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Business logistics refers to a group of related activities all involved in the movement and storage of products and information—from the sources of raw materials.
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Hence, a triangle of logistics strategy. Not all of these objectives can be achieved simultaneously since they may be in conflict. For example, minimizing costs and simultaneously maximizing service are incompatible. Research through the years, coupled with advancements in computer technology, has provided the capability to design logistics networks with the aid of mathematical modeling. The logistics network design problem is captured in the abstract diagram of Figure 4.

The strategic questions revolve around locating the facilities intermediate to source and demand points, determining the modes of transportation, projecting the amount of aggregate inventory in the logistics system, and controlling the design impact on logistics customer service. Network models used commercially are generally of two types: mathematical programming and computer simulation. Spatially geographical based models have been much more popular than temporally time based ones.

What is a Logistics Company? How a Logistics Company Works

Spatial models have primarily been used to locate the facilities in the logistics network on a geographical plane. They answer such questions as: How many facilities are needed, where should they be located, and what size should they be? Logistics-related costs are minimized, or profits maximized, subject to geographical constraints on customer service and restrictions on facility capacities.

Continuous location methods e. The reason for this would seem to be that the deterministic methods can handle most of the costs of location with a great deal of realism and also that restrictions can be introduced that are not easily handled within the continuous framework. Much of the development in the last 20 years has focused on making location models more user-friendly, adding features that enhance communication, and adding programs that facilitate data conversion to model format. Extended solution capabilities, such as adding more echelons to the network and more facilities, source points, and customers to be analyzed, have resulted primarily from greater memory and computational speed of computers.

When time becomes the important variable to be managed, as is the case when controlling inventories, choosing a transportation service, or scheduling product flow, computer simulation has been a good model choice. Such models permit the flow of product in the supply channel to be observed in simulated time. Complex product flow interactions among activities taking place between multiple echelons can be observed. Inventory levels, vehicle loading and shipping patterns, out-of-stock percentages and cost profiles are a few of the results obtained when the pattern of customer orders is placed on the simulated system.

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The extensive information detail for logistics channel simulators is generally not available in spatial models where products are grouped, costs are averaged, and the time frame for analysis is from a month to a year. However, they have not been used as extensively as spatial models. It also may be the case that the general computer simulation packages that can be adapted to deal with logistics problems fill the need. Little research is being conducted to develop channel simulators specifically designed for logistics planning.

Significant data requirements, lack of understanding of the value of simulation results compared to spatial model results, and limited promotion of the methodology are some of the reasons for the underutilization of this important methodology. The spatial and temporal aspects of logistics network design have not been effectively merged into one solution platform, although it is possible to run such models separately and sequentially to converge on network solutions that satisfy both strategic and tactical dimensions.

Neither a location model nor a channel simulator model by itself is a complete model for logistics network design. Location models rely on generalized inventory-throughput relationships and assumptions about the methods of transportation used as inputs and give good results on location issues.

On the other hand, channel simulators take facility locations as inputs and provide good results on inventory and transportation issues. The two models are inter-related, as shown in Figure 5. Research should be directed at bringing spatial and temporal dimensions together, probably within the location model framework since it is the most popular modeling platform. Perhaps the research needed most in logistics network design is to find methods for determining the relationship between the level of logistics customer service provided and the revenues generated by the firm.

Current practice is to treat customer service as a constraint on network design and minimize costs subject to the constraint. However, the preferred practice is to maximize profit when both revenue and cost are variables, since the customer service level will be set at optimal based on economic factors. The network design can be quite different depending on the objective used, that is, profit maximization versus cost minimization. Developing the revenue-logistics service relationship for a particular firm can be as difficult as determining the effectiveness of its advertising budget or other sales efforts.

For location models, the revenue-service relationship can be expressed as a price function where price declines as customers are farther from their sourcing points. For channel simulators, the appropriate variable might be customer order cycle time. Research could begin with the methods used for similar problems in marketing. Inventory represents a key economic factor in network design that forces consolidation of inventories into a small number of locations.

There has been substantial research over the years on controlling individual product item inventory levels but relatively little about estimating inventory levels when there is more than one product item taken at a time. The practical concerns of network design require that many items be collected into product families and dealt with as an aggregate group. What is needed is to be able to estimate inventory levels as demand is assigned to facilities. However, these rules are generally based on inventory control procedures formulated around the economic order concept.

Estimating inventory levels based on other rules found in practice would be beneficial. The relationship between inventory levels in facilities and the allocated demand to them is frequently nonlinear and concave. Since the solution platform for location models is typically linear or mixed integer programming, the nonlinear relationship causes computational difficulties.

Better methods, besides decomposing the function into piecewise linear elements, are needed so that the inventory relationship can easily be incorporated into the computational process. Too often, the inventory consolidation effects of network design are computed outside of the solution process to avoid these computational difficulties. Two transportation problems arise in logistics network planning as a result of using mathematical programming to design the network. These are the handling of private trucking and the selection of transportation services.

When using a linear programming-based solution method for network planning, it is assumed that transportation between points on the network is one-way.

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For-hire transportation fits this assumption since rates are quoted between two specified points. However, when transportation involves more than one stop before the delivery vehicle returns to its depot, transportation costs may be in error. Since equivalent rates must be calculated for each transport leg from the multiple-stop routes, the stops customers on the route and the depot facility to which they are assigned must be known. However, assigning customers to facilities is the result of the location analysis. Rates are a result of customer allocation and customer allocation depends on rates.

Therefore, research needs to be conducted on combining transport routing and location-allocation analysis. In a channel simulator, the transportation service can be selected based on the size and characteristics of the order when it is presented for delivery.

In a locator where products are grouped into families of items and transportation rates are constructed based on average shipment size, transportation modes are represented within the rate data. It is then assumed that the mode mix remains constant as reallocation of demand occurs among facilities during optimization.

Since the various modes used are combined into a weighted rate for product volume flowing from a facility, the percentage weights of shipments by the various modes should change as the network design is being calculated.

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This interdependence is an area for research in location models. The use of location modeling has been a dominant approach for logistics network design. At this point in time, the methodology which is based on linear programming concepts is well refined and quite robust in handling a wide range of practical network design problems.

Problems having numerous product families, thousands of customers, hundreds of intermediate facilities, and 4 or more network echelons are routinely solved. Most of the relevant costs can be handled by the methodology. However, designers are always wanting to press the model's limits which leaves opportunities for further research. Several of these are discussed below. A problem not easily handled within the standard network model occurs when vendors, as opposed to plants, supply the network.

Plants are usually represented by a location that can supply product up to its limit of capacity. In contrast, vendors supply only a portion of their total capacity to a particular firm's network and ship to all intermediate facilities a percentage of that firm's demand. In addition, a vendor may ship to the network from a number of locations.

A network design involving many vendors generally takes place for retail oriented companies. A network design problem occurs when optimization methodology will allocate to some vendors and not to others according to their costs and capacity limitations. The allocation results do not represent the actual flow patterns for the vendors. Adjustments are needed in the computational methodology. Users seem to like the feature that the continuous location model will give locational choices without preselection of candidate facilities, as is the case for deterministic methods.

It is well known that it is difficult to obtain exact solutions to continuous models having multiple facilities and a rich environment of logistics costs and constraints. Currently, continuous location models provide candidate locations for deterministic models to further evaluate. Research might be directed at improving the continuous location models for commercial-grade use and integrating them into the deterministic methods for facility location.

Much can be said about the need for improved understanding of the data relationships that are presented to the network analyzers. Whereas a great deal of research has been conducted on the methods by which to design the network, very little attention has been given to data elements that are the inputs to the design process. Yet, the solution quality of the network design is probably more sensitive to variations in data inputs than differences among solution methods.

Transport rates can be presented to a network model as specific rates by particular mode between defined points in the network. For practical-size problems, this may involve millions of rates. Alternately, transport rate curves can be developed that can be used for estimating rates for various distances from a facility.

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These rate curves can be developed from a sample of the actual rates and are usually a function of distance alone. Although a linear relationship between rates and distance work well for the for-hire carriers, it is not clear that it is the best fit for small package shipments or full vehicle load rates. Non-uniformly applied discounts and tariffs potentially can distort the linear relationship. Research to find good rate relationships and project the error that is involved in such estimating procedures is needed BALLOU, Costs associated with facilities are available through accounting reports.

For network analysis, the costs need to be separated into fixed, storage, and handling categories. Since the separation is arbitrary, the network design may be dramatically influenced by the cost allocation. For example, while one analyst may view such costs as trash removal, fire protection, and telephone charges as variable with the volume flowing through the facility, others may see these as fixed costs.

Of course, the number of facilities in a network may be greatly influenced by this arbitrary allocation of expenses.

Expense allocation rules need to be tested to show just how they affect network design and developed to provide reasonable and consistent treatment of these facility expenses. When thousands of customer locations are involved in the network design, it is practical to aggregate them into a smaller number of geographical clusters. This reduces the amount of data to be handled and the computational and computer memory requirements.

Demand clustering can result in errors when estimating the transportation cost to customers, since the basis for transportation rates is the center of the clusters and not the actual location of each customer. Research has shown that Logistics is a word that was initially used to refer to the process of sending supplies and equipment to military troops. However, today, the word has immense value in businesses development.