Download to read offline
FORECASTING Quantitative Method Topic 2.pptx
- 1.
- 2. Fore cast ing “Predictionis very difficult, especially if it is about the future” - N il s B oh r -
- 3. What is Forecasting? ☻Is a tool used for predicting future demand based on historical data ☻ Educated Guessing ☻ Underlying basis of all business decisions P ro duc tio n Inv ento ry P erso n nel F a c i l iti es
- 4. What is forecastingall about? Demand for LEYECO IV Time Jan Fe b Mar Apr May Jun Jul Aug Actual demand (past sales) Predicted demand We try to predict the future by looking back at the past Predicted demand looking back six months
- 5. Forecasting History ☻ Cicero’s– “De Divinatione” » bo o k w r itten i n 45 BC dur in g a n enf o rc e a bs en c e o f p ol i tic s. » Di vi n a tio n - “ th e pow er t o s ee, un d ers ta nd a nd ex p l a in p remo n ito ry si gn s gi ven to men by the go ds . ” » Tec hn iq ues : A rti fic i a l a nd Na tu ra l Di vi na t io n » A rti fic i a l d iv i na ti o n ba s ed o n o b serv a tio n a n d o pen to a n y on e c o m peten tly tra i n ed ; w h il e » N a tura l d iv i na ti o n is b a sed o n drea ms a n d rev ela ti o n by go d -p os sess ed seers.
- 6. Forecasting History cont. “in ev ery fiel d o f in j ury a g rea t leng th o f ti me emp l oy ed i n c o n tin ued o bs er va ti o n begets a n ex tra ordi n a ry f un d o f k now ledg e, w hi c h m a y be a c q ui red ev en w it ho ut the in tervent io n o r i ns pira ti o n o f th e go ds , s in c e repea ted ob serv a tio n ma kes i t c l ea r w ha t eff ec t fo l l ow s a n y gi v en c a use a nd w ha t si gn prec ed es a ny g iv en even t.” Theory-Free Forecasting “
- 7. Why is forecastingimportant? Demand for products and services is usually uncertain. Forecasting can be used for . . . ☻ Strategic planning ☻ Finance and Accounting ☻ Marketing ☻ Production & Operations
- 8. Why is forecastingimportant? (cont.) Departments depend on forecasts to formulate their plans. ☻ Accounting – Cost/Profit estimates ☻ Finance – cash flow and funding ☻ Human resources – hiring/recruitment/training ☻ Marketing – Pricing, Promotion, Strategy ☻ MIS – IT/IS systems, services ☻ Operations – Schedules, workloads ☻ Product/Service design – new products/services2
- 9. Some General Characteristicsof Forecasts ☻Forecasts are seldom perfect ☻Most techniques assume an underlying stability in the system ☻Product family and aggregated forecasts are more accurate than individual product forecast ☻Forecasts are more accurate for shorter time periods ☻Every forecast should include an error estimate
- 10. What should weconsider when looking at past demand data? ☻Trends ☻Seasonality ☻Cyclical elements ☻Autocorrelation ☻Random variation
- 11. Forecasting Time Horizons ☻Short-rangeforecast Up to 1 year, generally less than 3 months Purchasing, job scheduling, workforce levels, job assignments, production levels ☻Medium-range forecast 3 months to 3 years Sales and production planning, budgeting ☻Long-range forecast 3+ years New product planning, facility location, research and development
- 12. Distinguishing Differences ☻Medium/long rangeforecasts deal with more comprehensive issues and support management decisions regarding planning the products, plants and processes ☻Short-term forecasting usually employs different methodologies than longer-term forecasting and tend to be more accurate than longer-term forecasts
- 13. Forecasting During theLife Cycle Introduction Growth Maturity Decline Time Quantitative models - Time series analysis - Regression analysis Qualitative models - Executive judgment - Market research -Survey of sales force -Delphi method Sales
- 14. Types of Forecasts ☻EconomicForecasts » Address business cycle – inflation rate, money supply, housing starts, etc. ☻Technological Forecasts » Predict rate of technological progress » Impacts development of new products ☻Demand Forecasts » Predict sales of existing product
- 15. Seven Steps inForecasting Step 1 Determine use of the forecast Step 2 Select the time to be forecasted Step 3 Determine time horizon Step 4 Select forecasting models Step 5 Gather the data Step 6 Make the forecast “The forecast” Step 7 Implement results
- 16. Forecasting Approaches (2) ☻QualitativeMethod ☻Quantitative Method Naïve approach Moving averages Time-series Exponential smoothing Models Trend projection Linear regression Associative Model Rely on subjective opinions from one or more experts Rely on data and analytical techniques
- 17. How should wepick out forecasting model? ☻Data availability ☻Time horizon for the forecast ☻Required accuracy ☻Required resources
- 18. Common Measures ofError ☻Mean Absolute Deviation (MAD) ☻Mean Squared Error (MSE) ☻Mean Absolute Percent Error (MAPE)
- 19. Common Attributes andAssumptions Inherent in Forecasting Forecasting techniques generally assume that the same underlying causal relationship that existed in the past will continue to prevail in the future. Forecasts are rarely perfect. Therefore, for planning purposes, allowances should be made for inaccuracies.
- 20. Common Attributes andAssumptions Inherent in Forecasting Forecast accuracy decreases as the time period covered by the forecast (i.e., the time horizon) increases. Forecasts for groups of items tend to be more accurate than forecasts for individual items, because forecasting errors among items in a group tend to cancel each other out.
- 21.
- 22. Features of aQualitative Forecasting Method Uses factors that cannot be directly measured Relies less on data and much more on the opinions and experiences of the people involved in the forecasting process The estimates are made with a systems of ratings to produce figure instead of on hard (measurable and verifiable) data No verifiable data is used and mainly relies on expert human judgment Usually applied to medium-range or long-range decisions
- 23. QUALITATIVE FORECASTING Market Research / Survey SalesForce Composite Executive Judgment Delphi Method
- 24. Executive Judgment - Subjectiveviews of executives or experts from sales, production, finance, purchasing, and administration are averaged to generate a forecast about future sales ADVANTAGES Done quickly and easily without need of elaborate statistics No formal requirements in terms of historical data Timely in that the forecast is generated on the basis of the most current situation The knowledge on which the forecast is based is extremely rich
- 25. Executive Judgment DISADVANTAGES The informationon which the individual forecasts are based could differ from period to period The composition of the group might differ Creates problems inherent to those who meet in group which may raise issues on high cohesiveness, strong leadership and insulation of the group
- 26. Sales Force Composite -A sales force estimate involves obtaining the judgments from the sales-force. Each salesperson will provide a forecast for each product or product type by customer. ADVANTAGES It is simple to use and understand It uses the specialized knowledge of those closest to the action It can place responsibility for attaining the forecast in the hands of those who most affect the actual results The information can be broken down easily by territory, product, customer, or salesperson
- 27. Sales Force Composite DISADVANTAGES Salespeople’sbeing overly optimistic or pessimistic regarding their predictions and inaccuracies due to broader economic events that are largely beyond their control Salespeople often lack relevant information about a company’s plans and overall industry trends
- 28. Market Research /Survey - Customers are asked to make their own forecast about their usage and buying intentions. Customer intentions will be based on subjective judgments about future requirements. ADVANTAGES User-customer has the best information on which to base a forecast. Works best when the customers, or at least the major customers, are few in number
- 29. Market Research /Survey DISADVANTAGES Surveys with closed-ended questions may have a lower validity rate than other question types Survey question answer options could lead to unclear data because certain answer options may be interpreted differently by respondents Data errors due to question non-responses may exist. The number of respondents who choose to respond to a survey question may be different from those who chose not to respond
- 30. Delphi Method - Agroup technique in which a panel of experts is questioned individually about their perceptions of future events. The experts do not meet as a group in order to reduce the possibility that consensus is reached because of dominant personality factors. - It begins with the development of a set of open- ended questions on a specific issue. These questions are then distributed to various ‘experts’. - The responses and accompanying arguments are summarized by an outside party and returned to the experts along with further questions. This continues until a consensus is reached.
- 31.
- 32. Delphi Method ADVANTAGES Bring geographicallydispersed panel experts together gaining input with minimal personal access Useful and quite effective for long-range forecasting Anonymity and confidentiality of responses Limited time required for respondents to complete surveys Avoids direct confrontation of experts with one another ( no peer pressure, or extrinsic pressure) Structured/organized group communication process (condense experts opinions into a few precise and clearly defined statements) It is done by questionnaire format Cost effective and flexible/adaptable, fast, versatile
- 33. Delphi Method DISADVANTAGES Information comesfrom a selected group of people and may not be representative No guidelines for determining consensus, sample size and sampling techniques Requires time/participant commitment Requires skill in written communication Time delays between rounds in data collection process (multiple data collection, analysis , processing) Concerns about the reliability of the technique Drop-outs, response rates ( dependEnt upon a speedy response by busy experts) Low reliability
- 34.
- 35. Features of aQuantitative Forecasting Method Uses numerical and prior experience to predict upcoming events Appropriate to use when past numerical data is available Applicable when t is reasonable to assume that some of the patterns in the data are expected to continue into the future Usually applied to short- or intermediate-range decisions
- 36. QUANTITATIVE FORECASTING Regression Models Time Series Models Moving Average Naïve Exponential Smoothing a)Simple b) Weighted a) Level b) Trend c) Seasonality a) Drift b) Seasonal
- 37. Features of aTime Series Model Makes forecasts based solely on historical patterns in the data. The historical data is representative of the conditions expected in the future Uses time as independent variable to produce demand Measurements are taken at successive points or over successive periods
- 38. Features of aTime Series Model
- 39. Naïve Method Anestimating technique in which the last period's actual data are used as this period's forecast, without adjusting them or attempting to establish causal factors. It is used only for comparison with the forecasts generated by the better (sophisticated) techniques Drift Method - allow the forecasts to increase or decrease over time, where the amount of change over time is set to be the average change seen in the historical data Seasonal Naïve Method - forecast is set as equal to the last observed value from the same season of the year
- 40. Naïve Method FORMULA Sales ActualActual sales forecast for = sales x of this year next year of this year Actual sales of last year = P1,500,000 x P1,500,000/P1,480,000 = P1,520,270
- 41. Naïve Method ADVANTAGES The mostcost-effective forecasting model Simple to calculate Accuracy is good for short-term forecasting DISADVANTAGES Less accurate if past sales fluctuate Does not consider future relevant events which may affect decision
- 42. Types of forecastingmethods Rely on data and analytical techniques. Rely on subjective opinions from one or more experts. Qualitative methods Quantitative methods
- 43. Qualitative forecasting methods GrassRoots: deriving future demand by asking the person closest to the customer. Market Research: trying to identify customer habits; new product ideas. Panel Consensus: deriving future estimations from the synergy of a panel of experts in the area. Historical Analogy: identifying another similar market. Delphi Method: similar to the panel consensus but with concealed identities.
- 44. Quantitative forecasting methods TimeSeries: models that predict future demand based on past history trends Causal Relationship: models that use statistical techniques to establish relationships between various items and demand Simulation: models that can incorporate some randomness and non-linear effects
- 45. How should wepick our forecasting model? 1. Data availability 2. Time horizon for the forecast 3. Required accuracy 4. Required Resources
- 46. Time Series: Movingaverage • The moving average model uses the last t periods in order to predict demand in period t+1. • There can be two types of moving average models: simple moving average and weighted moving average • The moving average model assumption is that the most accurate prediction of future demand is a simple (linear) combination of past demand.
- 47. Time series: simplemoving average In the simple moving average models the forecast value is Ft+1 = At + At-1 + … + At-n n t is the current period. Ft+1 is the forecast for next period n is the forecasting horizon (how far back we look), A is the actual sales figure from each period.
- 48. Example: forecasting salesat Kroger Kroger sells (among other stuff) bottled spring water Month Bottles Jan 1,325 Feb 1,353 Mar 1,305 Apr 1,275 May 1,210 Jun 1,195 Jul ? What will the sales be for July?
- 49. What if weuse a 3-month simple moving average? FJul = AJun + AMay + AApr 3 = 1,227 What if we use a 5-month simple moving average? FJul = AJun + AMay + AApr + AMar + AFeb 5 = 1,268
- 50. What do weobserve? 1000 1050 1100 1150 1200 1250 1300 1350 1400 0 1 2 3 4 5 6 7 8 3-month MA forecast 5-month MA forecast 5-month average smoothes data more; 3-month average more responsive
- 51. Stability versus responsivenessin moving averages 500 550 600 650 700 750 800 850 900 950 1 2 3 4 5 6 7 8 9 10 11 12 D e m a n d Week Demand 3-Week 6-Week
- 52. Time series: weightedmoving average We may want to give more importance to some of the data… Ft+1 = wt At + wt-1 At-1 + … + wt-n At-n wt + wt-1 + … + wt-n = 1 t is the current period. Ft+1 is the forecast for next period n is the forecasting horizon (how far back we look), A is the actual sales figure from each period. w is the importance (weight) we give to each period
- 53. Why do weneed the WMA models? Because of the ability to give more importance to what happened recently, without losing the impact of the past. Demand for Mercedes E-class Time Jan Feb Mar Apr May Jun Jul Aug Actual demand (past sales) Prediction when using 6-month SMA Prediction when using 6-months WMA For a 6-month SMA, attributing equal weights to all past data we miss the downward trend
- 54. Example: Kroger salesof bottled water Month Bottles Jan 1,325 Feb 1,353 Mar 1,305 Apr 1,275 May 1,210 Jun 1,195 Jul ? What will be the sales for July?
- 55. 6-month simple movingaverage… In other words, because we used equal weights, a slight downward trend that actually exists is not observed… FJul = AJun + AMay + AApr + AMar + AFeb + AJan 6 = 1,277
- 56. What if weuse a weighted moving average? Make the weights for the last three months more than the first three months… 6-month SMA WMA 40% / 60% WMA 30% / 70% WMA 20% / 80% July Forecast 1,277 1,267 1,257 1,247 The higher the importance we give to recent data, the more we pick up the declining trend in our forecast.
- 57. How do wechoose weights? 1. Depending on the importance that we feel past data has 2. Depending on known seasonality (weights of past data can also be zero). WMA is better than SMA because of the ability to vary the weights!
- 58. Time Series: ExponentialSmoothing (ES) Main idea: The prediction of the future depends mostly on the most recent observation, and on the error for the latest forecast. Smoothing constant alpha α Denotes the importance of the past error
- 59. Why use exponentialsmoothing? 1. Uses less storage space for data 2. Extremely accurate 3. Easy to understand 4. Little calculation complexity 5. There are simple accuracy tests
- 60. Exponential smoothing: themethod Assume that we are currently in period t. We calculated the forecast for the last period (Ft-1) and we know the actual demand last period (At-1) … ) ( 1 1 1 t t t t F A F F The smoothing constant α expresses how much our forecast will react to observed differences… If α is low: there is little reaction to differences. If α is high: there is a lot of reaction to differences.
- 61. Example: bottled waterat Kroger Month Actual Forecasted Jan 1,325 1,370 Feb 1,353 1,361 Mar 1,305 1,359 Apr 1,275 1,349 May 1,210 1,334 Jun ? 1,309 = 0.2
- 62. Example: bottled waterat Kroger = 0.8 Month Actual Forecasted Jan 1,325 1,370 Feb 1,353 1,334 Mar 1,305 1,349 Apr 1,275 1,314 May 1,210 1,283 Jun ? 1,225
Editor's Notes
- #2 Niels Henrik David Bohr – was a Danish physicist (a scientist trained to understand the interactions of matter and energy across the physical universe) who made foundational contributions to understanding atomic structure and quantum theory. Was also a philosopher and a promoter of scientific research.
- #5 the most important ancient source of techniques of prediction and control is Cicero’s De Divinatione - Latin word concerning on Divination. This book is in the form of a dialogue whose interlocutors are Cicero MARCUS TULLIUS CICERO (Greek, Roman Philosopher, political, lawyer, orator, political theorist, consul and constitutionalist. Considered as one of Rome’s greatest orators and prose stylists. (speaking mostly in book II) and his brother Quintus. Artificial Divination – based on observation and open to anyone competently trained. Subdivisions: Divination from living things, such as behavior of birds. Divination from lifeless things such as casting of lots, weather anomalies and above all astrology. Natural divination – based on dreams and revelation by god-possessed seers. ND comes from within. Those artificial diviners employ art, who having learned the known by observation, seek the unknown by deduction. On the other hand those do without art, who forecasts the future while under the influence of excitement.
- #6 The modern distinction between the theory-free and structural models repeat the ancient distinction between artificial and natural divination. The ARTIFICIAL relies on the external signs, observed 470,000 times. The NATURAL relies on the internal intuitions in the form of specifications of the structure or the other constraints of an economic theory not itself derived from the signs.
- #7 Strategic planning (long range planning) Strategic planning is an organization's process of defining its strategy, or direction, and making decisions on allocating its resources to pursue this strategy. It may also extend to control mechanisms for guiding the implementation of the strategy. Finance & Accounting (budgets and cost controls) Marketing (future sales, new products) Production & Operations
- #8 Departments cannot pursue their plans without forecasting. Ex. LEYECO IV’s systems loss. Forecasts % is 10%
- #9 - Forecasts more accurate for groups vs. individuals. Forecasting errors among items in a group usually have a canceling effect. Extremes in a group cancel each other Ex. I can forecast the class average from the midterm better than Mrs. X’s individual grade. - Forecast accuracy decreases as time horizon for forecasts increases Ex. I can forecast this year’s class average better than next year’s class average
- #10 Cyclical elements – cyclical influence comes from political elections, war, economic conditions or sociological pressures. Autocorrelation – the value expected at any point is highly correlated with its own past values. When demand is random, it may vary widely from one week to another. where high auto-correlation exists, demand is not expected to chance very much from one week to the next. Random Variation – caused by chance events. If we cannot identify the cause of the reminder portion of demand, it is assumed to be purely random chance.
- #13 Introduction and growth require longer forecasts than maturity and decline As product passes through life cycle, forecasts are useful in projecting Staffing levels, Inventory levels and Factory capacity Introduction – best period to increase market share. Research & Development engineering is critical. Growth – practical to change price or quality image. Strengthen niche. Maturity – Poor time to change image, price and quality. Competitive costs become critical. Defend market position. Decline – Cost control critical.
- #15 Step 1. Who needs the forecast? Decisions to be made affects the future of the organization. Step 2. Short range, medium range or long range Step 3. Qualitative Methods used when situation is vague and a little data exists. (New product or technology) it involves intuition and experience. Quantitative method is used when situation is ‘stable’ and historical data exists. It involves mathematical techniques. Step 4. collection of reliable and accurate data, relevant, consistent and timely.
- #16 Will be discussed by Andy & Jocyl
- #18 Will be discussed by Ramir
- #19 Forecasting lays a ground for reducing the risk in all decision making because many of the decisions need to be made under uncertainty. In business applications, forecasting serves as a starting point of major decisions in finance, marketing, productions, and purchasing. 1 - In other words, most of our techniques are based on historical data. 2 - For example, the company should always maintain a safety stock in anticipation of a sudden depletion of inventory.
- #20 1 - Generally speaking, a long-term forecast tends to be more inaccurate than a short-term forecast because of the greater uncertainty. 2 - For example, industry forecasting is more accurate than individual firm forecasting
- #22 Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts; they are appropriate when past data are not available. They are usually applied to intermediate- or long-range decisions.
- #24 Usually this method is used in conjunction with some quantitative method, such as trend extrapolation. The management team modifies the resulting forecast, based on their expectations. 4th advantage - It includes at least potentially all the collective knowledge and experience of the involved managers
- #25 1st - In one period the marketing manager might have just returned from a trade show or from a sales meeting and have better information than usual this, however, is that of group-think With high cohesiveness, the group becomes increasingly conforming through group pressure that helps stifle dissension and critical thought. Strong leadership fosters group pressure for unanimous opinion. Insulation of the group tends to separate the group from outside opinions, if given.
- #26 The forecast source are salespeople who have continual contacts with customers. They believe that the salespeople who are closest to the ultimate customers may have significant insights regarding the state of the future market. Forecasts based on sales force polling may be averaged to develop a future forecast. Or they may be used to modify other quantitative and/or qualitative forecasts that have been generated internally in the company.
- #27 1st - In addition, there is a greater tendency for biases to occur.
- #29 3rd - thus creating bias
- #32 1st - gaining input with minimal personal access
- #33 1st - In addition, there is a greater tendency for biases to occur.
- #36 Will be discussed by Ramir