tool in showing the value, the value stream, and the flow, which represents the three lean principles. VSM and RM, when used in tandem with one another, are more advantageous in covering VSM issues such as the variability of production processes. In this article, a conceptual model that integrates the two is shown and explained. The model helps to generate scenarios of current state map (CSM) and future state map (FSM) in a dynamic way by identifying current and potential risks. These risks might happen in the future, bringing with it negative ramifications including not reaching the main objectives within the defined time. The model has been tested in a coffee production company belonging to health and food sector. The proposed model specified the ranges of variability through the drawing of CSM and FSM. This is quite a milestone because one of the challenges of VSM is that it is a static tool, and, as such, process variability cannot be captured appropriately. This new model is expected to overcome this drawback.
      12  1