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Supply Chain Dynamics and Forecasting

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Presenter: Mu Niu Dynamics and Forecasting * *Dynamics and Forecasting *Dynamics and Forecasting * The title of my thesis is Supply china dynamics and forecasting. – PowerPoint PPT presentation

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Title: Supply Chain Dynamics and Forecasting


1
Supply Chain Dynamics and Forecasting
  • Presenter Mu Niu

2
The Context
  • Companies make huge investments in Manufacturing
    Resource Planning systems. However, even with the
    introduction of resource planning systems, the
    performance of the supply chain remains
    problematic ( Lyneis, 2005 ).
  • They do not take into account the inherent
    messiness of situations that contain human
    decision making within the process.
  • Such tools do not promote learning or effective
    decision support as they do not include the
    powerful technique of simulation to allow for
    what-if analysis of alternative strategies .

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3
The Problem
  • A centralised supply chain system was recently
    implemented in Draeger Safety Ltd, with the
    purpose of diminishing costs and avoiding
    backlogs. However, the central Hub in Germany
    still hold big amount of inventory.
  • This made Draegers planning managers even more
    worried as it was difficult to predict what the
    consequences of centralised inventories would be
    for the manufacturing plant in Blyth.

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4
The Research Focus
  • Modelling and simulation of the material and
    information flows including the decision
    processes of the centralised supply-chain at
    Draeger Safety, UK
  • Analyses of the behaviour of inventories with
    relation to different decision strategies and
    characteristics of managers
  • Evaluate the sensitivity of the supply chain to
    different methods of forecasting
  • Develop a Microworld (Senge, 1990) to enable
    managers to conduct what-if scenarios and learn
    about the behaviour of the supply chain.

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5
Draeger supply chain structure
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6
Germany- UK Model
  • 1Month
  • Mfacture

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7
Model Equations
  • Hinv(t) max(0, Hinv(t-1) Fship(t-1)
    Hship(t))
  • Hblk(t) max(0, Hblk(t-1) Horders(t) -
    (Hinv(t-1) Fship(t-1)) HUB
  • Hship(t) min(Horders(t) Hblk(t-1), Hinv(t-1)
    Fship(t-1))
  • Finv(t) max(0, Finv(t-1) Fprod(t-1)
    Fship(t))
  • Fblk(t) max(0, Fblk(t-1) Hreq(t1) -
    (Finv(t-1) Fprod(t-1)) Factory
  • Fship(t) min(Hreq(t1) Fblk(t-1), Finv(t-1)
    Fprod(t-1))
  • Hforcast(t2) (1 - ?) Horders(t) ?
    Hforcast(t1)
  • Hreq(t2) max( 0, a( Q Hinv(t) Hblk(t) )
  • aß( Fblk(t) Fship(t) )
    Hforcast(t2)) Decision
  • Fprod(t) max( 0, a ( Q Finv(t) Fblk(t) )
    Hreq(t2) ) Making

a, is a measure of the aggressiveness with which
inventory differences are corrected. 0,1 ß, is
a measure of the weight with which inventory
ordered but still to arrive. 0,1
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8
Nonlinear block diagram
9
Time simulation
Stable
Limit cycle
Quasi periodic
Chaotic
10
Equations
X(k) A X(k-1) B U(k)
11
System Block Diagram
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12
Eigenvalues plotted for a 00.011 , ß 0
and ß 00.011 , a 1 with unit circle
a 00.011 , ß 0
ß 00.011 , a 1
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13
The stability analysis
  • For the condition ß 0 (depicted in Figure of
    eigenvalue plots), the Factory characteristic
    equation is
  • (z a)(z -1) a 0
  • This has two eigenvalues, one at z 0 and a
    second, which is always real and which lies in
    the range z 1 ? 0 as a 0 ? 1.
  • The Hub characteristic equation is
  • (z2 a)(z -1) a 0
  • This has three eigenvalues. Again one of these
    is at z 0, the other two form a second order
    pair that become complex when a gt 0.25. It is
    this pair that is clearly identified in Figure of
    eigenvalue plots.
  • Moreover, it is the Hubs dynamics and not the
    Factorys that are the potential source of
    unstable behavior. The Hub, potentially,
    becoming unstable for any value of a gt 1, (whilst
    the Factory would be stable for any value of a lt
    2.)

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14

Model with two additional Production delays
To explore the long lead time production
dynamics. The additional delay were added into
the production
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15
System Block Diagram
16
Eigenvalues plotted for a 00.011 , ß 0
and ß 00.011 , a 1 with unit circle
17
Model Analysis
  • analysis given that the replenishing inventory
    rate a has a destablising effecs while the
    consideration of the past decision rate ß has a
    stablising effects on the dynamics of this
    production delayed supply chain model.
  • The extra production delay has made the system
    more sensitive to the management decisions.
    Comparing with the original model, the production
    delay model could be unstable, even the
    eigenvalues locating inside of the unit circle.
  • managers have a flexible option by improving the
    safety stock Q to stabilize the supply chain and
    achieve the on time delivery. However the
    warehouse has to pay more costs for holding the
    extra mount of safety stock.
  • With the introduction of the two additional lead
    time states, it is the Factory which provide the
    primary route toward instablility. In this
    situation, the Hub can do little about the poor
    management decisions in the Factory.

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18
Model with an Planning delays
  • The planning delay represents two likely
    scenarios
  • Getting forecast wrong
  • Compatibility problems between the planning
    systems at different locations

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19
System block diagram
Hub
Factory
20
Eigenvalues plotted for a 00.011 , ß 0
and ß 00.011 , a 1 with unit circle
21
Model Analysis
  • Just as in the two previous cases, a has a
    destabilising influence whilst ß is stabilising.
  • For this situation it is again the Hub management
    policy that is the primary route to instability.
    However, with the additional information delay
    the Hubs route to instability now follows the
    more severe path.
  • In the presence of the one month information
    delay, even the stabilising influence of ß only
    lessens the severity of the route to instability.
    As long as a 1, no matter what ß is, the model
    is always oscillating. Operations on the safety
    stock Q cannot make effects for the unstable
    behavior.
  • Thus, for this situation good management and
    management policies are critical if significant
    problems are to be avoided. Therefore, the
    accurate forecasting is essential to improve the
    supply chain performance.

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22
Time series Prediction
  • The basic principle of time series prediction is
    to use a model to predict the future data based
    on known past data.
  • Many kinds of forecasting methods implemented
    with system dynamic approach, ARMA
    (auto-regression and moving average) model,
    wavelet neural networks model has been applied.
  • A performance function, which measures the
    absolute difference between forecast and real
    data, is employed to record the cost for each
    different structured model


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23
The original data
  • The original data is 64 months sales history of
    Lung demand valve

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24
ARMA without any preprocessing
The coefficient is produced and updated by
Recursive least square
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25
ARMA with Differencing preprocessing
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26
Cost function
None Preprocessing Logarithm Differencing Logarithm and Differencing
Accumulative costs 11806 11535 10219 11258
Average costs 787 769 681 750
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27
Wavelet Neural Networks
This hybrid scheme includes three stages. 1)The
time series were decomposed with a wavelet
function into three sets of coefficients. 2)
Three new time series is predicted by a separate
NN 3)The prediction results are used as the
inputs of the third stage, where the next sample
of is derived by NN4.
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28
Forecasting results
ARMA
Neural Network
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29
Cost function
None Preprocessing Logarithm Differencing Logarithm and Differencing Neural Wavelet
Accumulative costs 11806 11535 10219 11258 5822
Average costs 787 769 681 750 388
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30
Summary and Contributions
  • The behaviors of Draeger supply chain model has
    been analyzed with different decision parameters.
    The small signal analysis shows that when the
    system behaves normally (no backlog) the factory
    and the hub are decoupled.
  • We identified the principle source of unstable
    behavior could be the factory or hub depnding on
    the operating condition. In the original model
    the route toward instability is via via the Hub
    management policy. With the introduction of the
    extra states (additional lead-time), it is the
    Factory which now provides the primary route
    toward instability .In the presence of one month
    planning delay, the Hubs route to instability
    follows the more severe path.
  • Because the systems are isolated poor
    management decisions in the Hub cannot be
    corrected by good decisions in the Factory
  • We have shown the most severe route to the
    instability come from the errors in forecasting.
    The wavelet neural network forecasting apparently
    offers to improvement over the Draeger current
    forecasting approach.

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31
Microworld
32
Further Research
  • Include the dynamics of other Hubs
  • Look at different decision making in different
    Hubs
  • look for methods to further improve forecasting

33
Publication
  • Niu M.,Sice P.,French I., Mosekilde E., (2007)
    The Dynamics Analysis of Simplified Centralised
    Supply Chain, The Systemist Journal, Oxford, UK,
    Nov.2007.
  • Niu M.,Sice P.,French I., Mosekilde E., (2008)
    Explore the Behaviour of Centralised Supply Chain
    at Draeger Safety UK, International Journal of
    Information system and Supply Chain Management,
    USA, Jan. 2008 (print copy availibel in Dec
    2008).
  • French I., Sice P., Niu M., Mosekilde E.,(2008)
    The Dynamic Analysis of a Simplified Centralised
    Supply Chain and Delay Effects, System Dynamic
    Conference, Athens, July.2008.
  • Sice P., Niu M., French I., Mosekilde E., (2008)
    The Delay Impacts on a Simplified Centralised
    Supply Chain, UK Systems Society Conference,
    Oxford, UK, Sep.2008.
  • Niu M, Sice P., French I., (2008) Nonlinear
    Forecasting Model, Northumbria Research Forum
    2008, Newcastle upon Tyne, UK.

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