1. 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 theorem and Weibull probability distribution there are
exponential rate that is only overcome by our new advances in this areas we are going to review
ambition of keep growing, every single day two papers, the first one “Bayesian forecasting of
appears a new product, that is designed taking in parts demand” published by Elsevier B.V where
account the life cycle and obsolescence time, this applies Bayes theorem in the forecast of demand
happens specially with the information technology of technology parts and the second one
companies (ITC) that in our days are the most
“Development of wind speed forecasting Model
powerful giants of world`s industry in the other
Based on the Weibull Probability Distribution”
hand we are using too much energy than the
planet is capable to provide in an undefined period
published by Ruigang Wang, Wenyi Li and B.
of time for that reason alternative energy is Bagen where use Weibull probability distribution
earning value, this paper focuses on two new models to forecast the wind speed.
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 II. BAYESIAN FORECASTING OF PARTS
speed in the energy industry in order to increase DEMAND
the efficient of wind energy technology.
Manufacturing of high technology products like
computer is an exacting business were the supply
chain as we as industrial engineers know must be
I. INTRODUCTION synchronize optimizing the information and materials
flow, no all the times this job is easy, in this particular
Having the knowledge of the limited classic literature case the demand of computer parts is a very complex,
that is available and useful for this kind of forecasting because no matter if computers is an exacting
it was necessary to entrepreneur in this new field business, computers parts business is very complex
using the statistics tools that are around us, we have because it depends of the life cycle of the part and the
heard that the universe has an equilibrium equation, obsolescence of the part in this case Sun
natural events can be modeled in mathematics Microsystems Inc is a vendor of computer products
models, in the same way market behavior can be that it is fettered to the supply chain
modeled too, using statistical tools like Bayes

2. In order to have an idea of the type of market III. DEVELOPMENT OF WIND SPEED
behavior that we they are dealing with it appear in the FORECASTING MODEL BASED ON
next graphics. 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
Fig. 1. Demands
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
Where the solid lines present de demands, the power output of a wind turbine generator (WTG) unit
horizontal axe represent the financial planning fluctuates with the wind speed variations, existing
periods of roughly one month’s duration, we can see forecasting methods presents significant errors in the
how demand`s behavior depends of the life cycle and forecast what make no reliable to analyze power
obsolescence, short life cycles means that the networks impact, so in this paper present an improved
individuals parts frequently do not have sufficient probability method based on Weibull distribution,
observed demand values to support reliable with two parameters Weibull fit with the actual wind
extrapolation, Bayesian model take in account speed perfectly. Although there are only two
predictive conditionals, life cycle curves, uncorrelated parameters on Weibull distribution, the wind model is
errors, auto correlated errors, scale factors, priors very sensitive two those parameters, so if the
parameters, distributions of parts demand, estimation, parameters are designed with the proper accuracy the
the Bayesian model requires an investment of wind speed forecasting model can represent the actual
$10.000 USD due that is an heuristic algorithm of 482 speed variation.
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.
Fig. 2. Probabilities density function

3. This paper improves existing Weibull method majority of the cases, in this two reviews the new
combining the mean wind speed and standard forecasting methods will help in the company
deviation method with the maximum likehood evolution, will save a lot of money, will increase the
method, and wind speed is modeled as a random utilities, will optimize alternative energies, will help
variable with a Weibull distribution. to save the world etc.
It also compare time series methods like AR(p) and V. ACKNOWLEDGMENTS
MA(q) with the new method that is proposed, the
accuracy of this method is significant better than the This paper was supported by “Universidad Del
other ones, obviously every forecast includes a Norte”, Ing. Daniel Romero and Ing. Carlos Paternina
natural errors, the wind variation change the behavior that provides us with the knowledge in classic
depending of annual period, season period and diurnal forecasting methods and always emphasized us to
period, we can see the comparison of the methods in investigate in the new methods.
each period in the next table.
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.
Chart 1. Comparison of methods
[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
Improved method has smaller errors in each period, (CDCIEM), 2011 International Conference on , vol., no., pp.2062-2065,
what means that is describing and forecasting the 19-20 Feb. 2011
winds speed variation better than the other methods, doi: 10.1109/CDCIEM.2011.333
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