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![Manufacturing supply chain and planning processes [8].](publication/283960260/figure/fig1/AS:670714264838164@1536922150760/Manufacturing-supply-chain-and-planning-processes-8.png)
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![Fig. 1. Manufacturing supply chain and planning processes [8].](publication/283960260/figure/fig1/AS:670714264838164@1536922150760/Manufacturing-supply-chain-and-planning-processes-8_Q320.jpg)
![Fig. 2. Effects of deviations on the inventory management [6].](publication/283960260/figure/fig2/AS:670714264834075@1536922150786/Effects-of-deviations-on-the-inventory-management-6_Q320.jpg)
The steady increasing of supply chain complexity due to a rising global cross-linking of production and sales regions leads to an increasing sensitivity to disturbances while in the meantime the requirements of the availability, the time of delivery and the security of supplies within the supply chain increases. To meet this challenges the security...
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... its preliminary setup, the System Dynamics model is based on the work of Sterman for a four level manufacturing supply chain with detailed processes for two manufacturing participants (focused companies) within the supply chain as well as a demand source downstream the supply chain and a supply source at the upstream end of the supply chain (see figure 1). In contrast to Sterman's model, the continuous model for raw material stock and finished good stock replenishment processes for both focused manufacturing companies has been modified by using discrete event points for replenishment processes. ...
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Addressing the root causes of schedule instability, particularly the unreliability of suppliers’ production processes in a supply network, can help to curtail short-term demand variations and increase the overall supply chain efficiency. Hence, we introduce a stylized automotive supply chain with two suppliers and a single original equipment manufa...
Citations
... The decisions are made from logical rules of each supply chain entity. There are notable studies on simulation of supply chain network under disruptions, [16][17][18][19][20][21][22] these studies have given insights into best ways to manage disruptions and the potential benefits of such actions. Conversely, mathematical programming follows an analytical approach to make decisions using various optimization tools. ...
In this work, we proposed a two‐stage stochastic programming model for a four‐echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two‐stage stochastic model with a multi‐period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.
... The decisions are made from logical rules of each supply chain entity. There are notable studies on simulation of supply chain network under disruptions [16][17][18][19][20][21][22] , these studies have given insights into best ways to manage disruptions and the potential benefits of such actions. Conversely, mathematical programming follows an analytical approach to make decisions using various optimization tools. ...
In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.
... From the Figure 3, we can find that the supply chain in manufacturing can be defined as the network of firm's related to the activities from raw material to finished product and delivery to customers. Source: (Schuh, Schenk, & Servos, 2015) From the Figure 4, supply chain management can be defined as the management of the interface relationships among key stakeholders and enterprise functions that occur in the maximization of value creation which is driven by customer needs satisfaction and facilitated by efficient logistics management (Stock & Boyer, 2009). ...
The use of supply chain management in industry has been quite successful, particularly in the industrial sector. In today ’s marketplace, Jordanian construction firms must compete not only with local but also with international companies; therefore, the use of supply chain management is critical to improving efficiency and increasing competitive advantage. A survey was conducted in this study to investigate the main obstacles for adopting supply chain management to the Jordanian construction sector. The survey questionnaire was created by summarizing and incorporating prior findings, as well as consulting with specialists. Participants in the poll were those who have worked with main contractors and participated in construction projects. The findings revealed several major factors that obstruct the use of supply chain management in the construction industry.
... The authors considered disruptions caused by customers, suppliers and internal processes by implementing customer demand, replenishment lead time, production lead time and production output as variable parameters. The effects of the disruptions were determined with a cost and performance-based system of indicators [29]. Schmitt & Singh (2012) developed a discrete event-oriented simulation model of a production network to investigate how downtimes and temporary demand peaks influence the order fulfillment rate. ...
Companies nowadays act in global production networks. These networks offer advantages such as production close to market as well as the exploitation of low factor costs. However, due to their interlinkage and complex structure, the resulting networks are characterized by a high susceptibility to disruptions. This paper presents an approach, which quantitatively examines the effects of disrupting events in production networks. The main focus of the study is on the question of how product quality is influenced by quality-related disruptions. To describe the analyzed relationships best, a Kriging based metamodel is adapted to the behavior of the developed multimethod simulation model. The results of the investigation are formalized causal relationships between the error probabilities in production networks, the inspection frequencies of the production systems involved, the respective generic network configurations and the observed PPM quality score.
... One of the main flexibilities that this simulation process allows for is the joint consideration of two types of inventories, namely the inventory of standard chocolates and the inventory for semi-finished goods. Schuh et al (2015) develop a System Dynamics simulation model in order to address the actual complexities and dynamics in four-stage manufacturing supply chains. Gan and Cheng (2015) develop a centralized agent-based optimization model which focuses on task distribution and cooperation between business entities in the backfill supply chain. ...
We develop a simulation-based optimization methodology for the joint design and planning of globalized supply chains (SCs) under minimization objectives for cost and CO2 emissions. The assumptions required for the analytical optimization of such SCs include deterministic lead times, no-backorder occurrences at central distribution facilities, and powers-of-two replenishments of inventories at the SC nodes. The paper aims to investigate whether and how will the decisions that stem from the analytical optimization process change when these assumptions are relaxed. The proposed methodology is employed in a realistic SC structure. The results reveal that (i) the optimal order cycles of the nodes of the SC are not necessarily powers of two, (ii) the central distribution facilities have the option to operate at lower service levels, (iii) the network’s cost and CO2 emissions under optimality are reduced by approximately 0.9%, and (iv) the strategic network design decisions align with those of the analytical solution.
At present, sustainability science is mainly based on conscious information and strongly focused on analytical tools or strategies. Neuroscience has made obvious that human decisions are prepared by the unconsciousness. Intuition plays an important role in early and late stages of learning processes and has a crucial impact on decision-making. Thus, intuitive and unconscious thinking is crucial for management processes in general and production planning processes in the main. However, unconscious knowledge and human behaviour is predominantly neglected in production research. Especially the addressing of human machine interfaces (HMI), human cognitive biases have a crucial impact on decision making processes. Constellation work is based on unconscious knowledge and intuition. Thus, systemic structural constellations are an innovative tool to integrate unconscious knowledge in a research context. In systemic structural constellations specific foci of complex systems, such as a production system, can be simulated and represented through spatial arrangements of persons or symbols. So, the method was used to reveal relevant patterns of relationships, structures, interaction, implicit knowledge, including hidden or underlying dynamics and influences that are relevant to and within a production system to understand how the raised problems in HMI can be better solved. The guiding research question is: How can the use of structural systemic constellations improve decision-making processes in HMI contexts in production environments in order to increase sustainability? Results show sustainability seems to be a matter of consciousness and is closely linked to the bias group not enough meaning. Sustainability and complexity resemble more than being linked by trade-offs. The recognition of human biases can be trained to improve human-machine-interfaces and sustainability. Constellation work contributes to decision theory by supporting effectuation.
