Logistical Factors Influencing Location

One of the greatest challenges Upper Great Plains rural communities face in competing to attract value-added processing and manufacturing ventures is a lack of transportation options. Because of the lack of transportation options, location decisions are especially important for value-added processing and manufacturing ventures building in North Dakota. This study examines factors that influence the optimal location of such facilities in North Dakota.

Company investment decisions are based on profit-maximizing goals. As North Dakota competes for these investment dollars, logistical advantages, such as land values and labor costs, may be nullified by logistical disadvantages, such as freight rates and intermodal access. It is important to identify and understand these factors to help improve North Dakota's competitive position.

When considering a business venture, other than a clear product and market definition (including the total size of the market, as well as the number and size of competitors); the next most important consideration is to define a network for the product. The network design should take into account the number, size, and location of suppliers, producers, distributors, wholesalers, and retailers.

Specific factors to examine when considering the location of one particular component of the network, for example, a value-added processing facility, include:

1. Labor climate
2. Transportation availability
3. Proximity to markets/customers
4. Quality of life
5. Taxes/Industrial development incentives
6. Supplier networks
7. Land costs/utilities
8. Company preference

A number of important factors described above can be examined easily in a linear programming spreadsheet model to help make a location decision. These factors include the availability and cost of raw materials, capacity and operating costs of the proposed processing facilities/plants, transportation costs to ship from raw material sources to the plants and from the plants to the customers, and customer demand. One example of such a model is presented.

The objective of the model was to minimize total costs subject to four constraints: (1) each customer region's demand must be met; (2) for each supply source, raw material supply capacities can not be exceeded; (3) for each plant, the capacity of the plant can not be exceeded; and (4) for each plant, the amount of raw materials transported to the plant should equal the amount of product that is transported from the plant (i.e., there can not be more output than input).

Case studies were used to illustrate the model and consider the problem of whether to locate a new processing plant in northwest, south central, north central, or northeast North Dakota. This case study assumed a raw material supply was available in northeast, northwest, and southeast North Dakota, as well as in central Montana, to serve a proposed plant. It also assumed the amount and cost of raw material supply available are equal at each location. The case study further assumed that for each proposed plant, the plant capacity, fixed costs, and operating costs would be equal. These are all changeable in the model to reflect specific product information for different applications.

The first inputs needed for the model are the transportation costs to ship from each raw material supply source to each proposed plant, as well as the plant capacity and fixed/operating costs. The particular costs used in this case study are illustrated in Table 2, and are on a per hundredweight basis.

Table 3. Costs to Ship from Raw Material Supply Sources to Plants (Case 1)

The second set of inputs to the model were costs to ship from each proposed plant to each customer, as well as an estimate of the customer demand. The information used in this case study is displayed in Table 3.

Table 4. Costs to Ship from Plants to Customers and Customer Demand (Case 1)

The first decision part of the model considers the supply available at each raw material supply source and the volume to ship from each source to each plant. In the case study example in Table 4, the model recommended shipping 13,555,000 units from the supply source in northeast North Dakota to a plant located in northeast North Dakota.

Table 5. Volume to Ship from Raw Material Supply Sources to Plants (Case 1)

The second decision part of the model considered the volume to ship from each plant to each customer. The case study model in Table 5 recommended making all shipments to customers from the northeast North Dakota plant.

Table 6. Volume to Ship from Plants to Customers (Case 1)

Given the above decisions from the case study model, total costs for the proposed plant are $8,580,287 annually. The model estimated the lowest annual total costs for northeast North Dakota of all locations considered.

The model described in the previous case studies can be a useful tool helping in location decisions for a processing facility. It considers a number of important factors, such as transportation costs, raw material availability and cost, as well as costs associated with proposed plants. In addition, inputs to the model can be changed easily to allowing for many different scenarios. The model can demonstrate the benefits of a location over another based on factors such as available freight rates and land or labor costs. However, making a final decision, many other factors must be considered.