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Visualizing the Supply Chain

This presentation explores various visualization techniques for the supply chain, emphasizing the importance of flow charts to track parts and optimize processes. It covers transportation maps depicting interstate commerce shipments from California, showcasing unique modes of transport analyzed through geographic and statistical data. Additionally, the session incorporates mashups with extreme weather data, highlighting the impact of tornadoes on freight logistics from 1950 to 2010. Participants will gain insights into effective visualization strategies that enhance supply chain understanding.

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Visualizing the Supply Chain

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  1. Visualizing the Supply Chain Bay Area R Users’ Group 2011.12.13 Anthony Sabbadini sabbadini@economicriskmanagement.com

  2. Outline • 1. Flow Charts • 2. Transportation Maps • 3. Mashups

  3. 1. Flow Charts

  4. Keeping Track of Parts is Important

  5. This was especially important where I used to Work

  6. Flow Charts can Help

  7. Flow Charts with ‘diagram’ library(diagram) names <- c("Chicago", "Detroit", "Fremont", "LA", "Memphis", "San Antonio", "Tijuana") M <- matrix(nrow = 7, ncol = 7, byrow = TRUE, data = c( # ch de fr la me sa ti 0, 1, 0, 0, 0, 0, 0, #ch 0, 0, 0, 0, 0, 0, 0, #de 1, 0, 0, 1, 1, 1, 1, #fr 1, 0, 0, 0, 1, 0, 0, #la 0, 0, 0, 0, 0, 0, 0, #me 0, 0, 0, 0, 0, 0, 0, #sa 0, 0, 1, 1, 0, 1, 0 #ti )) png(’nummi.png') pp <- plotmat(M, pos = c(1,3,2,1), curve = 0.1, name = names, lwd = 1, box.lwd = 2, cex.txt = 0.8, box.type = "square", box.prop = 0.5, arr.type = "triangle", arr.pos = 0.4, shadow.size = 0.00, prefix = "f", main = "NUMMI network") dev.off()

  8. NUMMI factory network

  9. 2. Transportation Maps

  10. Interstate Commerce Shipments from California (2007) source: http://ops.fhwa.dot.gov/freight/freight_analysis/faf/index.htm

  11. Spoke Networks with ‘geosphere’ library(maps) library(geosphere) # Unique transportation modes modes <- unique(california$DMODE_MEANING) # Color pal <- colorRampPalette(c("#333333", "white", "#1292db")) colors <- pal(100) # Cycle through the transportation modes for (i in 1:length(modes)) { pdf(paste("value_mode", modes[i], ".pdf", sep=""), width=11, height=7) map("world", col="#191919", fill=TRUE, bg="#000000", lwd=0.05, xlim=xlim, ylim=ylim) csub <- california[california$DMODE_MEANING == modes[i],] csub <- csub[order(csub$Value..mil.),] maxcnt <- max(csub$Value..mil.) for (j in 1:length(csub$GEOGRAPHY)) { arc <- csub[j,] inter <- gcIntermediate(c(arc[1,]$Long_origin, arc[1,]$Lat_origin), c(arc[1,]$Long_dest, arc[1,]$Lat_dest), n=100, addStartEnd=TRUE) colindex <- round( (arc[1,]$Value..mil. / maxcnt) * length(colors) ) lines(inter, col=colors[colindex], lwd=0.6) } dev.off() }

  12. Truck shipments ($)

  13. Rail ($)

  14. Air ($)

  15. All ($)

  16. 3. Mashups

  17. Extreme Weather (NYT)

  18. Tornado Data (NOAA)

  19. Tornados in 1999 (by property damage $)

  20. Truck & Rail Shipments+ Tornados[time lapse: 1950-2010] +

  21. 1950

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