Allocation Planning for Demand Fulfillment in Make-to-Stock Industries

190 Pages·2015·3.22 MB·English

Allocation Planning for Demand Fulfillment
in Make-to-Stock Industries
– A Stochastic Linear Programming Approach
genehmigte Dissertation
zur Erlangung des akademischen Grades
eines Doctor rerum politicarum (Dr. rer. pol.)
der Technischen Universita¨t Darmstadt
Fachbereich Rechts- und Wirtschaftswissenschaften
Hochschulkennziffer D17
vorgelegt von
Dipl.-Wi.-Ing. Stephanie Eppler
Geburtsort: Albstadt
Referent: Prof. Dr. Herbert Meyr
Korreferent: Prof. Dr. Michael Schneider
Tag der Einreichung: 04.11.2014
Tag der Disputation: 28.01.2015
Darmstadt 2015
Acknowledgments
Firstandforemost, IwouldliketoexpressmygratitudetomyPhDadvisorProf.Dr.Herbert
Meyr for all his support and for providing guidance during the last years. Throughout the
entire project and despite your busy schedule, you always found time for fruitful discussions,
valuable comments and questions. You provided the opportunity to learn a lot by sharing
your great experience.
Furthermore, I would like to thank Prof. Dr. Michael Schneider for acting as second
reviewer of my PhD thesis. Also thanks to Prof. Dr. Anne Lange, Prof. Dr. Volker Nitsch
and Prof. Dr. Reiner Quick for being members of my PhD committee.
Thanks also to my colleagues at the Chair of Supply Chain Management at the University
of Hohenheim: In particular, I want to thank Mirko Kiel and Martin W¨orbelauer for their
support and for the great atmosphere at the chair.
Moreover, I am sincerely grateful to my family who has always believed in me and has
always supported me in achieving my goals. Thank you also for showing so much under-
standing throughout the last years.
Most of all, however, I am grateful to Volker. You have always been there for me, being
an invaluable source of motivation and confidence. Thank you so much for your love and
your support.
Stephanie Eppler, Stuttgart, November 2014
Contents
Acknowledgements ii
List of Figures vi
List of Tables viii
List of Abbreviations ix
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Goals and Methodology . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Conceptual and Methodological Basics 6
2.1 Quantity-based Revenue Management . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 Origins of Revenue Management . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Revenue Management – Definitions and Implementation Approaches . 8
2.1.3 Conditions for Revenue Management to Be Beneficial . . . . . . . . . 10
2.1.4 Capacity Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.5 Littlewood’s Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.6 Randomized Linear Programming . . . . . . . . . . . . . . . . . . . . 20
2.1.7 Flexible Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Demand Fulfillment & Available-to-Promise . . . . . . . . . . . . . . . . . . 23
2.2.1 Classification of Demand Fulfillment as a Planning Task of Supply
Chain Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.2 Customer Order Decoupling Point . . . . . . . . . . . . . . . . . . . . 28
2.2.3 Definition of the Terms Demand Fulfillment and Available-to-Promise 31
2.2.4 Planning Tasks of Demand Fulfillment . . . . . . . . . . . . . . . . . 34
2.2.5 TransferofRevenueManagementideastoDemandFulfillmentinMake-
to-Stock – Allocation Planning . . . . . . . . . . . . . . . . . . . . . 38
2.2.6 Demand Fulfillment in Advanced Planning Systems . . . . . . . . . . 42
2.2.7 State-of-the-Art Models for Allocation Planning in Make-to-Stock En-
vironments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Contents
2.3 Two-Stage Stochastic Linear Programming with Recourse . . . . . . . . . . . 50
2.3.1 General Model Formulation . . . . . . . . . . . . . . . . . . . . . . . 50
2.3.2 Measuring the Value of the Stochastic Solution . . . . . . . . . . . . 52
2.3.3 Literature Review of two-stage SLP and SMIP applications . . . . . . 53
3 Two-Stage Stochastic Linear Programming Formulations of Littlewood’s
Model 57
3.1 Model Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.1.1 Marginal Analysis of Littlewood’s Rule – SLW-MA . . . . . . . . . . 57
3.1.2 Littlewood’s Model with Partitioned Allocations – SLW-PAR . . . . 60
3.1.3 Littlewood’s Model with Nested Allocations – SLW-NES . . . . . . . 62
3.1.4 Dual Model of SLW-MA . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.1.5 Dual Model of SLW-PAR . . . . . . . . . . . . . . . . . . . . . . . . 65
3.1.6 Dual Model of SLW-NES . . . . . . . . . . . . . . . . . . . . . . . . 66
3.2 Numerical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.2.1 Verification Tests of the Primal Models . . . . . . . . . . . . . . . . . 67
3.2.1.1 SLW-MA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.2.1.2 SLW-PAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.2.1.3 SLW-NES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.2.2 Tests of the Dual Models . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4 Single-Period Models for Allocation Planning in Make-to-Stock Environ-
ments 75
4.1 Conditions for Allocation Planning to Be Beneficial . . . . . . . . . . . . . . 76
4.1.1 Order Arrival Sequence . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.1.2 Customer Segmentation and Heterogeneity . . . . . . . . . . . . . . . 82
4.1.3 Load Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.1.4 Demand Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.1.5 Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.2 Model Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.2.1 Allocation Planning Model Anticipating a Partitioned Consumption
Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.2.2 Allocation Planning Model Anticipating a Nested Consumption Rule 92
4.3 Numerical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.3.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.3.2 Analysis of the Base Case . . . . . . . . . . . . . . . . . . . . . . . . 98
4.3.3 Choice of Steering Profits . . . . . . . . . . . . . . . . . . . . . . . . 100
4.3.3.1 Choice of the Steering Profits for the Unallocated Quantities 101
4.3.3.2 Choice of the Steering Profits for the Nesting Quantities . . 104
iv
Contents
4.3.4 Benefit of the Anticipation . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.4.1 Benefit of Anticipating a Standard Nesting Consumption Rule109
4.3.4.2 Benefit of Anticipating a Partitioned Consumption Rule . . 111
4.3.5 Benefit of Accounting for Uncertainty . . . . . . . . . . . . . . . . . . 112
4.3.5.1 Influence of Demand Uncertainty . . . . . . . . . . . . . . . 113
4.3.5.2 Influence of Customer Heterogeneity . . . . . . . . . . . . . 116
4.3.6 Benefit of Nesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5 A Multi-Period Model for Allocation Planning in Make-to-Stock Environ-
ments 123
5.1 Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.2 Numerical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.2.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.2.2 Analysis of the Base Case . . . . . . . . . . . . . . . . . . . . . . . . 132
5.2.3 Benefit of Accounting for Uncertainty by Means of an SLP . . . . . . 134
5.2.4 Influence of the Replanning Frequency . . . . . . . . . . . . . . . . . 137
5.2.5 Influence of the Replenishment Frequency . . . . . . . . . . . . . . . 139
5.2.6 Influence of the Holding and Backlogging Costs . . . . . . . . . . . . 142
5.2.7 Comparison with APS Rules . . . . . . . . . . . . . . . . . . . . . . . 144
5.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
6 Conclusions and Further Research 149
6.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6.2 Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Appendix 155
Bibliography 162
Curriculum Vitae 179
v
List of Figures
2.1 Illustration of partitioned protection levels and booking limits . . . . . . . . 13
2.2 Illustration of nested protection levels and booking limits . . . . . . . . . . . 13
2.3 Illustration of the relationship between booking limits and bid prices . . . . 15
2.4 Decision tree when C˜R units of capacity are remaining . . . . . . . . . . . . 19
2.5 SCP matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6 SCP matrix with module structure . . . . . . . . . . . . . . . . . . . . . . . 26
2.7 Decoupling points in MTO, ATO and MTS . . . . . . . . . . . . . . . . . . . 29
2.8 Modeling environment without customer segmentation . . . . . . . . . . . . 37
2.9 Modeling environment with customer segmentation . . . . . . . . . . . . . . 42
3.1 Bid prices bLW and bmˆ3 of the SLP models mˆ = {SLW-MAD, SLW-PARD,
3
SLW-NESD} for different load factors lf and a different number S of scenarios 72
4.1 Visualization of an order sequence . . . . . . . . . . . . . . . . . . . . . . . . 78
4.2 Illustration of the benefit of allocation planning . . . . . . . . . . . . . . . . 78
4.3 Illustration of order arrival sequences and the related potential benefit of
allocation planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.4 Order sequences with different heterogeneities . . . . . . . . . . . . . . . . . 83
4.5 Alternatives of customer segmentation . . . . . . . . . . . . . . . . . . . . . 84
4.6 Variation of the ATP quantity . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.7 Illustration of two customer classes’ uncertain demand . . . . . . . . . . . . 87
4.8 Benefit of accounting for uncertainty – resulting profits for order sequence 3
when applying a DLP and an SLP . . . . . . . . . . . . . . . . . . . . . . . 88
4.9 Decision tree for allocation planning . . . . . . . . . . . . . . . . . . . . . . . 89
4.10 Representation of an lbh order arrival sequence and a standard nesting rule
in the AP-NES model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.11 Representation of an lbh order arrival sequence and the nesting rule described
by Vogel (2013) in the AP-NES model . . . . . . . . . . . . . . . . . . . . . 95
4.12 Percentage of ATP allocated to the different classes or remaining unallocated 99
4.13 Fill rates of the different classes for different allocation planning models and
FCFS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.14 Percentage of ATP remaining unallocated . . . . . . . . . . . . . . . . . . . 102
4.15 Impact of steering profits pu depending on cov . . . . . . . . . . . . . . . . . 103
s
List of Figures
4.16 Impact of steering profits pu on the profit deviation from the GOP depending
s
on lf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.17 Representation of a mixed order arrival sequence and a standard nesting rule
in the AP-NES model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.18 Allocated and unallocated ATP quantity for different steering profits . . . . 106
4.19 Impact of steering profits pn and pu on the profit deviation from the GOP
k(cid:48)k s
depending on cov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.20 ATP quantity allocated by the SLP models AP-PAR and AP-NES depending
on cov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.21 Absolute profits and the benefit of anticipating a nested consumption rule . . 109
4.22 Fill rates when a nested consumption rule is applied . . . . . . . . . . . . . . 110
4.23 Absolute profits and the benefit of anticipating a partitioned consumption rule111
4.24 Fill rates when a partitioned consumption rule is applied . . . . . . . . . . . 112
4.25 Influence of demand uncertainty on the absolute profits and on the percentage
profit deviation from the GOP . . . . . . . . . . . . . . . . . . . . . . . . . . 113
4.26 Allocations and fill rates of classes 1 – 3 for different values of cov . . . . . . 115
4.27 Influence of customer heterogeneity on the total profit and the percentage
profit deviation from the GOP . . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.28 Profits of different allocation planning models in combination with different
consumption rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.29 Benefit of allowing for standard nesting . . . . . . . . . . . . . . . . . . . . . 119
4.30 Comparison of the capacity utilization when applying standard nesting or a
partitioned consumption rule . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.1 Illustrationofsimulationhorizon,allocationplanninghorizon,andfrozenhori-
zon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.2 Base case related results: lost sales, quantities backlogged, fulfilled from ATP
which becomes available at or prior to the due date . . . . . . . . . . . . . . 133
5.3 AbsoluteprofitsandrelativeprofitdeviationsfromtheGOPfordifferentpolicies135
5.4 Costs and lost sales for different policies and values of cov . . . . . . . . . . 136
5.5 Absolute profits for different lengths of the frozen horizon . . . . . . . . . . . 138
5.6 Backlogging costs and lost sales for different lengths of the frozen horizon . . 139
5.7 Absolute profits for different replenishment frequencies . . . . . . . . . . . . 140
5.8 Lost sales, quantities backlogged, fulfilled from ATP which becomes available
at or prior to the due date for different replenishment frequencies . . . . . . 141
5.9 Absolute profits for different values of backlogging and inventory holding costs 142
5.10 Inventory holding and backlogging costs and related quantities for different
cost parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5.11 Absolute profits and relative profit deviations of different APS rules . . . . . 144
5.12 Lost sales of different allocation planning rules and TPAR . . . . . . . . . . 146
5.13 Lost sales of different allocation planning rules and TNES . . . . . . . . . . 147
vii
List of Tables
2.1 Indices, data and variables of the SOPA model (Meyr (2009)) . . . . . . . . 46
2.2 Additional index, modified data and variables of the RLP model by Quante
(2009), pp. 61 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.3 Allocation planning models for MTS environments . . . . . . . . . . . . . . . 49
3.1 Indices, parameters and variables of the SLW-MA model . . . . . . . . . . . 58
3.2 Additional parameters and variables of the SLW-PAR model . . . . . . . . . 61
3.3 Additional parameter and variables of the SLW-NES model . . . . . . . . . 62
3.4 Variables of the SLW-MAD model . . . . . . . . . . . . . . . . . . . . . . . . 64
3.5 Additional variables of the SLW-PARD model . . . . . . . . . . . . . . . . . 66
3.6 Absolute percentage deviations ∆¯zmˆ1,mˆ2 of the SLP protection levels zmˆ1 from
their analytical equivalents zmˆ2 for S = 100 scenarios (S = 10 scenarios),
computed as averages over all parameter combinations . . . . . . . . . . . . 68
¯ SLW−NES,LW
3.7 Average allocation deviations ∆z and the corresponding shortage
probabilities P(D +D > C) (both measured in %) for different load factors
1 2
lf and S = 100 scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
¯ SLW−NES,LW
3.8 Average absolute optimality gaps ∆E (measured in %) for the
revenue obtained in the consumption process for different load factors and
S = 100 scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.1 Exemplary order sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2 Exemplary order sequence with low heterogeneity . . . . . . . . . . . . . . . 82
4.3 Indices, data and variables of AP-PAR . . . . . . . . . . . . . . . . . . . . . 91
4.4 Additional data and variables of AP-NES . . . . . . . . . . . . . . . . . . . 93
4.5 Absolute profits and relative profit deviations for the base case data . . . . . 99
4.6 Values of γ and impact on the relation of the per-unit profit for unallocated
quantities pu to the classes’ profits in case of deterministic demand . . . . . . 101
s
5.1 Indices, data and variables of the consumption LP for order o¯ . . . . . . . . 125
5.2 Additional and modified indices, data and variables of AP-TIME . . . . . . 126
5.3 Absolute profits and relative profit deviations for the base case data . . . . . 133
5.4 Average computation times (in seconds) of an iteration n of the base case for
different allocation planning models . . . . . . . . . . . . . . . . . . . . . . . 137
List of Abbreviations
AATP Advanced available-to-promise
aATP Allocated available-to-promise
acc. According
analyt. Analytical
AP Allocation planning
AP-NES Single-period, multi-class allocation planning model anticipating a nested
consumption rule
AP-PAR Single-period, multi-class allocation planning model anticipating a parti-
tioned consumption rule
AP-TIME Multi-period, multi-class allocation planning model anticipating a time-
based consumption rule
APICS American Production & Inventory Control Society
APO Advanced Planner & Optimizer
APS Advanced planning system
ATO Assemble-to-order
ATP Available-to-promise
B Backlog
BOP Batch order processing
cATP Cumulated available-to-promise
CODP Customer order decoupling point
CR Consumption rule
CTO Configure-to-order
CTP Capable-to-promise
DD Discrete distribution
Dem. Demand
det. Deterministic
DLP Deterministic linear program
DND Discretised normal distribution
EEV Expected result of using the expected value solution
EMSR Expected Marginal Seat Revenue
end. Endogenous
ERP Enterprise resource planning
EV Expected value
List of Abbreviations
EVP Expected value problem
EVPI Expected value of perfect information
ex. Exogenous
FCFS First-come, first-serve
GLPK GNU Linear Programming Kit
GOP Global optimum
GSL GNU Scientific Library
hbl High-before-low
HN Here-and-now
KPI Key performance indicators
lbh Low-before-high
LP Linear program(ming)
LW Littlewood’s model
LW-PAR Littlewood’s partitioned model
MIP Mixed integer program
MRP Material requirements planning
MTO Make-to-order
MTS Make-to-stock
n.a. Not applicable
n.s. Not specified
NES Nested (rule)
NLKP Non-linear knapsack problem
NLSP Non-linear stochastic program
O&D Origin-destination
OP Order processing
PAR Partitioned (rule)
repl. Replenishment
RLP Randomized linear program
SAA Sample average approximation
SCM Supply chain management
SCP Supply chain planning
SDP Stochastic dynamic program
Seq. Sequence
SLP Stochastic linear program
SLW-MA Stochastic linear programming formulation of Littlewood’s rule derived
from the marginal analysis
SLW-MAD Dual stochastic linear programming formulation of Littlewood’s rule de-
rived from the marginal analysis
SLW-NES Stochastic linear programming formulation of Littlewood’s model
SLW-NESD Dual stochastic linear programming formulation of Littlewood’s model
x