OMCS3

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Case Study

Background:

American International Automotive Industries (AIAI) manufactures auto and truck engine, transmission, and chassis parts for manufacturers and repair companies in the United States, South America, Canada, Mexico, Asia and Europe. The company transports to its foreign markets by container ships. The company wishes to expand its business & select site for new European warehouse/ distribution center. The site should be selected such that it has minimum distance from the cities where supply is to be done to seven major customers.

Quantitative analysis:

http://www.prenhall.com/divisions/bp/app/russellcd/PROTECT/CHAPTERS/CHAP09/HEAD06.HTM

Plant sites (x, y)	Load	*Distribution center sites (x, y)*
Vienna (300, 60)	160	Dresden (225, 225)
Leipzig (180, 225)	100	Lodz (420, 250)
Budapest (390, 50)	180	Hamburg (90, 340)
Prague (240, 160)	210	Gdansk (370, 360)
Krakow (400, 170)	90	Frankfurt (40, 160)
Munich (150, 60)	120
Frankfurt (40, 160)	50

· Using the load-distance technique:

Comparative chart analysis was used to identify the site of distribution that is placed optimally w.r.t the customer sites.The five potential sites were Dresden, Lodz, Hamburg, Gdansk, and Frankfurt. & customer locations were Vienna, Leipzig, Budapest, Prague, Krakow, Munich, and Frankfurt. The distances of all customer location from each distribution site were obtained using standard map of Europe. The data is represented in the form of cross table as shown below.

Sites	Distance in miles	No. of containers to be shipped to the following places
	time taken to reach	Vienna	Leipzig	Budapest	Prague	Krakow	Munich	Frankfurt	Total
Dresdon	miles	308	74	426	98.4	325	286	288	1805.4
	time	5 hr 40 min	1 hr 22 min	7 hr 13 min	2 hr	5 hr 22 min	4 hr 34 min	4 hr 34 min
Lodz	miles	365	385	484	339	164	631	609	2977
	time	6 hr 48 min	6 hr 14 min	8 hr 18 min	6 hr 56 min	3 hr 26 min	9 hr 51 min	9 hr 31 min
Hamburg	miles	610	246	732	404	562	483	310	3347
	time	10 hr 13 min	4 hr 4 min	11 hr 48 min	6 hr 56 min	9 hr	7 hr 39 min	5 hr 5 min
Gdansk	miles	569	456	689	545	368	702	680	4009
	time	10 h 4 min	7 hr 58 min	11 hr 37 min	9 hr 32 min	6 hr 38 min	11 hr 36 min	11 hr 17 min
Frankfurt	miles	446	243	601	321	605	244	0	2460
	time	7 hr 17 min	3 hr 55 min	9 hr 30 min	5 hr 19 min	9 hr 28 min	4 hr	-

This table suggest that Dresdon could be an ideal site from where customer’s location is nearby, Given that there is uniform supply to each customer location. . Graphical representation further gives clearer picture.

However, since the supply is not uniformly distributed there will be different charges involved in shipping of the container site is assumed that shipping container costs Rs X/ mile & that this price is uniform throughout Europe.

Hence it is not possible to identify the distribution site solely using Table -1.

Table _ 2 –

Sites	Price factor involved in shipping from the distribution site to
	Vienna	Leipzig	Budapest	Prague	Krakow	Munich	Frankfurt	Total
Dresden	49280	7400	76680	20664	29250	34320	14400	231994
Lodz	58400	38500	87120	71190	14760	75720	30450	376140
Hamburg	97600	24600	131760	84840	50580	57960	15500	462840
Gdansk	91040	45600	124020	114450	33120	84240	34000	526470
Frankfurt	71360	24300	108180	67410	54450	29280	0	354980

it indicates price factor involvedd in shipping containers to customer location from each distribution site. This takes care of supply being on uniform. This is obtained by multiplying each entry of table 1 by no of containers (specific to the customer location)

We can use this table to conclude which distribution site involves minimum price factor. This conclusively suggest that company AIAI should open distribution site at dresdon

Here we have made the assumption that shipping rates are directly dependent on no of containers.

check this link http://www74.homepage.villanova.edu/sohail.chaudhry/MBA8503/Solutions/Russell%20Taylor%204th/ismsup05.pdf

Other answer that I found:

Load Distance Technique

I concluded that the best way to approach this problem is to start with the load-distance technique. The work is shown in Exhibit A. This is more unique than the Center of Gravity model because this formula takes into account distances and weights between facilities and does an individual analysis for each site. A Center of Gravity equation on the other hand, does an analysis where all sites are averaged and a central location is had. The load distance technique shows the plotting of each of the seven customer locations, their x/y map coordinates that show on a graph, and also the five potential site locations and their corresponding x/y map coordinates. It also shows the number of containers delivered to each customer per month. The OM tool was really helpful with this calculation because it calculates between the existing customer facilities and the proposed sites to assign a load-distance value to each of the potential locations.

Dresden is the best site based on using the load distance technique. Dresden is the site with the lightest load distance value of 133,161.0. Looking at a map of Europe, I was able to see that Dresden is centrally located among AIAI’s major customers in Vienna, Leipzig, Budapest, Prague, Krakow, Munich, and Frankfurt which is also attractive for a new site. Also, other factors should be taken under consideration when looking at Dresden. It was mentioned in the text of the case problem that AIAI currently ships into the port of Hamburg, so a warehouse/distribution center there would eliminate transport from the port of entry to a distribution center at Dresden or anywhere else.

Based on the load distance technique, Hamburg is the worst consideration for a new site, but is close to the port, Hamburg might be a more attractive choice. On the other hand, looking at a map of Europe, Dresden is closer to the plants in Vienna and Budapest, and there is a possibility that these plants will continuously need to be replenished with supplies. Another downside of Dresden for a location is that it is located in old East Germany, and that area is known problems with decaying infrastructure, and old agriculture.

Center of Gravity

The center of gravity formula is shown in Exhibit B. The OM tools application was used to calculate the formula. The center of gravity method does a quantitative analysis of locating a site at the center of movement in a geographic area. The distances that are of the result are based on weight and distance and are placed almost exactly between two or more comparable coordinates. The OM tool was used to calculate a new x/y coordinate for the potential warehouse/distribution center. The calculation of this coordinate is x/y: 266.59/115.60 as shown in Exhibit B. I looked at each of the five potential site’s coordinates against the coordinate calculation. The closest potential site coordinate to the calculated center of gravity will ultimately determine the best of the five locations to build the new warehouse/distribution center. Looking at the data, I determined that Dresden (x/y:225/225) is the best location. The answer is based on straight-lined decisions, and a real situation may warrant a slightly different answer because of routes that may or may not be available.

Distribution of centrewise distance from site to customer's location

Ditance from the sites to Vienna Dresdon Lodz Hamburg Gdansk Frankfurt 308.0 365.0 610.0 569.0 446.0 Ditance from the sites to Leipzig Dresdon Lodz Hamburg Gdansk Frankfurt 74.0 385.0 246.0 456.0 243.0 Ditance from the sites to Budapest Dresdon Lodz Hamburg Gdansk Frankfurt 426.0 484.0 732.0 689.0 601.0 Ditance from the sites to Prague Dresdon Lodz Hamburg Gdansk Frankfurt 98.4 339.0 404.0 545.0 321.0 Ditance from the sites to Krakow Dresdon Lodz Hamburg Gdansk Frankf urt 325.0 164.0 562.0 368.0 605.0 Ditance from the sites to Munich Dresdon Lodz Hamburg Gdansk Frankfurt 286.0 631.0 483.0 702.0 244.0 Ditance from the sites to Frankfurt Dresdon Lodz Hamburg Gdansk Frankfurt 288.0 609.0 310.0 680.0 0.0

Potential sites

Distance in miles

Prcie factor associated with shipping

Price factor involved in shipping from the distribution site to Vienna Dresden Lodz Hamburg Gdansk Frankfurt 49280.0 58400.0 97600.0 91040.0 71360.0 Price factor involved in shipping from the distribution site to Leipzig Dresden Lodz Hamburg Gdansk Frankfurt 7400.0 38500.0 24600.0 45600.0 24300.0 Price factor involved in shipping from the distribution site to Budapest Dresden Lodz Hamburg Gdansk Frankfurt 76680.0 87120.0 131760.0 124020.0 108180.0 Price factor involved in shipping from the distribution site to Prague Dresden Lodz Hamburg Gdansk Frankfurt 20664.0 71190.0 84840.0 114450.0 67410.0 Price factor involved in shipping from the distribution site to Krakow Dresden Lodz Hamburg Gdansk Frankfurt 29250.0 14760.0 50580.0 33120.0 54450.0 Price factor involved in shi pping from the distribution site to Munich Dresden Lodz Hamburg Gdansk Frankfurt 34320.0 75720.0 57960.0 84240.0 29280.0 Price factor involved in shipping from the distribution site to Frankfurt Dresden Lodz Hamburg Gdansk Frankfurt 14400.0 30450.0 15500.0 34000.0 0.0