Supply Chain Check - SCC
SCC – the tool for checking time-sensitive supply chains
Various industries have implemented complex supply chains for organizing and controlling material- and information flow via different value-added steps from suppliers to customers.
The supply chains of some industry sectors are extremely time-sensitive, e.g. the just-in-time deliveries in the automotive industry. In this case malfunction of supply chains may cause production down-time at the end of the supply chain.
The SCC-procedure supports the check of supply chains in terms of robustness compared to malfunction as well as the effectivity of measures.
The supply chains of some industry sectors are extremely time-sensitive, e.g. the just-in-time deliveries in the automotive industry. In this case malfunction of supply chains may cause production down-time at the end of the supply chain.
The SCC-procedure supports the check of supply chains in terms of robustness compared to malfunction as well as the effectivity of measures.
SCC – the principle
SCC is a simulation-based procedure for modelling the supply chain and analyzing different scenarios.
The SCC-procedure includes the following steps:
Preliminary clarification:
Modelling:
Analysis:
The SCC-procedure includes the following steps:
Preliminary clarification:
- Clarification of targets and related key figures, e.g. parts availability
- Identification of all relevant process steps and its interactions
- Clarification of available supply chain data
- Evaluation of all data for normal process mode
- Identification of possible malfunction of each process step
- Generation of typical fault data
Modelling:
- Supply-chain modelling by means of a simulation tool, e.g. VENSIM
- Data configuration concerning normal and malfunctioning operation
Analysis:
- Implementing different scenarios for normal operation and malfunctioning operation (single or multiple incidents)
- Identifying critical malfunction or combinations of malfunction for the supply-chain
- Testing the effectivity of counter measures