I’d like to see plans that assume we are operating a stochastic system—one with variation. Te best way to think about this is that there are a number of scenarios that represent the possible actual outcomes of something that is as complicated as the retrograde that the military’s planning.
Tere will, of course, be a range of inputs, the things that happen every day that are part of the plan. Tere will be a range of possible performance each day in those inputs. And that leads to a range of out- puts that are almost guaranteed to not be the average values that you would pre- dict upfront. Of course, we should audit the model outcomes for reasonableness, which helps us learn together. Planning for variation usually produces a deeper understanding of how the team should react if the actual mission goes awry.
Stochastic models help uncover poten- tial bottlenecks. (Bottlenecks are choke points that determine the overall throughput of the system.) I believe you have a good operational plan when you decide where you want the bottlenecks to be. Surprise bottlenecks indicate poor planning. As my team examines the model inputs and outputs, I would direct us to decide where we will accept con- straints (or capacity limits).
As planning progresses, I would pay par- ticular attention to the resources and leadership assigned to each bottleneck. Our team would ask: Where do we have the most flexibility in the overall plan? Where do we have the least flexibility and the fewest options for recovery? We’d perform “what if” scenarios: What if we lose transportation capacity? What if a particular load area comes under bad weather? What if we have political inter- ference in a particular country? And then what you do when you play those what-ifs is, you look at the outputs of the model
and ask, do they seem reasonable? I have no idea what the percentage is, but sup- pose that 20 percent of our assets are in a particular part of Afghanistan, and in that area it will be very difficult to get the permission that we need to move things out. Well, a scenario that we would run would be, what if we can’t move 20 per- cent of the items for an additional month, two months? If the model predicts that this has no impact on the ultimate mis- sion, I’m going to be very skeptical.
Q. Of course, in the case of Afghanistan, that’s hugely complex, because shipment through Pakistan is off-again situation.
A. Right. So you just described a political impediment to achieving the expected value—what time we expect it would take
for an item to move from where it is to a location where we have a little bit more control over our ability to move it. Tis might be the item that ends up having the most impact on the variation in the plan, the place where we would need to have the most flexibility because we can’t be sure of our underlying ability to meet the plan.
such an on-again,
I would expect to find variation all over the place. We’ll find it in the capacity associated with the natural “batches” that we use in moving items from one point to another. Tose batches are usually constrained by the size of trucks, con- voys, railcars or ships. Sometimes you lose capacity because of mechanical fail- ure, or you have to substitute one mode for another mode. Goods can arrive early, exceeding the storage capacity at the load- ing point. And then you have humans
WHEN NATURE INTERFERES
Bad weather is one of the many less predictable variables in a logistics operation that thorough what-if planning can help manage. Here, an Army convoy stops on the Terra Pass in Logar province, Afghanistan, Dec. 15, 2012, to help Afghan truckers put snow chains on their tires. (U.S. Army photo by SPC Tayler Rovere)
ASC.ARMY.MIL
115
CRITICAL THINKING
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156 |
Page 157 |
Page 158 |
Page 159 |
Page 160 |
Page 161 |
Page 162 |
Page 163 |
Page 164 |
Page 165 |
Page 166 |
Page 167 |
Page 168 |
Page 169 |
Page 170 |
Page 171 |
Page 172 |
Page 173 |
Page 174 |
Page 175 |
Page 176 |
Page 177 |
Page 178 |
Page 179 |
Page 180 |
Page 181 |
Page 182 |
Page 183 |
Page 184 |
Page 185 |
Page 186 |
Page 187 |
Page 188 |
Page 189 |
Page 190 |
Page 191 |
Page 192 |
Page 193 |
Page 194 |
Page 195 |
Page 196
