Cat slides

THE HIVEBernard Goh

Andy Tanzil

Joash YeoSeng Keong

Jin Hao

WHATSTHE

HIVE?

THE HIVE, IN-BRIEF

12

3

Hostel that provides LOW-COST accommodation to backpackers and travelers around the world

Total of 15 rooms

Best accommodation, Lowest prices

WHAT’STHEPROBLEM?

PROBLEMS1. Inventory Wastage

2. Inefficient Operational System

3. Unknown Occupancy

4. Unknown Optimal Bed Pricing

HOWARE SOLVE THESE

WE GONNA

PROBLEMS?

OUR PROLIFIC USEOF BEESWAS AIMED AT DRIVING HOMEFOUR CRUCIAL SOLUTIONS...

#1.THE HIVEREALLY NEEDS

REORDER SYSTEM

Using an Inventory Planning System,we minimise wastage, reduce stockouts.

0% FOOD WASTAGEWith an Inventory Planning System, we minimise wastage, reduce stockouts.

Level of Inventory that triggers an order for additional stock

reorder pointsorder quantity, Q

lead time time

Safety Stock

Reorder Point Model

11

Data Collection

11

Data CollectionBreakfast

consumption data for 2012

Calculate mean demand and

standard deviationGenerate Reorder

Point

Reorder System MODEL


AUTOMATING CHECK INS

Problem

Check-in Date Name Amount Paid (S$)

Mon Ah Kow 45

Tues Barnabas 45

Wed Ma Lian 90

Thurs Maki - San 135Fri 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

Check-in Date

Name Nights Amount Paid (S$)

Mode of Payment

Payment Settled?

Mon Ah Kow 1 45 Cash YTues Barnabas 1 45 Master Y

Wed Ma Lian 2 90 Visa NThur

sMaki - San 3 135 Cash N

Fri Elon 2 90 Check Y

SolutionA spreadsheet that correctly captures the payment made by every guest in real-time


How does our AUTOMATION work?

Guest Registration Landing Page

Walk-ins and RegistrationsPayment Look-up

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

Data Processing: Guests

Excel Visual Basic for Applications (VBA)

UserForm Input Defined Lists

Data Entry Macros

RESULTAn faster, smoother and indispensable assistant in raw data collection and monitoring of guest payments.


OCCUPANCYFORECAST

Uncertainty of occupancy rates

Unable to do necessary adjustments

Method

Occupancy Forecast

Exponential Smoothing with Linear Trend

Occupancy Forecast

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,...;

Occupancy Forecast MODEL


OPTIMALPRICING

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

1234

Optimal Room Pricing

Hostel under-going expansion in converting all rooms to dorms

Wants a price that maximises price and capacity

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

3How do we find the OPTIMAL PRICE?

Relationship between price and occupancy

Cost data associated with occupancy changes


Estimating Demand:

Historical Panel Data on price and

occupancyPerform Linear

Regression

Tease out relationship

between price and occupancy


Estimating Demand:

Multi-Variable Regression

Priceit = β0 + βoccOccit + βfebFebi + βmarMari + βaprApri + βmayMayi

+βjunJuni +βjulJuli + βaugAugi + βsepSepi + βoctOcti + βnovNovi +

βdecDeci + βincincit + βprice2 Price2

it + uit

Regression Model:

OBSERVATIONS

• t-stat for βprice2 & βinc insignificant at 5%.

–Drop Incit & Price2it from the model

–Linear demand curve is justified

60

Priceit = β0 + βoccOccit + βfebFebi + βmarMari + βaprApri + βmayMayi

+βjunJuni +βjulJuli + βaugAugi + βsepSepi + βoctOcti + βnovNovi +

βdecDeci + βincincit + βprice2 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

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

• 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

• 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

Setting up the model: Cost

Input:• Cost:

• Breakfast items• Utilities

• Estimates:• Useful life• Disposal value

• Month• GST• Service Charge

Intermediate OutputsVariable Cost per headFixed CostLevy per head

Input:• Demand function• Room rate• Month

Constraints:• Max occupancy

Intermediate Outputs:Total RevenueOccupancy

Setting up the model: Revenue

Final Output:Profits

Intermediate Outputs:Total RevenueVariable Cost per guestFixed CostTaxes

Setting up the model: Profits

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

Model Flexibility

• Expansion plans–Constraint value–Input more fixed cost items purchased

• Inflation/ Change of suppliers–Breakfast cost items

Model Flexibility

• Monthly analysis–Month input from drop-down list

• Change in policies/seasonal levies–GST input–Service Charge input

Model Flexibility

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.

ANYWAY,THAT’S PRETTY MUCHALL WE HAVE TO SAYABOUT OUR MODEL.

BUT WE WOULD REALLY LOVETO HEAR WHATYOU*HAVE TO SAY ABOUT IT.

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

• 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

Further Assumptions

• Manager to run model only at the start of every month• Other independent costs are excluded

from the analysis–Requested by owner

Model Limi ta t ions

Reorder PointThe 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.

Cat slides

Technology

Transcript of Cat slides