You are here

Decision Policy Scenarios for Just-in-Sequence Deliveries

Journal Name:

Publication Year:

DOI: 
doi.org/10.3926/jiem.2090
Abstract (2. Language): 
Purpose: The Just-in-Sequence (JIS) approach is evidencing advantages when controlling costs due to product variety management, and reducing the risk of disruption in sourcing, manufacturing companies and third-party logistics (3PL). This has increased its implementation in the manufacturing industry, especially in highly customized sectors such as the automotive industry. However, despite the growing interest from manufacturers, scholarly research focused on JIS still remains limited. In this context, little has been done to study the effect of JIS on the fluidity of supply chains and processes of logistics suppliers as well as providing them with a decision making tool to optimise the sequencing of their deliveries. Therefore, the aim of this paper is to propose a genetic algorithm to evaluate different decision policy scenarios to reduce risks of supply disruptions at assembly line of finished goods. Consequently, the proposed algorithm considers a periodic review of the inventory that assumes a steady demand and short response times is developed and applied.Design/methodology/approach: Based on a literature review and real-life information, an abductive reasoning was performed and a case study application of the proposed genetic algorithm conducted in the automotive industry. Findings: The results obtained from the case study indicate that the proposed genetic algorithm offers a reliable solution when facing variability in safety stocks that operate under assumptions such as: i) fixed costs; ii) high inventory turnover; iii) scarce previous information concerning material requirements; and iv) replenishment services as core business value. Although the results are based on an automotive industry case study, they are equally applicable to other assembly supply chains. Originality/value: This paper is of interest to practitioners and academicians alike as it complements and supports the very limited scholarly research on JIS by providing manufacturers and 3PL suppliers competing in mass customized industries and markets, a decision support system to help decision making. Implications for the design of modern assembly supply chains are also exposed and future research streams presented.
581
603

REFERENCES

References: 

Bayara, N., Darmoulb, S., Hajri-Gabouja, S., & Pierreval, H. (2016). Using immune designed ontologies to
monitor disruptions in manufacturing systems. Computers in Industry, 81, 67-81.
https://doi.org/10.1016/j.compind.2015.09.004
Belekoukias, I., Garza-Reyes, J.A., & Kumar, V. (2014). The impact of lean methods and tools on the
operational performance of manufacturing organisations. International Journal of Production Research,
52(18), 5346-5366. https://doi.org/10.1080/00207543.2014.903348
Benkherouf, L., & Sethi, S. (2010). Optimality of (s; S) policies for a stochastic inventory model with
proportional and lump-sum shortage costs. Operations Research Letters, 38(4), 252-255.
https://doi.org/10.1016/j.orl.2010.02.008
BNSF. (2016). Private Equipment Policy 2016. http://www.bnsf.com.mx/customers/equipment/ (Accessed: July
2016).
Braden, D. (2016). US ports set to receive millions to improve freight fluidity . Journal of Commerce, 1 July, 2016.
http://www.joc.com/port-news/us-ports/us-ports-set-receive-millions-impr... (Accessed:
July 2016).Bueno, A., & Cedillo-Campos, M. (2014). Dynamic impact on global supply chains performance of
disruptions propagation produced by terrorist acts. Transportation Research Part E: Logistics and
Transportation Review, 61, 1-12. https://doi.org/10.1016/j.tre.2013.09.005
Bunkley, N. (2015). Ford F-150 output pinched by frame shortage, workers say. Automotive News, May 29, 2015.
http://www.autonews.com/article/20150529/OEM01/150529839/ford-f-150-outp...
(Accessed: July 2016).
Cedillo-Campos, M., & Perez-Araos, A. (2010). Hybrid supply chains in emerging markets: the case of the
Mexican auto industry. South African Journal of Industrial Engineering, 21(1), 193-206.
https://doi.org/10.7166/21-1-77
Cedillo-Campos, M., & Gudiño, G. (2011). A Custom-made production system for emerging markets, (In
Spanish). Interciencia, 36(6), 456-462.
Cedillo-Campos, M., Sanchez, C., Vadali, S., Villa, J., & Menezes, M. (2014). Supply chain dynamics and
the “cross-border effect:” The U.S.–Mexican border’s case. Computers and Industrial Engineering, 72,
261-273. https://doi.org/10.1016/j.cie.2014.03.015
Cedillo-Campos, M., & Cedillo-Campos, H. (2015). W@reRISK method: Security risk level classification
of stock keeping units in a warehouse. Safety Science, 79, 358-368. https://doi.org/10.1016/j.ssci.2015.06.009
Chakraborty, A., & Chatterjee, A. (2016). A surcharge pricing scheme for supply chain coordination under
JIT environment. European Journal of Operational Research, 253(1), 14-24.
https://doi.org/10.1016/j.ejor.2016.02.001
Chen, J.C., Chen, Y.Y., & Liang, Y. (2016). Application of a genetic algorithm in solving the capacity
allocation problem with machine dedication in the photolithography area. Journal of Manufacturing
Systems, 41, 165-177. https://doi.org/10.1016/j.jmsy.2016.08.010
Chiang, C.Y., Lin, W.T., & Suresh, N.C. (2016). An empirically-simulated investigation of the impact of
demand forecasting on the bullwhip effect: Evidence from U.S. auto industry. International Journal of
Production Economics, 117, 53-65. https://doi.org/10.1016/j.ijpe.2016.04.015
Diabat, A., & Deskoores, R. (2016). A hybrid genetic algorithm based heuristic for an integrated supply
chain problem. Journal of Manufacturing Systems, 38, 172-180. https://doi.org/10.1016/j.jmsy.2015.04.011
Dietrich, A.J., Kim, S., & Sugunaram, V. (2007). A service-oriented architecture for mass customization –
a shoe industry case study. IEEE Transactions on Engineering Management, 4(1), 190-204.
https://doi.org/10.1109/TEM.2006.889076Doolen, T.L., & Hacker, M.E. (2005). A review of lean assessment in organization: An exploratory study
of lean practices by electronic manufacturers. Journal of Manufacturing Systems, 24(1), 55-67.
https://doi.org/10.1016/S0278-6125(05)80007-X
Dubois, A., & Gadde, L.E. (2002). Systematic combining: an abductive approach to case research. Journal
of Business Research, 55(7), 553-560. https://doi.org/10.1016/S0148-2963(00)00195-8
ElMaraghy, H., Schuh, G., ElMaraghy, W., Piller, F., Schönsleben, P., Tseng, M. et al. (2013). Product
variety management. CIRP Annals – Manufacturing Technology, (62), pp. 629-652.
https://doi.org/10.1016/j.cirp.2013.05.007
Esmaeilian, B., Behdad, S., & Wang, B. (2016). The evolution and future of manufacturing: A review.
Journal of Manufacturing Systems, 39, 79-100. https://doi.org/10.1016/j.jmsy.2016.03.001
Graf, H. (2007). Innovative logistics is a vital part of transformable factories in the automotive industry.
In Dashchenko, A.I. (Ed.). Reconfigurable manufacturing systems and transformable factories. Berlin: Springer:
423-457.
Green, K., Inman, A., Birouc, L., & Whitten, D. (2014). Total JIT (T-JIT) and its impact on supply chain
competency and organizational performance. International Journal of Production Economics, 147(Part A),
125-135. https://doi.org/10.1016/j.ijpe.2013.08.026
Gunner, H., Tunali, S., & Jans R. (2010). A review of applications of genetic algorithms in lot sizing.
Journal of Intelligent Manufacturing, 21(4), 575-590. https://doi.org/10.1007/s10845-008-0205-2
Heinecke, G., Köber, J., Lepratti, R., Lamparter, S., & Kunz, A. (2012). Event-driven order rescheduling
model for just-in-sequence deliveries to a mixed-model assembly line. Advances in Production Management
Systems. Competitive Manufacturing for Innovative Products and Services, Volume 397 of the series IFIP Advances in
Information and Communication Technology, 326-333.
Hüttmeir, A., de Treville, S., Van Ackere, A., Monnier, L., & Prenninger, J. (2009). Trading off between
heijunka and just-in-sequence. International Journal of Production Economics, 118(2), 501-507.
https://doi.org/10.1016/j.ijpe.2008.12.014
Jianga, L., Wanga, Y., & Yan, X. (2014). Decision and coordination in a competing retail channel involving
a third-party logistics provider. Computers & Industrial Engineering, 76, 109-121.
https://doi.org/10.1016/j.cie.2014.07.026Kincade, D.H., Regan, C., & Gibson, F.Y. (2007). Concurrent engineering for product development in
mass customization for the apparel industry. International Journal of Operations & Production Management,
2(6), 627-649. https://doi.org/10.1108/01443570710750295
KPI Library (2016). Stock cover 2016. http://kpilibrary.com/kpis/stock-cover (Accessed: June 2016).
Kovács, G., & Spens, K.M. (2005). Abductive reasoning in logistics research. International Journal of Physical
Distribution & Logistics Management, 35(2), 132 144. https://doi.org/10.1108/09600030510590318
Kumar, V. (2010). JIT based quality management: Concepts and implications in Indian context.
International Journal of Engineering Science and Technology, 2(1), 40-50.
Kumar, V., Mishra, N., Chan, F.T., & Verma, A. (2011). Managing warehousing in an agile supply chain
environment: an F-AIS algorithm based approach. International Journal of Production Research, 49(21),
6407-6426. https://doi.org/10.1080/00207543.2010.528057
Kumar, V.N.S.A., Kumar, V., Brady, M., Garza-Reyes, J.A., & Simpson, M. (2017). Resolving forwardreverse
logistics multi-period model using evolutionary algorithms. International Journal of Production
Economics, 183(Part B), 458-469. https://doi.org/10.1016/j.ijpe.2016.04.026
Lindner, K. (2008). Evaluierung von Strategien zur standardisierten Notversorgung von Justin-Sequence-Teileumfängen in
der Motormontage der BMW Group [Evaluating strategies for the standardisation of contingency plans for JIS modules
in the engine assembly of BMW]. Working paper 2008; Vienna University of Economics and Business.
Ludwig, C. & Hogg, R. (2016). Audi adapts logistics for San José Chiapa. Automotive Logistics, 30 January.
http://automotivelogistics.media/news/mexico-conference-audi-adapts-logi... (Ac c e s s ed: July
2016).
Ludwig, C. (2016a). Volkswagen & Audi in Mexico part 2: Everything in its right place . Automotive Logistics, 19
J u l y . http://automotivelogistics.media/intelligence/volkswagen-audi-in-mexico-...
(Accessed: July 2016).
Ludwig, C. (2016b). Ford Mexico part 1: A landmark investment gradually coming into focus. Automotive Logistics,
1 Jul y. http://automotivelogistics.media/intelligence/landmark-investment-gradua... (Accessed: July
2016).
Ludwig, C. (2016c). Canada’s metro supply chain buys emergency freight provider evolution time critical. Automotive
Logistics, 5 April. http://automotivelogistics.media/news/canadas-metro-supply-chain-buys-em...
time-critical (Accessed: July 2016).Meissner, S. (2010). Controlling just-in-sequence flow-production. Logistics Research, 2(1), 45-53.
https://doi.org/10.1007/s12159-010-0026-5
Memaria, A., Rahimb, A., Absic, N., Ahmad, R., & Hassana, A. (2016). Carbon-capped distribution
planning: a JIT perspective. Computers & Industrial Engineering, 97, 111-127.
https://doi.org/10.1016/j.cie.2016.04.015
Morones, D. (2011). Modelo de escenarios de decisión para envíos de lotes fijos en inventarios con revisión periódica. M.Sc.
Thesis. In Spanish. National Council of Science and Technology, COMIMSA, Saltillo, Mexico.
Moslehi, G., & Mahnam, M. (2011). A Pareto approach to multi-objective flexible job- shop scheduling
problem using particles warm optimization and local search. International Journal of Production Economics,
129(1), 14-22. https://doi.org/10.1016/j.ijpe.2010.08.004
OICA - Organisation Internationale des Constructeurs d’Automobiles (2016). Production Statistics 2015.
http://www.oica.net/category/production-statistics/ (Accessed: July 2016).
Pelikan, M. (2010). Genetic Algorithms. Missouri Estimation of Distribution Algorithms Laboratory (MEDAL) ,
Department of Mathematics and Computer Science; St. Louis, MO, 29P.
Poiger, M., & Reiner, G. (2008). Gestaltung und Bewertung von Just-in-Sequence-Anlieferung in der
Automobilindustrie [Design and evaluation of JIS delivery in the automotive industry]. In Engelhardt-
Nowitzki, C., Nowitzki, O., & Krenn, B. (Eds.). Praktische Anwendung der Simulation im
Materialflussmanagement [Practical applications of material flow simulation]. Wiesbaden: Gabler, 133-143.
https://doi.org/10.1007/978-3-8349-9806-4_9
Rosendahl, F., & Radow, R. (2004). Produktionsendstufe für Motorra der [Final assembly for
motorcycles]. In Wiendahl, H.P., Gerst, D. & Keunecke, L. (Eds.). Variantenbeherrschung in der Montage
[Variety management in production assemblies] 2004. Berlin: Springer, 191-207.
https://doi.org/10.1007/978-3-642-18947-0_11
Sanchez, C., Cedillo-Campos, M., Martinez, J., & Perez, P. (2011). Global economic crisis and Mexican
automotive suppliers: impacts on the labor capital. Simulation: Transactions of the Society for Modeling and
Simulation International, 87(8), 711-725. https://doi.org/10.1177/0037549710393259
Saracoglua, I., Topaloglub, S., & Keskinturk, T. (2014). A genetic algorithm approach for multi-product
multi-period continuous review inventory models. Expert Systems with Applications, 41(18), 8189-8202.
https://doi.org/10.1016/j.eswa.2014.07.003Staeblein, T., & Aoki, K. (2015). Planning and scheduling in the automotive industry: A comparison of
industrial practice at German and Japanese makers. International Journal of Production Economics, 162,
258-272. https://doi.org/10.1016/j.ijpe.2014.07.005
Shi, Y., Zhang, A., Arthanari, T., Liu, Y., & Cheng, T.E.C. (2016). Third-party purchase: An empirical
study of third-party logistics providers in China. International Journal of Production Economics, 171, 189-200.
https://doi.org/10.1016/j.ijpe.2015.08.028
Suyabatmaza, A., Altekinb, F., & Şahin, G. (2014). Hybrid simulation-analytical modeling approaches for
the reverse logistics network design of a third-party logistics provider. Computers & Industrial Engineering,
70, 74-89. https://doi.org/10.1016/j.cie.2014.01.004
Thun, J.H., Drücke, M., & Silveira-Camargos, V. (2007). Just in Sequence – Eine Erweiterung des Just in
Time durch Sequenzzulieferung [Just-in-sequence – an expansion of just-in-time by sequence delivery].
Logistik Management, 9(4), 34-46.
Thun, J.H., Marble, R.P., & Silveira-Camargos, V. (2007). A conceptual framework and empirical results of
the risk and potential of just-in-sequence – a study of the German automotive industry. Journal of
Operations and Logistics, 1(2), 1-13.
T’kindt, V. (2011). Multicriteria models for just-in-time scheduling. International Journal of Production
Research, 49(11), 3191-3209. https://doi.org/10.1080/00207541003733783
Toth, M., Seidel, T., & Klingebiel, K. (2008). Moving towards BTO – an engine case study. In Parry, G., &
Graves, A. (Eds.). Built to order – the road to the 5-day car. London: Springer. 297-310.
https://doi.org/10.1007/978-1-84800-225-8_17
Trebilcock, B. (2006). Building a new supply chain. Modern Materials Handling, 61(1), 32.
Trentin, A., Forza, C., & Perin, E. (2015). Embeddedness and path dependence of organizational
capabilities for mass customization and green management: A longitudinal case study in the machinery
industry. International Journal of Production Economics, 169, 253-276. https://doi.org/10.1016/j.ijpe.2015.08.011
Vörös, J., & Rappai, G. (2016). Process quality adjusted lot sizing and marketing interface in JIT
environment. Applied Mathematical Modelling, 40(13-14), 6708-6724. https://doi.org/10.1016/j.apm.2016.02.011
Wagner, S., & Silveira-Camargos, V. (2011). Decision Model for the application of just-in-sequence.
International Journal of Production Research, 49(19), 5713-5733. https://doi.org/10.1080/00207543.2010.505216Wagner, S., & Silveira-Camargos, V. (2012). Managing risks in Just-In-Sequence supply networks:
exploratory evidence from automakers. IEEE Transactions on Engineering Management, 59(1), 52-64.
https://doi.org/10.1109/TEM.2010.2087762
Werner, S., Kellner, M., Schenk, E., & Weigert, G. (2003). Just-in-sequence material supply – a simulation
based solution in electronics production. Robotics & Computer Integrated Manufacturing, 19(1/2), 107-111.
https://doi.org/10.1016/S0736-5845(02)00067-4
Zhang, Y., & Lam, J. (2016). Estimating economic losses of industry clusters due to port disruptions.
Transportation Research Part A: Policy and Practice, 91, 17-33. https://doi.org/10.1016/j.tra.2016.05.017

Thank you for copying data from http://www.arastirmax.com