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Supply chain information sharing under consideration of bullwhip effect and system robustness

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Abstract

Supply chain system experiences variance amplification in order replenishment and inventory level, leading to severe inefficiencies of the system. Information distortion is universally known as a fundamental reason for the variance amplification phenomenon. The purpose of this paper is to study the effect of demand information sharing in reducing bullwhip effect and improving the robustness of supply chain systems. The “automatic pipeline inventory and order-based production control system, APIOBPCS” is adopted to model supply chains with different information-sharing strategies. The stochastic factors in the supply chain system lead to poor performance in system robustness. Taguchi design is adopted to find out the optimal setting of ordering parameters in the APIOBPCS model for a robust supply chain. An extension of Taguchi design is adopted to solve the multi-response problems. The weighted signal-to-noise ratio is used as the performance index of the overall performance of the supply chain, including inventory cost, customer service level, and inventory variance amplification. The results show that full demand information transparency helps to improve the overall performance of supply chain. Furthermore, the sensitivity analysis of stochastic lead times verifies the results. This research gives some insights to improve the overall performance of supply chain via information sharing.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China (Nos. 71931006, 71871119 and 71771121), the Natural Sciences and Engineering Research Council of Canada (No. RGPIN-2018-03862), the Fundamental Research Funds for the Central Universities (No. 3091511102), and China Scholarship Council.

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Correspondence to Yizhong Ma.

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Appendices

Appendices

1.1 Appendix 1: Nomenclature

1. Variables in simulation model.

AINV

Actual inventory

AVCON

Average consumption

AWIP

Actual work-in-progress

COMRATE

Completion rate

CONS

Consumption

TINV

Target inventory

EINV

Error of inventory

TWIP

Target WIP

EWIP

Error of WIP

ORATE

Order rate

DRATE

Demand rate of customer

TBA

Time between arrivals of customer demand

TQ

Transportation quantity

OWD

Products on way of delivery

QP

Quality passed products

QF

Quality failed products

2. Parameters in simulation model.

CAPCON

Capacity constraint

QRATE

Quality pass rate of products

To

Order lead time

Td

Delivery lead time

Tp

Production lead time

Tr

Rework time of unqualified products

Kw

Constant multiplier to determine target WIP

Ta

Time to smooth consumption

Ti

Time to recover inventory

Tw

Time to recover WIP

1.2 Appendix 2: Equations for the APIOBPCS based simulation model

1. Determine the order/production rate

$$ \begin{gathered} n = 1,\;retailer; \, n = 2,wholesaler;\;n = 3,distributor;\;n = 4,factory \hfill \\ CONS_{{_{t} }}^{1} = DRATE \hfill \\ CONS_{{_{t} }}^{n} = ORATE_{t - To}^{n - 1} \;(n = 2,3,4) \hfill \\ AVCON_{t}^{n} = AVCON_{t - 1}^{n} + (DRATE - AVCON_{t - 1}^{n} )/(1 + Ta)\;)\;\;({\text{demand}}\;{\text{information}}\;{\text{sharing}}\;) \hfill \\ AVCON_{t}^{n} = AVCON_{t - 1}^{n} + (CONS_{{_{t} }}^{n} - AVCON_{t - 1}^{n} )/(1 + Ta)\;\;({\text{without}}\;{\text{shared}}\;{\text{demand}}\;{\text{information}})\; \hfill \\ \end{gathered} $$
$$ \begin{gathered} \;s = ORATE_{t - Tp}^{4} \hfill \\ For\;i = 1:s \hfill \\ \;\;\;\;q(i) = DICS\left( {0.9,1,1.0,2} \right) \hfill \\ \;\;\;\;QP_{t} = \sum\limits_{i = 1}^{s} {\left( {q(i)\left| {q(i) = 1} \right.} \right)} \hfill \\ \;\;\;\;QF_{t} = \frac{1}{2}\sum\limits_{i = 1}^{s} {\left( {q(i)\left| {q(i) = 2} \right.} \right)} \hfill \\ \end{gathered} $$
$$ AINV_{t}^{n} = AINV_{t - 1}^{n} - CONS_{t}^{n} + COMRATE_{t}^{n} $$
$$ EINV_{t}^{n} = TINV^{n} - AINV_{t}^{n} $$
$$ AWIP_{t}^{n} = AWIP_{t - 1}^{n} + ORATE_{t}^{n} - COMRATE_{t}^{n} $$
$$ \begin{gathered} TWIP_{t}^{n} = AVCON_{t}^{n} *Kw \hfill \\ EWIP_{t}^{n} = TWIP_{t}^{n} - AWIP_{t}^{n} \hfill \\ ORATE_{t}^{n} = MAX(AINT(AVCON_{t}^{n} + EINV_{t}^{n} /Ti + EWIP_{t}^{n} /Tw + 1),0)\;\;n = 1,2,3 \hfill \\ ORATE_{t}^{4} = MIN(MAX(AINT(AVCON_{t}^{n} + EINV_{t}^{n} /Ti + EWIP_{t}^{n} /Tw + 1),0),CAPCON) \hfill \\ \end{gathered} $$

2. Ordering delay and transportation delay

$$ \begin{gathered} TQ_{t}^{1} = MAX(MIN(CONS_{t}^{1} ,AINV_{t}^{n + 1} ),0)\; \hfill \\ TQ_{t}^{n + 1} = MAX(MIN(ORATE_{t - To}^{n} ,AINV_{t}^{n + 1} ),0)\;\;\;\;n = 1,2,3 \hfill \\ COMRATE_{t}^{n} = TQ_{t - Td}^{n + 1} \;\;n = 1,2,3 \, \hfill \\ \end{gathered} $$

3. Production delay and rework delay

$$ \begin{gathered} \;s = ORATE_{t - Tp}^{4} \hfill \\ For\;i = 1:s \hfill \\ \;\;\;\;q(i) = DICS\left( {0.9,1,1.0,2} \right) \hfill \\ \;\;\;\;QP_{t} = \sum\limits_{i = 1}^{s} {\left( {q(i)\left| {q(i) = 1} \right.} \right)} \hfill \\ \;\;\;\;QF_{t} = \frac{1}{2}\sum\limits_{i = 1}^{s} {\left( {q(i)\left| {q(i) = 2} \right.} \right)} \hfill \\ \end{gathered} $$
$$ COMRATE_{t}^{4} = QP_{t} + QF_{t - Tr} $$

4. Fulfilling backorders

$$ When\;AINV_{t}^{n} < 0 $$
$$ TQ_{t}^{n} = MIN(COMRATE_{t}^{n} ,ABS(AINV_{t}^{n} )) $$

1.3 Appendix 3: Taguchi design for three responses with 10 replicates

See Tables 10,11, 12

Table 10 The experiment data for inventory cost in EPOS model
Table 11 The experiment data for customer service level in EPOS model
Table 12 The experiment data for inventory variance amplification in EPOS model

1.4 Appendix 4: Experiment data for sensitivity analysis of stochastic lead time

See Table 13

Table 13 The responses data under three scenarios of stochastic lead times

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Tang, L., Yang, T., Tu, Y. et al. Supply chain information sharing under consideration of bullwhip effect and system robustness. Flex Serv Manuf J 33, 337–380 (2021). https://doi.org/10.1007/s10696-020-09384-6

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