Supply chain simulation tools and techniques: a survey <s> Validation and verification (V & V) <s> This paper shows which statistical techniques can be used to validate simulation models, depending on which real-life data are available. Concerning this availability, three situations are distinguished: (i) no data; (ii) only output data; and (iii) both input and output data. In case (i)-no real data-the analysts can still experiment with the simulation model to obtain simulated data; such an experiment should be guided by the statistical theory on the design of experiments. In case (ii) only output data-real and simulated output data can be compared through the well-known two-sample Student t statistic or certain other statistics. In case (iii)-input and output data-trace-driven simulation becomes possible, but validation should not proceed in the popular way (make a scatter plot with real and simulated outputs, fit a line, and test whether that line has unit slope and passes through the origin); alternative regression and bootstrap procedures are presented. Several case studies are summarized, to illustrate the three types of situations. <s> BIB001 </s> Supply chain simulation tools and techniques: a survey <s> Validation and verification (V & V) <s> Abstract The focus on supply chain management has increased in recent years; among academics as well as among practitioners. In this paper, we present a supply chain simulation study for a real case, concerned with the manufacturing of mobile communication systems. The purpose of the study is twofold. One objective is to evaluate alternative supply chain designs with respect to quality, lead-times and costs as the key performance parameters. A second objective is to increase the understanding of the interrelationships among these and other parameters, relevant for the design of the supply chain structure. The design alternatives differ in terms of the level of integration and synchronisation between supply chain stages. Modelling and validation issues are highlighted since these are fundamental to a supply chain simulation study. The results are discussed and a model capturing the relationships among cost, quality, and lead-times, based on the simulation study, is presented. <s> BIB002
The simulation model for the 'old' supply chain depicted in Figures 1 and 2 was validated and verified through discussions with Ericsson engineers; see again BIB002 . This establishes face validity; see  . Quantitative V & V may use DOE to check whether the estimated effects of changing inputs of the simulation model agree with the experts' qualitative knowledge about the system. A simple example is a queuing simulation: does an increase in simulated traffic give an increased average waiting time? A case study is reported by  : does the DEDS simulation give correct signs for the estimated effects of inputs (such as sonar tilt angle) on the output (detection of mines at the sea bottom)? If data on the real-world output are available, then real and simulated outputs may be compared statistically. An overview of the role of different statistical techniques-dependent on the availability of data-in V & V is given in BIB001 .