Travel Reimbursement Cost Analysis Liam Davies, Anthony Thoel and Connor White

Transcription

1 Travel Reimbursement Cst Analysis Liam Davies, Anthny Thel and Cnnr White The Overview Kicking ff the start f the year, spring f 2017 a handful f students here at McKendree University were tasked with a prject t help the Girl Scuts f America: Suthern Illinis Chapters. The Chapters f Glen Carbn IL, and Munt Vernn IL, apprached McKendree University fr a slutin t a real wrld prblem that needed t be slved within their rganizatin. Under the Guidance f Dr. Heather Dye A Prfessr f Mathematics here at McKendree University, and with supprt frm PIC Math, the Sciety fr Industrial and Applied Mathematics, as well as the Natinal Science Fundatin ( NFS Grant DMS ), a driven grup f prblem slvers decided t take n the challenge and tackle this prblem. This wuld be a first experience fr the students t take n and prvide a slutin t a true real wrld lgistical prblem. Over a recrding time f 2 fiscal years, the Girl Scuts f Suthern IL recrded sme several thusand travel entries that varied in categry, magnitude, destinatin, as well as many ther factrs. These recrdings were lgged in an Excel Spreadsheet where each vlunteer was tasked with recrding their wn travel frm a central dcument where they wuld append their new travel event infrmatin t the end f the existing master dcument. This system was set in place fr bvius lgistical reasns that wuld be beneficial t the rganizatin fr later review, in a similar matter that any ther everyday rganizatin might als recrd different lgistical statistics abut their business, club, r rganizatin. This prject entailed examining the raw data recrded by the Girl Scuts f Suthern Illinis' tw Chapters f Glen Carbn IL, as well as Munt Vernn IL in rder t determine if the current methd f travel that the Girl Scut rganizatin had been fllwing was in fact the mst efficient methd. As ppsed t a new methd that wuld require the rganizatin t purchase, ne, r mre cars that wuld remain strictly tasked with Girl Scut Cmpany Travel, in rder t truly becme mre efficient in their travel expenses. These travel expenses stated abve cme frm the travel required between the tw rganizatins in rder t make sure vlunteers culd make meetings, summits, r ther rganizatinal events. The travel data als entailed trips where trp members, ckies, r ther supplies were being transprted t a lng list f pssible destinatins and events. These travel events required tw methds f cmpensatin. The first methd f cmpensatin was directly debiting the vlunteer wh wuld drive their persnal vehicle fr the trip, and was thus reimbursed based n mileage csts established by the rganizatin. The secnd methd f cmpensatin was directly debited t Enterprise Rental Services where a certain trip required a lnger distance f travel, that per the rganizatins established rules, warranted Enterprise Car rental. Cmbined, these expenses lgged bth separately and differently acrss the tw ffices in Glen Carbn, and Munt Vernn wuld sum t the Girl Scuts f Suthern Illinis' ttal travel expenses per year, ver the tw years in study which became ur baseline f expense histry t begin analysis n. Our gal frm the beginning f the prject was t recmmend a number f cars t the rganizatin fr purchase r lease that was nt nly affrdable, but als beneficial and efficient specifically t each member f the chapter. This garnered a need fr analysis f their travel data, in rder t extract specific statistics t shw that the existing travel plicy was in fact the mst efficient. If ur studies wuld prve therwise we wuld then recmmend a different plicy t the Girl Scuts in rder t help them save rganizatinal funds lng term.

2 Upn beginning the prject, ne thing became evidently clear frm the first glance ver f the data by all the students f the class. The data was recrded imperfectly, much like data is cmmnly recrded imperfectly in the real wrld. The grup nticed immediately that multiple data types that wuld prve later n t be extremely valuable, were cndensed int ne single clumn within the Excel dcument where srting and sifting fr said values wuld nt be pssible. This simple fact is part f ur everyday lives as future students f Data Science and we must cntinue t be flexible with the peple we wrk with in rder t give thse results n matter hw gd r bad the data might be. It is extremely imprtant tday t be flexible in ur field f wrk being that data if fueling the markets f tmrrw and that data is, and will always be recrded and shared imperfectly. With respect t this grup f students, it was extremely beneficial t nt nly take n a prblem and prvide a slutin t it, but als extremely beneficial t see exactly what the real wrld will lk like utside f schl. This prject directly exemplified the real wrld type f prblem slving teams that wuld be frmed where students f Mathematics, Cmputer Science, and Educatin wuld jin tgether t cmbine their expertise in rder t slve a larger prblem that withut each ther they might nt have been able t slve. In the fllwing sectins the prject will be brken dwn int a synpsis f exactly what steps were taken frm the beginning, being the cleansing f the raw data given, all the way thrugh ur findings specific t the Girl Scuts current plicy, as well as ur wn recmmendatin fr the rganizatins travel plicy. The Data Analysis Frm the start f the prject, it was evidently clear that due t the large amunt f data entries in the raw data, autmatin was ging t be required. The data riginally cnsisted f sme several thusand lines f manually entered travel dates t where each entry cnsisted f several clumns. These clumns varied frm Cdes representative f separate Girl Scut functins, vlunteer names, dates traveled, descriptin f travel, Date f transactin, transactin debits, as well as transactin debits and credits. This pattern was represented acrss the multiple data sheets we were given ranging frm tw separate ffices ver 2 years. These sheets while rganized t an extent had ne majr flaw typically fund in the data recrding prcess when peple are nt aware f exactly hw data is srted r manipulated. Mre specifically this flaw came frm Clumn D f each sheet having cmbined multiple separate yet srtable data values, which happened t be key imprtant values fr later use. This very typical mistake in the data

3 recrding prcess leads t hurs spent n the analysis side f sifting and cleansing the data in rder t extract srtable meaningful data. As easily nticed frm the table abve, clumn D was extremely difficult t handle, as it was a cmpilatin f multiple imprtant values. This prblem was vercme by implementing a basic sift thrugh the data where a layer f values was initially remved frm cnsideratin. As the first step f the sifting prcess, the data was srted in alphabetical rder in rder t create grups f valuable and like data. This gruping by alphabetical rder was extremely imprtant due t the fact that it separated the gd data values as determined by the grup, frm the bad. All valuable, crrectly lgged data was cleanly and easily srted in t tidy grups where ther data that was less desirable was frced naturally t either blatantly stand ut amid ther valuable entries fr future cpy and paste rganizatin, r was cnveniently srted dwn t the bttm f the sheet due t alphabetical srting algrithms built int Excel. At this pint, the data was much mre useful t us as we had clean valuable infrmatin with all f the less desirables sifted ut. Yu ll ntice Travel Date, Destinatin, and Trip Inf were handily sifted ut frm the riginal cngested D clumn exemplified in the table snippet abve. Frm this pint, the final step in the data cleanse was t present the desirable data int a clean new sheet in the mst beneficial way pssible fr analysis. After the data cleanse, the first cnsideratin we had t make, was hw d we take ur data and extract valuable infrmatin frm it. While pndering what mve t make next, glancing ver thusands f lines f excel data cntaining travel dates, vlunteer names, destinatin descriptins, as well as debits and credits. It was bvius that the first thing we needed t d was t find the Unique Travel Dates assciated with all f the travel data we had left after the cleanse. These Unique Travel Dates wuld prve later t be extremely useful t us. The gal f this sectin f the analysis was t get an understanding f hw many days f the year the Girl Scuts were paying ut reimbursements. With an understanding f hw many days ut f the year travels were ccurring, we culd already get an idea f hw necessary a prpsitin f purchasing a car, r multiple cars was ging t be. Once we had an understanding f the Unique Travel Dates, the next step was determining the Daily Lad at each ne f thse unique days. This lad, wuld give us an even further understanding f the benefits f purchasing a Girl Scut Car, r multiple cars based n the distributin f travel lads acrss thse unique days. Our findings initially were dismal. We nticed that nce srted the list f Unique Travel Dates with Daily Lads was very heavily averaging multiple trip days.

4 Upn analyzing the lads, ur first understanding f what a slutin was ging t lk like immediately changed. It was n lnger a slutin with the gal f eliminating reimbursement altgether but rather became a slutin fcused n lightening the reimbursement lad n the rganizatin by purchasing ne car, r even tw cars that ver time wuld eliminate sme travel as well as save club funds. We planned t lighten the rganizatins travel expenses by a simple cst reductin cncept utlined in the fllwing table. Based n the purchase f each new car per ffice, travel lads per unique day f the year wuld be reduced by ne trip. This will present a savings amunt per year summed up by nt having reimbursed persnal travel, r enterprise. With this savings amunt, a car t be purchased by the Girl Scuts wuld have t cst less than the amunt f savings made frm the reductin cncept in rder t save them mney n travel each year. With an Average Trip Cst Fund t be $46.50 we see that $46.50 x 16 Trips in the First Rw = $744 $46.50 x 9 Trips in the Secnd Rw = $ The difference between these values represents savings thrugh the purchase f ne car a reductin f travel cst This prcess can be repeated t shw the savings made thrugh each additinal purchase With an understanding f the Sample Data Breakdwn Listed abve, the fllwing table relates similarly as fllws. Left-mst clumn represents the trips each purchased car will take per year wherein reducing the number f reimbursement / rental trips that year by that much Use_Rent clumns represent the rental car cst reductin by each new car purchased

5 Use_Persnal clumns represent the persnal travel cst reductin by each new car purchased The right-mst clumn represents an acceptable annual cst fr each respective amunt f cars purchased in rder fund by summing the Use_Rent & Use_Persnal expenses per car purchased This reductin shws us that fr each car purchased we are reducing the Travel Csts by the listed amunts n the rightmst clumn, yielding the annual cst the girl scuts wuld be limited t spending when purchasing the car f their chice. If they wuld spend less annually n the car f their chice than seen abve accrding t ur table, this wuld represent savings. Listed belw is a cmprehensive table shwing selected car candidates fr purchase that wuld allw fr the lwest annual csts. Frm this table f Annual Csts, the Girl Scuts are t make their wn selectin as t which car better suits their needs ecnmically. Each car listed with its assciated cst per year represents a value listed by Edmunds True Cst t Own. Edmunds True cst t wn accunts fr, vehicle depreciatin, interest financing the vehicle, taxes and fees, insuring the vehicle, fuel csts, maintenance, repairs, as well as federal tax credits fr driving ecnmically efficient cars. This value determined by Edmunds True Cst t wn represents a rugh average cst f a car per year in current markets. $40,000 $35,000 $30,000 $25,000 $20,000 $15,000 $10,000 $5,000 $0 5yr price f car

6 Frm ur selected cars and their respective cst analysis we were able t put tgether a savings plan fr the Girl Scuts based n the fllwing tables. The fllwing savings plan is t be implemented at the discretin f the Girls Scuts f Suthern IL based ff f ur recmmendatins. Based n these calculatins, we can begin searching fr car ptins that fit within the necessary price ranges. The easier way f ding this is t find suitable cars, find their annual cst, and then determine hw many can be bught. Recmmended Vehicles: Chevrlets: 1. The 2017 Spark has an annual cst f 4939 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 2 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD f reimbursement csts n an annual basis, a savings f 7250 USD.

7 Frds: 2. The 2017 Cruze has an annual cst f 5932 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 1 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 4500 USD The 2017 Fiesta has an annual cst f 5691 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 2 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD f reimbursement csts n an annual basis, a savings f 5000 USD. 2. The 2017 Fcus has an annual cst f 5943 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 1 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 4500 USD Hndas: Others: 1. The 2017 Civic has an annual cst f 5882 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 1 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD f reimbursement csts n an annual basis, a savings f 4750 USD. 2. The 2017 CR-Z has an annual cst f 6075 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 1 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 4250 USD. 3. The 2017 Fit has an annual cst f 5001 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 2 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 7000 USD. 1. The 2017 Chrysler 200 has an annual cst f 6768 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 1 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD f reimbursement csts n an annual basis, a savings f 3000 USD. 2. The 2016 Ddge Dart has an annual cst f 5967 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 1 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 4500 USD. The 2017 Hyundai Accent has an annual cst f 5421 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 2 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 6000 USD. 3. The 2013 Nissan Altima has an annual cst f 5721 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 2 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 5000 USD. 4. The 2017 Kia Frte has an annual cst f 5472 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 2 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 5750 USD. 5. The 2017 Tyta Crlla has an annual cst f 5580 USD. At this cst, 1 wuld be purchased fr the Munt Vernn ffice and 2 fr Glen Carbn, at a ttal annual cst f USD. This replaces apprximately USD, an annual savings f 5500 USD.

8 As a general rule when selecting a car fr purchase, it shuld be ensured that the annual cst f the car is less than the annual expenditure n the travel this car is used fr. We suggest the fllwing plicies fr the cars in rder t benefit the mst frm ur study. Recmmended Travel Plicy Adjustments 1. Purchased vehicles must be used fr the lngest trip per day, unless therwise priritized fr strage capability r any ther alternative use. 2. Vehicles will nt sit idle unused fr mre than 30 days. 3. If multiple vehicles are purchased each vehicle will alternate lngest trips made, in attempt t keep mileage n each car within the same relative range. 4. Vehicles will be maintained accrding t manufacturer s manuals and specificatins in rder t maintain the highest quality vehicle fr the lngest pssible duratin. 5. Vehicles are t be returned with a full tank f gas which will either be charged t the girl scuts, r reimbursed t the emplyee s upn prcessing f a receipt. Recmmended Bk Keeping Adjustments 1. Vlunteer Names shuld be identical t ffice phne lists 2. Destinatin MUST be in frm City, State fllwed by if multiple destinatins are reached per trip. 3. Mileage MUST be recrded fr daily trips using built in trip dmeters standard t all vehicles, r GPS. Each Emplyee shuld recrd this reading upn parking the car at the end f the trip. 4. Dates shuld all be recrded in the same frmat 5. Take advantage f the Cmments clumn t recrd pertinent infrmatin 6. Cnsistency in the data recrding prcess is critical. Try t minimize typs, abbreviatins, and irregularities.

9 References Girl Scuts Of Suthern Illinis- Edmunds True Cst T Own- Analyst Cave Gecding-