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The document describes models created to help optimize operations at a hostel called The Hive. Four key problems were addressed: 1) inventory wastage using a reorder system, 2) inefficient check-ins through automation, 3) unknown occupancy via forecasting, and 4) unknown optimal pricing. Excel models were developed for inventory planning, automating guest check-ins, occupancy forecasting using exponential smoothing, and determining optimal pricing through constrained optimization. The models provide solutions to operational issues but have limitations such as assumptions of constant variables and uncertainty in forecasts.
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- 1. THE HIVE Bernard Goh Andy Tanzil Joash Yeo Seng Keong Jin Hao
- 4. THE HIVE, IN-BRIEF 1 2 3 Hostel that provides LOW-COST accommodation to backpackers and travelers around the world Total of 15 rooms Best accommodation, Lowest prices
- 7. PROBLEMS 1. Inventory Wastage 2. Inefficient Operational System 3. Unknown Occupancy 4. Unknown Optimal Bed Pricing
- 10. OUR PROLIFIC USE OF BEES WAS AIMED AT DRIVING HOME FOUR CRUCIAL SOLUTIONS...
- 12. Using an Inventory Planning System, we minimise wastage, reduce stockouts. 0% FOOD WASTAGE With an Inventory Planning System, we minimise wastage, reduce stockouts. Level of Inventory that triggers an order for additional stock
- 13. reorder points order quantity, Q lead time time Safety Stock Reorder Point Model
- 15. 11 Data Collection Breakfast consumption data for 2012 Calculate mean demand and standard deviation Generate Reorder Point
- 19. Problem Check-in Date Name Amount Paid (S$) Mon Ah Kow 45 Tues Barnabas 45 Wed Ma Lian 90 Thurs Maki - San 135 Fri Elon 90 $135, Receipt to Ms Chick $45, paid on Wed $90, billed on Sat Manager scours through daily records to tally with cash/check records Automating Guest Check-ins
- 20. Check- in Date Name Nights Amount Paid (S$) Mode of Payment Payment Settled? Mon Ah Kow 1 45 Cash Y Tues Barnabas 1 45 Master Y Wed Ma Lian 2 90 Visa N Thur s Maki - San 3 135 Cash N Fri Elon 2 90 Check Y Solution A spreadsheet that correctly captures the payment made by every guest in real-time Automating Guest Check-ins
- 22. How does our AUTOMATION work? Guest Registration Landing Page Walk-ins and RegistrationsPayment Look-up
- 23. Data Required: Guests Personal details • ID & Name • Nationality • Passport # / Expiry Date Payment Details • Total Amount • Payment Mode • Remarks(Card No., Special Details) Stay details • Booking Type: Single/Multiple • IF Multiple: # of Beds • # of Nights • Room Rate • Check in/out dates
- 24. Data Processing: Guests Excel Visual Basic for Applications (VBA) UserForm Input Defined Lists Data Entry Macros
- 25. RESULT An faster, smoother and indispensable assistant in raw data collection and monitoring of guest payments.
- 27. Uncertainty of occupancy rates Unable to do necessary adjustments
- 29. Exponential Smoothing with Linear Trend Occupancy Forecast
- 30. Using historical data, we construct a trend analysis. Exponential Smoothing With Linear Trend F(t) = αA(t)+(1−α)[F(t−1)+T(t−1)]; T(t) = β[F(t)−F(t−1)]+(1−β)T(t−1); f(t+τ) = F(t)+τT(t), τ =1,2,...;
- 33. Optimal Room Pricing No framework in organizing prices!!! Reliance on intuition and gut feel Might be under-charge during peak seasons Afraid to raise prices excessively 1 2 3 4
- 34. Optimal Room Pricing Hostel under-going expansion in converting all rooms to dorms Wants a price that maximises price and capacity
- 35. Optimal Room Pricing MODEL –Rela&onship between price and occupancy –Cost data associated with occupancy changes Pricing framework that optimizes profits Maximizing total revenue relative to total cost • Need to es&mate
- 36. 3 How do we find the OPTIMAL PRICE? Relationship between price and occupancy Cost data associated with occupancy changes
- 37. Optimal Room Pricing MODEL Estimating Demand:
- 38. Historical Panel Data on price and occupancy Perform Linear Regression Tease out relationship between price and occupancy Optimal Room Pricing MODEL Estimating Demand:
- 39. Multi-Variable Regression Priceit = β0 + βoccOccit + βfebFebi + βmarMari + βaprApri + βmayMayi +βjunJuni +βjulJuli + βaugAugi + βsepSepi + βoctOcti + βnovNovi + βdecDeci + βincincit + βprice 2 Price2 it + uit Regression Model: OBSERVATIONS • t-stat for βprice 2 & βinc insignificant at 5%. –Drop Incit & Price2 it from the model –Linear demand curve is justified 60
- 40. Priceit = β0 + βoccOccit + βfebFebi + βmarMari + βaprApri + βmayMayi +βjunJuni +βjulJuli + βaugAugi + βsepSepi + βoctOcti + βnovNovi + βdecDeci + βincincit + βprice 2 Price2 it + uit • F-stat for month variables are significant at 1% – Cannot drop Febi … Deci from the model – Able to observe monthly demand curve Multi-Variable Regression Regression Model: OBSERVATIONS 60
- 41. Data: Variable Cost • Breakfast • Need to es&mate marginal cost pertaining to breakfast per addi&onal consumer • Issue: –Backpackers’ appe&te vary –Hence consump&on paCerns vary from month to month
- 42. • Monthly breakfast data in 2012 • Proxy for future monthly breakfast consump&on paCerns Data: Variable Cost • Constant Unit Cost (per guest) –Laundry • Assump&on: –Same contractor will be engaged for the foreseeable future
- 43. • Cost factors that do not vary with occupancy –Bed frames –Pain&ng –Ligh&ng –Air-‐condi&oning • Assump&on for monthly fixed cost –Straight line deprecia&on Data: Fixed Cost
- 44. Setting up the model: Cost Input: • Cost: • Breakfast items • Utilities • Estimates: • Useful life • Disposal value • Month • GST • Service Charge Intermediate Outputs Variable Cost per head Fixed Cost Levy per head
- 45. Input: • Demand function • Room rate • Month Constraints: • Max occupancy Intermediate Outputs: Total Revenue Occupancy Setting up the model: Revenue
- 46. Final Output: Profits Intermediate Outputs: Total Revenue Variable Cost per guest Fixed Cost Taxes Setting up the model: Profits
- 47. Constrained Optimization • Use of solver to find: –A room rate that maximizes total profits –Subjected to maximum occupancy constraints • Performance variables: –Profit –Room rate • Consequence Variables: –Total revenue –Total Cost –Occupancy
- 49. • Expansion plans –Constraint value –Input more fixed cost items purchased • Inflation/ Change of suppliers –Breakfast cost items Model Flexibility
- 50. • Monthly analysis –Month input from drop-down list • Change in policies/seasonal levies –GST input –Service Charge input Model Flexibility
- 51. Lack of industrial data & knowledge. Difficulty in conceptualising relevant variables and integrating models. LEARNING JOURNEY In the end, we learn how powerful and beneficial a simple program like Excel is.
- 52. ANYWAY, THAT’S PRETTY MUCH ALL WE HAVE TO SAY ABOUT OUR MODEL.
- 53. BUT WE WOULD REALLY LOVE TO HEAR WHAT YOU* HAVE TO SAY ABOUT IT.
- 55. Assumptions • 1. Lead time is constant • 2. Inventory carrying cost per unit of item do not vary • 3. Monthly consumption patterns should be similar to previous years • 4. Variability of consumption each month is similar
- 56. • Hostel relies heavily on a high no. of regulars –Come at specific time periods of the year –Same backpacker’s appetite do not change • Future breakfast consumption will follow similar patterns • Reasonable assumption • Most reliable estimate Assumptions
- 57. Further Assumptions • Manager to run model only at the start of every month • Other independent costs are excluded from the analysis –Requested by owner
- 58. Model Limitations Reorder Point The reorder point model can only be used in cases where ordering costs, lead time and demand are constant. Occupancy Forecast Despite using Exponential Smoothing to forecast the trend of the occupancy rates, there is always uncertainty involved in predicting occupancy rates. There are many other factors involved such as economic conditions of the tourism market, affluency rates and presence of competition. Hence, the model is used only for estimation purposes and should be treated as such. Optimal Bed Pricing This model can only be run at the beginning of the month. It will not export accurate results if it were to be run at any other point during the month. Visual Basic for Applications (Automating Guest Check Ins) The limitations of Excel as a database management system (DBMS) are quite apparent in this project. While it is effective in handling raw entry and tabulation of guest data, it is inflexible in allowing the user to edit information that has already been entered. This modification anomaly commonly present in many DBMS cannot be resolved by the Excel model alone. Also, as the system does not support client-side validation for reservations, erroneous entries made by the staff might be picked up, leading to inaccuracy. In the long run, the lack of accurate data input or consistent updates may lead to compounding inaccurate trends. Looking forward, as the Hive expands, it may consider more complex DBMS solutions such as SQL and Oracle to give the owner of Hive greater flexibility in managing the hostel’s guest data.