Supply Chain

Misc

Flow Distribution of Units Between Production Areas and Markets

  • Data
    • Source: the production facility name (left-side)
    • Target: the market supplied (right-side)
    • Units: the number of items flowing (width of bars)
  • Interpretation
    • India is the biggest country for production output
    • Japan market demand is mainly supplied locally
    • USA and Germany do not have local production facilities

Network Optimization

  • X-Axis: each column represents a demand scenario (i.e. there are 50 demand scenarios in this example)
  • Y-Axis: are the production/supply locations
  • A blue box means that that location is included in the optimal configuration of locations for that scenario
    • e.g. In scenario 1, having a low capacity facility in India and a high capacity facility in India is optimal for this scenario.
  • I think this viz can be done with {waffle} using geom_waffle without theme_enhance_waffle
  • Simulate how the variability of demand in various markets (e.g. 50 scenarios) affects the optimal distribution of production/supply locations
    • Hopefully a configuration of locations will be optimal for a preponderance of scenarios. Assuming each scenario is equally important, that configuration of locations is the optimal choice.
      • Or I guess you could weight each scenario by frequency or something. Maybe you have a distribution of scenarios from which you drawing from.
  • Linear programming
    • Also see Optimization, Equation Systems
    • Set decision variable, objective function
    • List the constraints according to the demand for each market
    • Solutions are indicator variables for production/supply locations and whether they are 1 or 0.
      • There should be a boolean variable for a high capacity location and low capacity location in each country
      • For each variable, 1 indicates that location should be built or that it should be in operation at that particular capacity

Pareto Plot

  • (Unfinished Note but I think there is an examle of a Pareto Plot in Warehouse Management)
  • Data
    • “BOX” is the number of box/packs picked of that product (“SKU”) for that order (“ORDER_NUMBER”) on that date (“DATE_FORMAT”)
  • Preprocessing
    • Sum the number of boxes picked per SKU
    • Sort your data frame by descending order on BOX quantity
    • Calculate the cumulative sum of BOX
    • Calculate the cumulative number of SKU