Page 297 - DMGT523_LOGISTICS_AND_SUPPLY_CHAIN_MANAGEMENT

Logistics and Supply Chain Management

Notes Specific customer data are also required to impart a spatial dimension to a logistics analysis. The
spatial dimension reflects the fact that effective logistics must consider the cost and time associated
with moving product across distance. Customers and markets are often aggregated by location,
type, size, order frequency, growth rate, and special logistical services to reduce analysis
complexity while not substantially reducing analysis accuracy.
For integrated channel analysis, it is necessary to identify and track the costs associated with
manufacturing and purchasing. This often requires further classification using a bill of materials.
While manufacturing plant locations may not be a variable component in a logistical system
design, it is often necessary to consider the number and location of plants, product mix, production
schedules, and seasonality. Policies and costs associated with inventory transfer, reordering,
and warehouse processing must be identified. In particular, inventory control rules and product
allocation procedures are often important elements. Finally, for each current and potential
warehouse, it is necessary to establish operating costs, capacities, product mix and storage
levels, and service capabilities.
Transportation data requirements include the number and type of modes utilized, modal selection
criteria, rates and transit times, and shipping rules and policies. If private transportation is
included in the analysis, then corresponding information is required for the private fleet.
The preceding discussion offers some perspective regarding the necessary data to evaluate
logistics alternatives. The primary justification for placing the formal data collection process
after the selection of analysis technique is to allow data collection to match specific analysis
technique requirements. In other words, the design solution can be no better than the data it is
based on.

For most logistics analysis applications, market data is useful for evaluating future scenarios.
Management can normally provide an estimate of anticipated sales for future planning horizons.
The difficulty lies in obtaining market-by-market projections.
One solution to the problem is to use demographic projections that correlate highly with sales.

Example: Assume that sales or usage correlates highly with population. Using such a
correlation and government population projections, it is possible to estimate future demand
levels and thus determine future logistics requirements.
A variety of projections concerning demographic factors are regularly published by various
government agencies and universities. A number of zip code sources exist which provide useful
data for logistics planning. Thus, a reasonable data bank of environmental information is readily
available.

It is also useful to document competitive logistical system designs and flows to provide
information regarding competitor strategies and capabilities. In most cases, this information is
readily available from published material, annual reports, and general knowledge of company
executives. The main purpose in collecting such data is to provide competitive benchmarks that
compare customer service capabilities, distribution networks, and operating capabilities.

Collecting Data
Once alternative data sources have been identified, the data collection process can begin. The
process includes assembly of required data and conversion to appropriate formats for the analysis
tool. This is often a tedious and time-consuming task, so errors are likely. Potential errors include
collecting data from a misrepresentative time period and overlooking data that do not reflect
major components of logistics activity, such as customer pickup volume. For this reason, the data
collection process should be carefully documented to assist in identifying errors that might reduce
analysis accuracy and to determine any necessary changes to achieve acceptable accuracy.

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