Brewery Products (BP); of York, Pennsylvania, is the parent company of Chambersburg Beverage (CB); of Chambersburg, Pennsylvania. Over the past few years, BP has taken over beverage delivering duties of several accounts between CB and BP. These actions have reduced CB’s distribution responsibilities, increased BP’s distribution responsibilities, and more than likely have reduced overall costs to the company as a whole.

The purpose of this lab is to determine if cost was the underlying issue in regards to CB’s distribution responsibility reduction, or if the distribution center was merely the target of downsizing. I will produce a model of the profitable limits of both the Chambersburg and York locations and see how their limits relate.

There are a variety of objectives that must be completed to successfully fulfill this lab’s purpose. To begin, the appropriate type of data must be determined and then retrieved. The network analysis tools must then be understood and implemented to determine which shipping point serves each destination in the most cost effective manner. The results of the analyses must then be interpreted as an answer to the research question and supported through the production of visual aids.

The service area analysis revealed that CB is closest to seventeen destinations, while BP is closest to the other thirty two. Figure 2 shows how the road network distances compare between BP and CB. The closest facility calculations were used in conjunction with attribute table editing and field calculator implementation to create Figure 3. Figure 3 shows which locations are best served by each shipping point. As can be seen, BP can serve many more locations than CB. CB is the cheapest shipping point for seven destinations, while distribution from BP is cheapest for the other forty two destinations. This reveals the fact that CB acquires all of its products from BP and therefore absorbs the cost of a round trip delivery from BP to CB in all of its delivery costs. Therefore, cost was the underlying issue in CB’s distribution responsibility reduction

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The analysis portion of the project began with the use of Google Earth to find the location of each account and place a marker in that area. Once all forty nine accounts had been marked ,they were saved as a .kml file. This .kml file was then converted to a shapefile with the KML_SHP tool that was acquired from a fellow student and added to the ArcToolbox. All data was then added to a new ArcMap document. A query was then inserted in the definition query portion of the l “PAcounties ” layer’s properties. This definition query caused only the counties of interest (Adams, Cumberland, Franklin, Fulton, and York), in relation to the road network, to be displayed. The newly queried layer was then used as the basis of a clip (extract, analysis tools) to reduce the PAStateRoads2010 to only roads that are in the study area. This newly created layer was aptly named “RoadStudyArea.” The “BeerStores” layer was then manipulated using selections and data exporting to create three new shapefiles. These shapefiles consisted of; CB’s location, BP’s location, and the locations of all accounts or destinations. A network dataset was then created within the BevDist feature dataset. The network dataset was named BevDist_ND and the “RoadStudyArea” shapefile was selected as the feature class that would participate in the analysis. The new network dataset was then created and built. A “New Service Area Analysis” was then carried out. Two “facilities” locations were loaded, representing CB and BP. In the analysis settings; the impedance was set as the length in feet and default breaks were entered in feet representing 5, 10, 15, 20, 25, 30, and 35 miles. Polygons were generated, with the polygon type being ‘generalized.’ Accumulation attributes under the accumulation tab were set as length. Finally, the snap to closest road shape options were selected under the network locations tab with a tolerance of 100 meters. The solve button on the network analysis toolbar was clicked and the results were then manipulated and displayed in an exported bitmap. A “New Route” was created next. Two “stops” locations were loaded, CB and BP. The impedance was once again set as length in feet and under accumulation, length was again selected. The solve button was then clicked, and the shortest route between CB and BP was calculated and created. The attribute table of this route was then examined to find the distance between CB and BP. This distance in feet was then converted to miles. The resulting number was then multiplied by two, to determine the length of a single round-trip from BP to CB in miles. This value in miles was then multiplied by a cost per mile of $0.65. This cost per mile rate was determined from www.worktruckonline.com in conjunction with personal consideration. The determined cost of traveling from BP to CB, and then back to BP, was used as a base rate to be added to the per mile rate of each CB to destination round trip to be calculated in a future step. A “New Closest Facility” analysis was then established individually for both CB and BP. One “facilities” location was input for each analysis, in the first analysis BP and in the second CB. In addition, forty nine “Incidents” locations were input into both analyses, consisting of the forty nine destinations. The impedance was again set to length, travel was from facility to incident, and accumulation was set as length. The solve button was then clicked and the shortest route was calculated from BP to each destination in the first analysis, and CB to each destination in the second analysis. The “Routes” data was then exported from both analyses and added to the ArcMap document. These layers were named “BPcostEQ” and "CBcostEQ" respectively. An editing session was then started and stopped at separate moments in time for both the “BPcostEQ” and the “CBcostEQ” layers. While editing, a new field, “RoundTripCost” was added to both attribute tables. An equation converted the length calculated by ArcMap to miles, then converted this distance measurement into a cost measurement, and then accounted for the fact that a delivery would be a round trip from the distributor to each destination. A similar, but more complicated equation was used for the “CBcostEQ” attribute table’s “RoundTripCost” field. This equation was mostly the same, but the operating cost per mile for CB deliveries was $0.50 per mile instead of the $0.65 per mile of BP. This cost differential was used to recognize the fact that truck operation would be more expensive in a higher populated area such as York, PA due to inflated wages, insurance rates, and other related costs. In addition, the base rate calculated in the earlier route analysis was added to reflect the fact that all beverages distributed by CB are first shipped to CB from BP. Therefore, the base rate of $65.06 is the cost of a round trip between CB and BP and must be considered in the overall cost of distribution to each destination by CB. The cost of distribution from CB and BP to each destination was now present in separate attribute tables. The attribute tables of the “CBcostEQ” and “BPcostEQ” layers were joined, based on OBJECTID and preserving all fields. The joined attribute table was then used to determine which shipping point, CB or BP, was best suited to deliver to each destination in regards to cost. A new field was added to the attribute table, simply named BP__CB (BPcost-CBcost). The field calculator was implemented and the resulting values were simple to categorize; negative values signified that it was cheaper for BP to deliver to that destination, while positive values signified that it was cheaper for CB to deliver to that destination. The values of the BP__CB field were then used to create two new layers, one representing destinations best served by BP and one representing destinations best served by CB. These new layers were then implemented in the production of a visual aid that was exported as a bitmap.

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Load: Driver: Date: Time:

Item # Description Out Charge Sold Returns Truck Warehouse

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Table 1: Acquired Datasets

Distributor Data Provider Obtainment Method Dataset

PASDA PennDOT Internet PA counties (2010)

PASDA PennDOT Internet PA state roads (2010)

Brewery Products Wendy Keesee Personal E-mail Partial Company Account List

Figure 1: Study Area of Analysis

Figure 2: Comparison of Road Network Distances of Shipping Point

Figure 3: Most Cost Effective Shipping Point for Destinations

By Aaron Grove

Geo420: GIS III with Dr. Scott Drzyzga, Fall 2010, Shippensburg University