Optimize for the Moment






Site Selection
A look at the most logistics-friendly locations in North America







Despite a company’s bestlaid logistics plans, site selection is a constantly changing variable. Companies frequently reach a crossroads in their logistics network optimization, often driven by some strategic change — a new product, a lease coming due, automation not working, or changes in product mix. That’s when logistics executives typically need to reevaluate and analyze their network and requirements.

The “where” in a logistics network affects the product mix as much as it is affected by product mix, says Vincent Gulisano, senior vice president of global sales, marketing and solutions engineering for APL Logistics (www.apllogistics.com). Product mix dictates transportation needs, but it isn’t a clear-cut linear relationship. Changes in product mix also affect storage and handling characteristics and automation needs, which can render an efficient facility sub-optimal.

Sometimes a location becomes wrong over time because of changing transportation rates, changing demographics, or other changes in a company’s business. For businesses driven by inbound and out-bound truckload shipments, the optimization process will favor points nearer manufacturing based on the economies of transportation, says Lamar Duncan, senior director of services engineering with APL Logistics. The optimization decision is cost- driven, and truckload moves are more economical. Goods are handled once, they go onto a truck and they are delivered.

However, when you start changing modes, the optimal location may change. Where the scenario is truckload in and less-than-truckload out, the more expensive outbound transportation will dictate the distribution center be located closer to demand points to minimize transportation costs. Transportation is the larger part of the location equation, accounting for as much as $3 or $4 for every $1 spent on warehousing.

Though transportation is the item most logistics executives are trying to reduce through optimization, the expectation is often that both warehousing and transportation costs can be reduced in the network optimization process. The process might involve two or three iterations, or it could run as high as 15 or 20, says Duncan. Optimizing a logistics network is complicated, after all, and may require running numerous factors through the decision cycle a number of times to come up with the right answer.

“I’ve seen people fall in love with technology and automation in their facilities,” says Gulisano, “and it becomes a noose around their necks.” Another risk is buying or leasing a facility built for someone else’s automation requirements and trying to retrofit it to your product. Gulisano remembers a consumer packaged goods company trying to fit its product down chutes designed to handle T-shirts. It was workable until the company hit seasonal volumes, then the inappropriateness of those automated chutes became
painfully obvious.

On the automation theme, Gulisano recommends planning for changes in product, order sizes and load design. Improvements in storage methods have to be weighed against the added labor. For instance, increasing stacking heights may require 10% more labor hours and 10% more spending for equipment to access product at the higher stacking levels, which reduces the overall benefit.

Getting the facility right is important, even if it is the lesser of the two major costs. Gulisano says it’s more complicated and harder to change the facility than it is to change transportation. If volumes
increase, you can throw more trucks at the dock, provided you have the dock space. But, you don’t always have the quare footage or people to throw at new volumes.

It’s always good to spend a little more on the facility for design features like dock cutouts or door knock outs that allow more flexibility, but Gulisano questions whether the project can gain approval with the added expense.

The facility is the heart of the supply chain, says Gulisano. “It’s pumping the blood out into the arteries. The transportation network is waiting to receive the blood, and if the facility is not designed right and you have trucks backed up and waiting, you won’t be able to optimize and get that network moving.”

“You find more catastrophic supply chain problems with the facility not working than with transportation not working,” Gulisano adds. “It’s easier to change carriers or transportation modes than it is to uproot a facility, products, people, and all that goes with it.”

Some organizations review their networks periodically to ensure they remain at their optimal level, but for those organizations that review on an as-needed basis, they often aren’t prepared to complete the process. The one thing they lack most often is a firm set of transit times.

Transit time is a strategic decision based on customer service levels and cost to deliver those service levels. The decision can be centered on a set of customers, by product, or by region. If a company with national distribution patterns wants to provide two-day service to the majority of its customers, it typically takes five distribution centers to get it done, Duncan says. If, however, the service level can be five days, then two or three distribution centers might be sufficient.

Logistics executives should enter the network optimization process armed with shipping data on all current facilities and all of the economics associated with that network. More facilities will typically mean lower transportation costs because you are getting closer to the customer. But, there’s a practical limit to the number of facilities that will reduce transportation costs and still generate at payback.

There’s no rush to ownership despite historically low interest rates. Most manufacturing and distribution firms want to focus on their core competencies and minimize the risks associated with owning warehouses or distribution centers. Even thirdparty logistics providers (3PLs) don’t want to own property, says Duncan. Logistics executives who want to outsource distribution center operations need to recognize that 3PLs will expect them to share some of the risk associated with brick and-mortar operations.

A 3PL will typically commit to a market for a longer period than its client may be willing to lease a facility. That gives the 3PL an advantage in negotiating lease terms with a property owner, but it doesn’t let the client off the hook.

Most 3PLs will try to arrange their leases so they’re contiguous
with the contract terms. If the client isn’t willing to commit for the full term of the lease, 3PLs often try to make the customer the assignee of the lease so that if there is an early termination of the contract, the customer takes over responsibility for the remainder of the lease term. In other cases, customers may take on the lease and reduce the margin the 3PL builds into its responsibility as lease holder.

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September, 2003

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Logistics Today’s Logistics Quotient of American Cities — Southwest
Regional
Rank
Metropolitan Area Logistics
Industry
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Work
Force/
Labor
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Road
Infra-
structure
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Density/
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Safety
Condition
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Inter-
state
High-
ways
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Taxes
& Fees
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Rail-
Road
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borne
Com-
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Air
Cargo
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2001
National
Rank
2002
National
Rank
1 San Diego, CA 76 71 77 114 314 24 173 13 47 26 9 7
2 Los Angeles-Long Beach, CA 40 74 205 117 314 13 173 13 3 1 12 8
3 Houston, TX 2 14 202 315 141 55 186 38 1 13 15 11
4 Dallas, TX 5 16 211 307 141 4 186 38 197 2 31 31
5 San Antonio, TX 21 10 167 286 141 24 186 38 197 41 32 26
6 Corpus Christi, TX 111 46 154 224 141 160 186 38 5 100 40 34
7 Fort Worth-Arlington, TX 9 6 208 305 141 4 186 38 197 85 42 29
8 Beaumont-Port Arthur, TX 101 39 94 300 141 160 186 38 6 183 57 48
9 Austin-San Marcos, TX 43 7 178 301 141 160 186 38 197 20 58 54
10 Denver, CO 184 61 226 108 64 16 269 141 197 6 59 74
11 Riverside-San Bernardino, CA 59 97 128 133 314 55 173 13 197 107 60 35
12 El Paso, TX 12 15 311 236 141 125 186 38 197 29 61 52
13 Las Vegas, NV-AZ 105 41 268 165 38 125 128 213 197 25 64 116
14 Amarillo, TX 79 86 158 261 141 85 186 38 197 97 70 70
15 Tulsa, Okla. 199 29 237 140 301 114 26 166 59 63 73 76
16 Brownsville-Harlingen-San Benito, TX 31 11 316 190 141 298 186 38 50 76 75 53
17 Salt Lake City-Ogden, UT 144 26 223 116 83 24 288 205 197 32 76 61
18 Laredo, TX 14 4 306 121 141 160 186 38 197 188 79 56
19 Lubbock, TX 104 44 143 258 141 160 186 38 197 88 81 77
20 Odessa-Midland, TX 107 88 146 249 141 160 186 38 197 85 95 71
21 Greeley, CO 217 181 45 11 64 85 269 141 197 193 99 121
22 Oklahoma City, Okla. 201 9 233 186 301 16 26 166 197 75 101 89
23 Orange County, CA 54 100 183 126 314 125 173 13 197 138 104 35
24 Wichita Falls, TX 188 68 85 205 141 160 186 38 197 193 114 95
25 Abilene, TX 207 69 79 245 141 160 186 38 197 169 124 130
26 Phoenix-Mesa, AZ 129 45 203 222 51 85 269 282 197 12 125 99
27 Waco, TX 175 42 115 271 141 160 186 38 197 193 138 118
28 Flagstaff, AZ-UT 288 158 24 10 67 85 278 243 197 175 143 123
29 Killeen-Temple, TX 148 66 281 159 141 160 186 38 197 193 151 140
30 McAllen-Edinburg-Mission, TX 52 5 278 304 141 298 186 38 197 106 171 153
31 San Angelo, TX 212 138 91 130 141 298 186 38 197 174 171 170
32 Sherman-Denison, TX 154 136 71 210 141 298 186 38 197 193 179 182
33 Longview-Marshall, TX 112 49 261 301 141 160 186 38 197 193 183 182
34 Galveston-Texas City, TX 164 209 243 281 141 160 186 38 33 193 190 107
35 Pueblo, CO 318 269 35 9 64 160 269 141 197 192 194 225
36 Grand Junction, CO 310 207 110 21 64 160 269 141 197 193 198 194
37 Colorado Springs, CO 276 230 191 45 64 160 269 141 197 100 199 174
38 Bryan-College Station, TX 213 118 81 208 141 298 186 38 197 193 200 160
39 Tyler, TX 167 25 256 323 141 160 186 38 197 193 206 191
40 Tucson, AZ 226 95 253 196 51 85 269 282 197 61 216 154
41 Brazoria, TX 124 131 98 312 141 298 186 38 197 193 217 115
42 Ventura, CA 136 216 82 109 314 298 173 13 197 193 222 146
43 Santa Barbara-Santa Maria-Lompoc, CA 249 202 39 79 314 298 173 13 197 170 225 142
44 Bakersfield, CA 146 239 116 50 314 298 173 13 197 193 229 197
45 Provo-Orem, UT 245 121 163 100 83 160 288 205 197 193 233 235
46 Lawton, Okla. 323 109 245 60 301 160 26 166 197 193 237 238
47 Boulder-Longmont, CO 312 258 176 46 64 160 269 141 197 193 247 286
48 Victoria, TX 190 147 287 295 141 298 186 38 42 193 249 246
49 Albuquerque, N.M. 272 55 282 303 250 85 141 256 197 28 268 247
50 Fort Collins-Loveland, CO 296 285 258 40 64 160 269 141 197 193 275 269
51 Las Cruces, N.M. 298 51 184 282 250 85 141 256 197 193 284 277
52 Yuma, AZ 301 262 247 20 51 160 269 282 197 193 293 257
53 Enid, Okla. 314 196 266 143 301 298 26 166 197 193 314 294
54 Santa Fe, N.M. 328 141 204 284 250 160 141 256 197 191 319 307
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