prachi admm conference paper

7/30/2019 Prachi Admm Conference Paper

1/4

Paper title: A Decision Support System for Warehouse Management

Prachi A. Deshmukh[1], Sriram Venkatesan[2], Dr. Subramanya K.N. [3]

1 MTech student, RVCE, Bangalore, [email protected], 8050005867

2. Manager, HTSL, Bangalore 3. H.O.D. and Professor, Dept of IEM, RVCE, Bangalore

ABSTRACT: In todays competitive business environment fastest and efficient decision makers win the race.Statistical and analytical tools, techniques, softwares and decision support systems make the decision makers jobmuch easier. Spreadsheet based decision modeling is widely used in business environment [6]. The functionalitiesand programmability of integrated spreadsheet software packages (such as excel) help building complex; butcustomized business models. We have developed a spreadsheet based decision support system for a conglomeratecompany which produces variety of products and is present at multiple locations across the globe. This system will

help the site manager to select the warehouse taking care of the constraints such as distance of the warehouse fromthe site, SQFT area available, ownership type and the business group to which the site/warehouse belongs.

INTRODUCTION:

Companies have lot of data flooding in everyday. Lack of data is no longer a problem; lack of actionableinformation from the data is the problem. Recently an economist at MIT Sloan School of management hasconducted a survey of 179 companies. The findings suggest that the companies that adapted data driven decisionmaking, have 5 to 6 percent more productivity than the others[3].

Decision support systems developed using analytical techniques can help a lot in decision making. Beloware the steps in building the Decision Support System:

i. Data Collection:

The data about sites and warehouses was collected from various sources of the company. When all datawas collected, the important decision variables were identified.

Location and geographical co-ordinates of sites and warehouses were found out. This was further used forcalculating the distance between each site and warehouse.The deciding factors for warehouse selection are:1.distance from the site2. Available area in each warehouse ( SQFT)3.Ownership Type: whether the warehouse is leased, Owned or 3PL

4.the business group to which the site/warehouse belongs: Site and warehouse belonging to same or similar can bepaired

ii. Data Cleansing:

While dealing with huge data coming from various sources there is often possibility of errors, inconsistencyin the data. Data cleansing is the process of removing these errors and inconsistencies to improve the data quality.Missing values, Duplicates, typographic error, inconsistency in data are some reasons of dirty data.

Datacollection

DataCleansing

Data analysisand decision

model

Development ofDecision Support

System
mailto:[email protected]:[email protected]


2/4

Using various standard data sources of the organization as reference, available data was cleaned, missing valueswere obtained.

i ii . Data Analysis And Decision Model:

Once the data was collected, cleaned and organized; in the next stage the important factors for warehouseselection were identified. The important factors to be considered while selecting the warehouse are distance from the

site, location, ownership type, size category and the business group to which the warehouse belongs. The approachwas to arrange the warehouses in ascending order based on their distance from the sites.

Using T Vincenties formula for distance measurement, distance matrix was formed between site andwarehouse. Here is the formula for distance measurement between two points A(LAT1,LONG1), B(LAT2,LONG2)distance=ACOS(COS(RADDIANS(90-LAT1))*COS(RADIANS(90-LAT2))+SIN(RADIANS(90-LAT1))*SIN(RADIAN(90-LAT2))*COS(RADIAN(LONG1-LONG2)))*6371

Once the matrix between all sites and warehouses is formed the nearest 10 warehouses and theircorresponding IDs are identified. Following excel formulae are used for that.=SMALL($F6:$DB6,1) for smallest distance, likewise can be found from 1 to 10=INDEX($F$1:$DB$1,1,MATCH(SMALL($F6:$DB6,1),$F6:$DB6,0)*1) ID of the nearest warehouse, likewisecorresponding IDs for 1to10 warehouses are found.

iv. Developing the Decision Support System:

This work is based on spreadsheet based decision model. Microsoft Excel is a basic analytical tool. Usingsimple and advanced excel functions and Visual basic for applications, programming language for excel a decisionsupport system is developed. This system will be helpful for the user for selecting the appropriate warehouse.

Homepage for the Decision Support System:

Apply Vincenties


3/4

The tool provides the user with 3 selection options. In all the three modes, the user has to select the site, and the tooldisplays the nearest 10 warehouses. This is based on the distance matrix discussed in previous section. Along withthe warehouse ID the other deciding factors such as ownership type, size category based on SQFT, location ofwarehouse, its distance from site, business group status ( whether it belong to same as that of site) etc. are displayedfor each warehouse option. Below are the 3 selection modes:

1. Global Selection: The user can view any site across the globe. User has to select a region, then country, city andfinally the site.

2. Business Group wise selection: This is for a user who might be knowing the site ID and is interested to view asite from particular business group.

3. Warehousing cost calculator: If the user is interested in calculating the cost of warehousing, this mode of toolasks to enter the required area, area available at the warehouse, per SQFT cost of warehousing, per km cost oftransportation and gives the approximate cost of warehousing.


4/4

CONCLUSION:Using the analytical techniques and tools, a decision support system is developed. This system isuseful for the users of different business groups of the organization. These business groups of the organization areoperating independently. Instead of going with the conventional approach of warehouse selection, use of this toolcan provide multiple options of warehouses. Strategic Business group, ownership type, size category and distanceare the deciding factors for selecting the warehouse. Considering these factors; different business groups can co-ordinate with each-others and utilize the available warehouses efficiently. This will help to reduce the cost.The tool also provides a cost calculator. Its can give the approximate cost of warehousing provided that the userknows the area available at the warehouse, rate per SQFT and the transportation cost/km.

REFERANCES:

[1]Remco Chang, Caroline Ziemkiewicz, Tera Marie Green, and William Ribarsky, Defining Insight for VisualAnalytics,Visualization viewpoints, published by IEEE computer society, IEEE computer and graphics andapplications, 0272-1716/09/$25.00 2009 IEEE , March/April 2009, pp. 14-17

[2]Olusegun Folorunso, Data Mining for Business Intelligence in Distribution Chain Analytics, InternationalJournal of the Computer, the Internet and Management, Vol. 18 No.1 (January-April, 2010), pp15-26

[3] Accenture Technology Vision 2013, http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Technology-Vision-2013.pdf,pp22

[4] Pack Chung Wong, Han-Wei Shen, Christopher R. Johnson, Chaomei Chen, Robert B. Ross, Top 10Challenges in extreme-scale visual analytics, Visualization viewpoints, published by IEEE computer society, IEEEcomputer and graphics and applications, 0272-1716/12/$31.00 2012 IEEE, pp 63-67

[5]Prof.Medha Kulkarni, Ashish Wadhaval, Preeyal Shinde, Decision Support System,International Journal ofEngineering Trends and Technology (IJETT) - Volume4Issue4- April 2013, pp 671-675

[6]D Mather, A framework for building spreadsheet based decision models, Journal of the Operational ResearchSociety (1999), pp70-74

[7] Rus Veronica Rozalia, Toader Valentin Spreadsheet based decision support systems,

http://steconomiceuoradea.ro/anale/volume/2008/v4-management-marketing/275.pdf

[8] S. R. Kolhe Business Intelligence (BI) Tools and Techniques: Indian Scenario, ISSN 0976-5832 Volume 2,Number 1 (2011), pp. 31-38

[9]Thomas H. Davenport and Jerry O'Dwyer, Tap into the power of analytics, CSCMPs Supply Chain Quarterly,Quarter 4 2011 issue

[10]T.Vincenty, finding the distance with geo coordinates (1978)

http://www.ngs.noaa.gov/PUBS_LIB/inverse.pdf

[11] P.rajarajeswari , allam apparao , Normalized distance matrix method for Construction ofphylogenetic trees using new Compressor - dnabit compress, ] Journal of Advanced BioinformaticsApplications and Research ISSN 0976-2604Vol 2, Issue 1, 2011, pp 89-97 http://www.bipublication.com.
http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Technology-Vision-2013.pdf,pp22http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Technology-Vision-2013.pdf,pp22http://steconomiceuoradea.ro/anale/volume/2008/v4-management-marketing/275.pdfhttp://www.ngs.noaa.gov/PUBS_LIB/inverse.pdfhttp://www.bipublication.com/http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Technology-Vision-2013.pdf,pp22http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Technology-Vision-2013.pdf,pp22http://steconomiceuoradea.ro/anale/volume/2008/v4-management-marketing/275.pdfhttp://www.ngs.noaa.gov/PUBS_LIB/inverse.pdfhttp://www.bipublication.com/