Latest forecasting methods applied to lean

production systems

José Fragozo

Dept. Of Industrial Engineering, Universidad Del Norte

Barranquilla, COLOMBIA

Jmfragozo@uninorte.edu.co

Abstract- Nowadays technology increases in an

exponential rate that is only overcome by our

ambition of keep growing, every single day

appears a new product, that is designed taking in

account the life cycle and obsolescence time, this

happens specially with the information technology

companies (ITC) that in our days are the most

powerful giants of world`s industry in the other

hand we are using too much energy than the

planet is capable to provide in an undefined period

of time for that reason alternative energy is

earning value, this paper focuses on two new

forecasting methods, the first one help the

forecasting for parts on a technology supply chain,

and the second one help us to forecast the wind

speed in the energy industry in order to increase

the efficient of wind energy technology.

I. INTRODUCTION

Having the knowledge of the limited classic literature

that is available and useful for this kind of forecasting

it was necessary to entrepreneur in this new field

using the statistics tools that are around us, we have

heard that the universe has an equilibrium equation,

natural events can be modeled in mathematics

models, in the same way market behavior can be

modeled too, using statistical tools like Bayes

theorem and Weibull probability distribution there are

new advances in this areas we are going to review

two papers, the first one “Bayesian forecasting of

parts demand” published by Elsevier B.V where

applies Bayes theorem in the forecast of demand

of technology parts and the second one

“Development of wind speed forecasting Model

Based on the Weibull Probability Distribution”

published by Ruigang Wang, Wenyi Li and B.

Bagen where use Weibull probability distribution

models to forecast the wind speed.

II. BAYESIAN FORECASTING OF PARTS

DEMAND

Manufacturing of high technology products like

computer is an exacting business were the supply

chain as we as industrial engineers know must be

synchronize optimizing the information and materials

flow, no all the times this job is easy, in this particular

case the demand of computer parts is a very complex,

because no matter if computers is an exacting

business, computers parts business is very complex

because it depends of the life cycle of the part and the

obsolescence of the part in this case Sun

Microsystems Inc is a vendor of computer products

that it is fettered to the supply chain

In order to have an idea of the type of market

behavior that we they are dealing with it appear in the

next graphics.

Fig. 1. Demands

Where the solid lines present de demands, the

horizontal axe represent the financial planning

periods of roughly one month’s duration, we can see

how demand`s behavior depends of the life cycle and

obsolescence, short life cycles means that the

individuals parts frequently do not have sufficient

observed demand values to support reliable

extrapolation, Bayesian model take in account

predictive conditionals, life cycle curves, uncorrelated

errors, auto correlated errors, scale factors, priors

parameters, distributions of parts demand, estimation,

the Bayesian model requires an investment of

$10.000 USD due that is an heuristic algorithm of 482

forecast each one has 4000 iterations in the Gibbs

Sampler so are required 16 computers processors, this

model describes the behavior of the demand better

than classic forecast methods, its limited for this kind

of demands that depends of life cycles and

obsolescence, it has a investment but the forecasting

is a vital tool in the planning so for giants vendors

like in this case this new forecasting method is a very

good option.

III. DEVELOPMENT OF WIND SPEED

FORECASTING MODEL BASED ON

THE WEIBULL PROBABILITY

DISTRIBUTION

In a unsustainable world like our world where oil

provides us with the major percent of our energy

demands, worlds population is around 6.000.000 and

in 2050 it is forecasted that will be around 9.000.000,

we consume more energy than the energy that the

planet is able to provide, so in this point of time is

essential to look alternative ways to produce energy,

since many years ago those alternative methods exist

but are far away to be compared with the oil energy,

is too less efficient and is more expensive, so oil

energy still being the best option, taking in account

that oil is a non renewable resource, alternative

methods needs to be improved, in this paper develop

a forecasting method to forecast the wind´s speed,

wind energy is a variable energy source that, the

power output of a wind turbine generator (WTG) unit

fluctuates with the wind speed variations, existing

forecasting methods presents significant errors in the

forecast what make no reliable to analyze power

networks impact, so in this paper present an improved

probability method based on Weibull distribution,

with two parameters Weibull fit with the actual wind

speed perfectly. Although there are only two

parameters on Weibull distribution, the wind model is

very sensitive two those parameters, so if the

parameters are designed with the proper accuracy the

wind speed forecasting model can represent the actual

speed variation.

Fig. 2. Probabilities density function

This paper improves existing Weibull method

combining the mean wind speed and standard

deviation method with the maximum likehood

method, and wind speed is modeled as a random

variable with a Weibull distribution.

It also compare time series methods like AR(p) and

MA(q) with the new method that is proposed, the

accuracy of this method is significant better than the

other ones, obviously every forecast includes a

natural errors, the wind variation change the behavior

depending of annual period, season period and diurnal

period, we can see the comparison of the methods in

each period in the next table.

Chart 1. Comparison of methods

Improved method has smaller errors in each period,

what means that is describing and forecasting the

winds speed variation better than the other methods,

accurate wind speed forecasting are necessaries in the

network energy planning on a wind energy station so

this new methods are very useful in the industry.

IV. CONCLUSIONS

Forecasting methods need to be developed in the

same rate and time that the new markets behavior is

appearing, classic methods are good backgrounds

when a forecasting its necessary but are not useful in

a lot of cases where the behavior of the data is

particular of the case like in this paper cases,

knowledge is a continuos in the universe, several

times the change resistance difficult the

implementation of new methods that are better in the

majority of the cases, in this two reviews the new

forecasting methods will help in the company

evolution, will save a lot of money, will increase the

utilities, will optimize alternative energies, will help

to save the world etc.

V. ACKNOWLEDGMENTS

This paper was supported by “Universidad Del

Norte”, Ing. Daniel Romero and Ing. Carlos Paternina

that provides us with the knowledge in classic

forecasting methods and always emphasized us to

investigate in the new methods.

VI. REFERENCES

[1] Phillip M. Yelland, Bayesian forecasting of parts demand,

International Journal of [1] Forecasting, Volume 26, Issue 2, Special

Issue: Bayesian Forecasting in Economics, April-June 2010, Pages 374-

396, ISSN 0169-2070, DOI: 10.1016/j.ijforecast.2009.11.001.

[2] Ruigang Wang; Wenyi Li; Bagen, B.; , "Development of Wind Speed

Forecasting Model Based on the Weibull Probability Distribution,"

Computer Distributed Control and Intelligent Environmental Monitoring

(CDCIEM), 2011 International Conference on , vol., no., pp.2062-2065,

19-20 Feb. 2011

doi: 10.1109/CDCIEM.2011.333