Focus forecasting bmb

Focus Forecasting

1. Romi Chordia B091062. Anshat Singhal B090703. Abhishek Dassani B090634. Nidhi Tenguria B091225. Vidisha Vijay B091186. Mohit Jalan B09092

Forecasting-Process

Forecasts are estimates of timing and magnitude of the occurrence of future events

Key functions:• An estimation tool• A way of addressing the complex and uncertain environment surrounding business decision making.

• A tool for predicting events related to operations, planning and control.

• A vital perquisite for the planning process in organizations.

Why do we forecast…

• Dynamic and complex environment• Short term fluctuations in production• Better materials management• Rationalize man-power decisions• Basis for planning and scheduling• Strategic decisions

Focus-Forecasting-Introduction

• Bernie Smith- Servistar division of TruValue • Two Principle theory1.All complex forecasting models are not always better than simpler ones.2.No single technique for products and services

• Simple techniques that work on past data also helps in developing forecasts about future as well

• Reasonable approach for short term (period less than a year)

Methodology of Focus Forecasting Various rules to project data for future forecast

Two components are Several simple simulation Rules Computer Simulation of past data

Some examples of Forecasting Rules are as follows: Whatever is sold in last 6 months is what will be the sales in

near future

Current year’s quarter wise sales can be predicted by last year’s quarter wise pattern

Next three months sales will be 10 percent more than last three months sales

Percentage change in sale levels will be same for all the quarters this year

Cases

For non-seasonal items and spare parts with weak and irregular demand

• The sales for the item in the next quarter will be the same as the actual sales for the last quarter

• F = Forecast for the item over the next quarter

• Q1 = Actual Sales over the most recent 3 months° (example: 100)• °the first quarter in the past counting backwards from now

• F = Q1 ; F = 100 for the next quarter

• This formula looks modest but is nonetheless surprisingly robust for all types of non-seasonal items, whether their demand be strong or weak

Contd..For items with an irregular demand history Sales for the item in the next quarter will be half of actual sales

over the last 6 months

F = Forecast for the item over the next quarterQ1 = Actual Sales over the most recent 3 months° (example : 100)°the first quarter in the past counting backwards from now

Q2 = Actual Sales over the 3 months before that ° (example : 150 )° the second quarter in the past counting backwards from now

( Q1 + Q2/2)=(100 + 150)/2 = 125 for the next quarter

This formula generates a reasonable forecast despite a demand history needing to be corrected

Contd.. For items with modest seasonality

Sales for the item in the next quarter will follow the percentage increase or decrease with respect to last year.

F = Forecast for the item for the next quarter.Q4 = Actual Sales of the next 3 months for last year° (example: 100)° The fourth quarter in the past counting backwards from todayQ1 = Actual Sales over the most recent 3 months° (example : 100)°the first quarter in the past counting backwards from nowQ5 = Actual Sales of the corresponding quarter last year° (example : 150)° The fifth quarter in the pas counting backwards from now F = Q4 x Q1/Q5; 100 x 100/150 =100*.67= 67 for the next quarter This formula is useful for following the market for many items, as long as

their seasonality is moderate and the corresponding quarter of last year was not zero

Advantages

" A Complex answer is just used to hide the fact that the answer has not yet been found - complex answers don’t work".

B. Smith

Simple approach and easy to understand Helps people in forecasting seasonality, trends, items with sporadic history and other demand conditionsSelects the best option which results in the least error out of varied forecasting modelsIt lays emphasis on simulation rather than the optimizationIt can even include methods like exponential smoothing, if desiredIt can regenerate forecast values without impairing the performance of hardware.

Limitations

Small sample size. Only three monthly forecasts.

Ad hoc system with no theoretical basis to aid analysis or understanding.

Impossible to compute confidence intervals, regions of stability for the forecasts or other standard analytical tools

No way to predict how Focus Forecasting should perform compared to any other forecasting system.

Currently offers two products-– Master Scheduling/MRP: offers single-organization, unconstrained planning of material and resources

– Advanced Supply Chain Planning: offers multi-organization planning and the option of constraint-based and optimized plans.

Examples of companies using focus forecasting

Oracle E-Business Suite Manufacturing and Supply Chain Management

Contd…

• process involves the recognition of demand • netting of those requirements against available and scheduled quantities

• generation of recommendations to meet those requirements

• proceeds top-down through the bill of material

Contd..• Considers alternate forecast scenarios, and so the forecasts are stated in a Master Demand Schedule (MDS)

• determine how much demand satisfied from existing stock or existing orders

• Oracle Demand Planning generates forecast data. Oracle Inventory and Master Scheduling/MRP provide basic methods to generate forecasts from historical data

• one forecast typically contains multiple items; each item has multiple entries. For ease of use and to control forecast consumption, forecasts are grouped into forecast sets and forecasts and forecasts sets are identified by unique names

Generating Forecasts from Historical Information

• Statistical Forecasts: can span multiple periods and can recognize trend and seasonality.

• Focus Forecasts: examines five different forecast models against past history, determines the model that would best have predicted the history, and uses that model to generate a forecast for the current period

Generating Forecasts

• defining a forecast rule • forecast method (statistical or focus), and the sources of demand

• in the Generate Forecast window– Name of the forecast you want to populate. – Forecast rule– Selection criteria to identify the items – An overwrite option– Start date and cutoff date

• Post this Oracle uses Open Forecast interface and forecast entries API to integrate with other systems

Other companies

• WorldPac• Carquest Auto parts• Digi-Key• guestsupply.com• usballoon.com

THANK YOU