OPIM 3104 exam 2 set 1

forecasting in the service sector

-presents unusual challenges -special need for short term records -needs differ greatly as function of industry and product -holidays and other calendar events -unusual events

pattern of arrivals

scheduled or random, often a poisson distribution

techniques for improving service productivity (8)

separation, self-service, postponement, focus, modules, automation, scheduling, training

seasonal

-regular pattern of increasing and decreasing fluctuations -due to weather, customers, etc -occurs within a single year -data pattern that repeats itself after a period of days, weeks, months or quarters

long range

-3+ years -new product planning, facility location, research and development -typically needed for strategic decision making -deal with more comprehensive issues

random

-aka error -erratic, unsystematic, "residual" fluctuations -due to random variation or unforeseen events -short duration and non repeating

naive approach

-assumes demand in the next period is the same as the demand in the most recent period -sometimes costs efficient and effective -can be a good starting point, but not used by itself in general ex: Jan sales=68 therefore Feb sales will =68

service factory and service shop

-automation of standardized services -restricted offerings -low labor intensity responds well to process technology and scheduling -tight control required to maintain standards

service time distribution

-constant service time -random service times, usually a negative exponential distribution

quantitative forecasts

-employ mathematical modeling to forecast demand -used when situation is "stable" and historical data exists -existing products -current technology -employs mathematical techniques ex: forecasting sales of color TVs

high contact= "pure service"

-entertainment centers, health centers, hotels, public transportation, retail establishments, schools, personal services, jails

the realities of forecasting

-forecasts are rarely perfect, unpredictable outside factors may impact the forecast -most techniques assume an underlying stability in the system -product family and aggregated forecasts are more accurate than individual product forecasts

qualitative forecasts

-forecasts that incorporate such factors as the decision maker's intuition, emotions, personal experiences and value system -used when situation is vague and little data exists -new products -new technology -involved intuition and experience ex: forecasting sales on internet

exponential smoothing

-form of WMA -weights decrease exponentially -most recent data is weighted most -requires smoothing constant (ranges 0-1 and is subjectively chosen) -involves little record keeping of past data

documents for service

-high levels of customer interaction necessitates different documentatoin -often explicit job instructions -scripts and storyboards - "service blueprint"

human resources management

-hiring, training, laying off workers all depend on anticipated demand -quick hires lead to less training which leads to decreases quality of the workforce

potential problems with moving averages

-increasing n smooths the forecast but makes it less sensitive to changes -does not forecast trends well -requires extensive historical data

mass service and professional service

-labor involvement is high -focus on human resources -selection and training highly important -personalized services

waiting line characteristics

-limited or unlimited queue space -queue discipline- FIFO is most common -other priority rules may be used in special circumstances

tracking signals

-measures how well the forecast is predicting actual values -ratio of cumulative forecast errors to mean absolute deviation -a good tracking signal has low values - if a forecast is continually high or low, the forecast has a bias error

delphi method

-panel of experts, queried iteratively -iterative group process, continues until consensus is reached 3 types of participants: decision makers, staff and respondents -uses a group process that allows experts to make forecasts

special considerations for service process design

-perception of service quality is idiosyncratic and situation dependent -physical good quality does not equal good service quality -the necessary interactions with customers often affect performance adversely -the better these interactions are accommodated in the process design, the more efficient and effective the process will be -the objective of system design is to find the right combination of cost and customer interaction

trend

-persistent, overall upward or downward pattern -changes due to population, technology, age, culture,etc

the service facility

design, statistical distribution of service times

all 3 regions have similar operating issues but the appropraite way of handling the issues differs across regions- service operations exist only within the areas of ___________&_____________

direct& surrogate interaction

singel server, multiphase ex

dual window drive through at McDonalds

trend projection

fitting a trend line to historical data points to project into medium to long range -linear trends can be found using the least squares technique

forecasting

the art and science of predicting future events -the underlying basis of all business decisions

impact of different smoothing models

- when it =0, forecast never changes (extreme stability_ -when it =1, forecast is last observed demand, (naive forecast, extreme responsiveness) -choose low values of a smoothing constant when underlying average is stable -choose high values of a smoothing constant underlying average is likely to change

multiple server, single phase system ex

bank and post office service windows

capacity

capacity shortages can result in undependable delivery, loss of customers, loss of market share

single server, single phase system ex

family dentist's office

medium range

-1-3 years -sales& production planning, budget -usually used in tactical/ managerial decision making context -deal with more comprehensive issues

medium contact = "mixed service"

-"branch" offices of financial institutions, government, computer firms, law firms, ad agencies, real estate firms -park service, police and fire department, moving companies, repair shops

low contact= "quasimanufacturing"

-"home" offices of financial institutions, government, law firms, ad agencies, real estate firms, etc. -whole establishments, postal service, mail order services, news syndicates, research labs

cyclical

-repeating increasing and decreasing movements -affected by business cycle, political and economic factors -multiple years duration -not observable over the short term -often causal or associative relationships

self-service

-self service so customers examine, compare and evaluate at their own pace ex: supermarkets, internet ordering

automation

-seperating services that may lend themselves to some type of automation ex: automatic teller machines

adding service efficiency

-service productivity is notoriously low partially because of customer involvement in the design or delivery of the service or both -complicates product design -limits the options (improves efficiency and ability to meet customer expectations) -delay customizatoin -modularization (eases customization of a service) -automation (decreasing cost and increasing customer service) -moment of truth (critical moments between the customer and the organization that determine customer satisfaction)

forecasting time horizons

-short range -medium range -long range

queueing system configuration

-single server system vs. multiple server system -single phase system vs. multiphase system

seperation

-structuring service so customers must go where the service is offered ex: bank customers go to the manager to open a new account

short range

-up to 1 year, generally less than 3 months -purchasing, job scheduling, workforce levels, job assignments, production levels -usually used in tactical/managerial decision making context -usually more accurate -employees different methodologies

time series forecasting

-uses a series of past data points to make a forecast -set of evenly places numerical data -obtained by observing response variable at regular time periods -forecast based only on past values, no other important variables -assumes that factors influencing past and present will continue influence in the future

moving averages

-uses an average of the most recent period of data to forecasts the next period -simple moving average is a series of arithmetic means -used if little to no trend -used often for smoothing -provides overall impressions of data over time weighted moving average (WMA)- used when some trend might be present -older data usually less important -weights based on experience and intution

behavior of arrivals

-wait in the queue and don't switch lines -no balking, jockeying or reneging

smoothing constant is generally

.05-.50

7 steps in forecasting

1. Determine the use of the forecast 2. Select the items to be forecasted 3. Determine the time horizon of the forecast 4. Select the forecasting model(s) 5. Gather the data needed to make the forecast 6. Make the forecast 7. Validate and implement results

3 parts of a waiting line

1. arrival characteristics 2. waiting line characteristics 3. service characteristics

procedure for forecasting with seasonal data(4 steps)

1. compute seasonal indices 2. de- seasonalize the data 3.compute the de-seasonalized forecasts using moving averages, exponential smoothing or trend projection (As appropriate) 4. apply the seasonal indices to the de-seasonalized forecasts

PCN analysis 3 regions

1. direct interaction region 2. surrogate (substitute) interaction region 3. independent processing region

types of service businesses (2)

1. facility based (banks, hospitals, etc) 2. field-based (repair, cleaning services)

when given data with seasonality (4 steps)

1. label quarters chronologically 2. apply trend projection to the de-seasonalized data 3. forecast de-seasonalized demands 4. apply seasonal indices

2 types of forecasting approaches

1. qualitative forecasts 2.quantitative forecasts

service characteristics (2)

1. queueing system configuration 2. service time distribution

arrival characteristics (3)

1. size of arrival population 2. pattern of arrivals 3. behavior of arrivals -statistical distribution of arrivals

least square requirements

1. we always plot the data to ensure a linear relationship 2. we do not predict time periods far beyond the database 3. deviations around the least squares line are assumed to be random

characteristics of waiting line systems (3)

1.arrivals or inputs to the system 2.queue discipline, or the waiting line itself 3.the service facility

coefficient of correlation (r)

measures degree of association -ranges from -1 to 1

arrivals or inputs to the system

Population size, behavior, statistical distribution

quality management

accurately forecasting customer demand is a key to providing good quality service

forecast error

actual demand-forecast value

market survey

ask the customer -ask customers about purchasing plans -useful for demand and product design and planning -what consumers say and what they actually do may be different -may be overly optimistic

the forecast is the only estimate of demand until

demand becomes known

training

clarifying the service options; explaining how to avoid problems ex: investment counseler, funeral directors, after sale maintenance personnel

cycle of bad service

customer dissatisfaction--> increase customer turnover--> decrease profit margins--> employee dissatisfactions--> increase employee turnover--> customer dissatisfaction

cycle of good service

customer satisfaction--> lower customer turnover--> increase profit margins--> employee satisfaction--> lower employee turnover ---> customer satisfaction

potponement

customizing at delivery ex: customizing vans at delivery rather than at production

services are classified according to the

degree of customer contact with the technical core

types of forecasts (3)

economic forecasts technological forecasts demand forecasts

sales force composite

estimates from individual salespersons are reviewed for reasonableness, then aggregated -each salesperson estimates his/her sales -combined at district and national levels -sales reps know customer wants -may be overly optimistic

process chain network (PCN) analysis

focuses on the ways in which processes can be designed to optimize interaction between firms and their customers

supply-chain management

good supplier relations advantages in product innovation cost and speed to market

correlation

how strong is the linear relationship between the variables?

coefficient of determination (r^2)

measures the percent change in y predicted by the change in x -ranges from 0 to 1 -easy to interpret

adaptive smoothing

it's possible to use the computer to continually monitor forecast error and adjust the values of the a and B coefficients used in exponential smoothing to continually minimize forecast error

4 types of qualitative

jury of executive opinion sales force composite market survey delphi method

as the smoothing constant increases, older values become

less significant

queue discipline or the waiting line itself

limited or unlimited in length, behavior of people or items in it

technological forecasts

long term forecasts concerned with the rates of technological progress -predict rate of technological progress -impacts development of new products

modules

modular selection of service; modular production ex: investment and insurance selection; prepackaged food modules in restaurants

quantitative approaches (5)

naive approach --> time series model moving averages --> time series model exponential smoothing --> time series model trend projection --> time series model linear regression--> associative/causal model

economic forecasts

planning indicators that are valuable in helping organizations prepare medium to long range forecasts -address business cycle: inflation rate, money supply, housing starts, etc

jury of executive opinion

pools opinions of small group of high level experts to form a group estimate of demand, sometimes augmented by statistical models -combines managerial experience with statistical models -relatively quick "group think" disadvantage

PCN analysis provides insight to aid in

positioning and designing processes that can achieve strategic objectives

scheduling

precise personnel scheduling ex: scheduling ticket counter personnel at 15 min intervals at airlines

surrogate (substitute) interaction region includes

process steps in which one participant is acting on another participant's resources

direct interaction region includes

process steps that involve interaction between participants

demand forecasts

projections of a company's sales for each time period in the planning horizon -predict sales of existing products and services

focus

restricting the offerings ex: limited menu at restaurants

service design

service typically induces direct interaction with customer

independent processing region includes

steps in which the supplier and/ or the customer is acting on resources where each has maximum control

customer interaction is a ___________choice

strategic

strategic planning

successful strategic planning requires accurate forecasts of future products and market

seasonal variations in data

the multiplicative seasonal model can adjust trend data for seasonal variations in demand

least square method minimizes the sum of

the squared errors (Deviations)

queueing theory

the study of waiting lines -waiting lines are common situations -useful in both manufacturing and service areas

the objective when selecting a smoothing constant

to obtain the most accurate forecast no matter the technique *do this by selecting the model that gives us the lowest forecast error according to one of these preferred measures- * 1.mean absolute deviation 2. mean squared error 3. mean absolute percentage error

Time Series Components

trend, cyclical, seasonal, random

size of arrival population

unlimited (infinite) or limited (finite)