Further Research - Improving Transportation and Warehousing Efficiency with Simulation-Based De

Further research concerning the first publication includes expanding the seaport simulation model to contain information about hinterland development as well. The potential hinterland includes warehouses, railway capacity and needs of road transportation. The driving factors of the model (industrial production and Russian oil exports) should also be analyzed more thoroughly to find out how better short term forecasts could be made.

In the second publication, hinterland logistics is seen to be a possible way to expand the simulation models as well. Emergency situations are another possible avenue for further research. In addition to these, the paper proposes the use of hybrid-simulation models to see whether more useful models can be created.

The model presented in the sixth publication could be improved by analyzing the hinterland logistics more thoroughly. Potential further research includes using other

Table 17: Limitations of the simulation models

Article Method Limitations PI SD & ARIMA

combined

No feedback inside the model

Industrial production and Russian oil exports were the only external variables, while in reality the seaports are connected PII SD & ABM

separately

Demand in both models is external

The models operate on a high level of abstraction

PIII ABM No feedback between preventive and corrective maintenance Only one machine segment

Some of the engineers operate on too simple heuristics PIV Monte carlo No dynamic behavior

The models are mainly linear

PVI SD Only two seaports in both models

Does not take the size of ships into account

The validity of the synthesis presented in Section 7, “Advanced Methods in Logistics Simulations”, is of higher importance. When this section was written, the models were contrasted in order to find out similarities and differences. This is somewhat similar to cross-case analysis or grounded theory, but in this research the researcher has participated in the cases, which decreases the objectivity of the observer. However, it can be argued that the observer needs to be close to the models, as only the modeller can understand the decisions made during the whole development process, and as such, the SIMDSS framework could have been incomplete without adequate exposure to the models. The SIMDSS framework needs more tests to verify whether it represents SIMDSSs in logistics accurately.

8.4 Further Research

The model presented in the sixth publication could be improved by analyzing the hinterland logistics more thoroughly. Potential further research includes using other

simulation tools (DES or ABM) to analyze the disruptions. The paper also encourages using a similar approach in other geographical areas or similar crisis situations in other than maritime supply chains.

Hinterland logistics is seen to be a possible extension in three different publications. The sixth publication has a very simplistic way of presenting hinterland capacity, so more research should be done regarding seaport hinterland modelling. It is an important factor regarding macro-logistics simulations and should not be overlooked in a DSS either.

Otherwise the results of the model might be too optimistic, which could lead to bad decisions by the decision-maker.

In the fourth publication, the potential avenues of research include connecting AS/RS spreadsheet simulation models to other simulation software. The simulation software could then simulate the solution proposed by the spreadsheet model. The spreadsheet model could be expanded to include other design parameters or modify the model to be a standalone application to any computer with spreadsheet software. Spreadsheet connectivity is an important issue, as spreadsheets cannot grasp dynamic behaviour very well. A good visualization is a good way to achieve buy-in, as it is easier to understand a visual representation of a system.

The third paper contains two potential avenues for further research. One possibility is to analyze in which situations the management of the company would find the model to be of best use. Another possibility is to study how the simulation model can be connected to other DSSs. These are both general issues in SIMDSSs. It is possible to improve the quality of DSSs, if it is known in which situations managers generally use simulations.

Also, connectivity to other DSSs would greatly enhance the validity of the model.

The suggestions for further research in the fifth publication include the need to have more simulation models with a hybrid approach and then analyzing what was difficult during the construction process. Also, the five interfaces proposed in the publication should be tested empirically to see whether it would improve the quality of the simulation models. In addition, an Expert System containing a hybrid simulation model should be compared against an Expert System with a one-method simulation model to see how much additional benefits hybridization provides in Expert Systems.

This thesis has only briefly analyzed the benefits achievable by combining simulation models with some sort of a GIS. The initial experiences are encouraging, however.

Especially ABM seems to benefit greatly in transportation research by using GIS as part of the model. GIS is a powerful tool if it is combined with optimization, and combining all three approaches (GIS, optimization and simulation) will likely yield extremely powerful analyses, which in turn need to be converted to a proper DSS for managers.

Another important issue is the use of various simulation approaches in transportation and warehousing; which simulation approach (SD, DES, ABM) should the modeller use in different situations? This issue was not fully covered in this thesis and needs to be studied further. Also, how great advantages does hybridization provide compared to using a single-method simulation? Hybridization was found to be highly beneficial, but it would be important to know whether the additional advantage justifies the use of multiple approaches, as it generally takes a longer time to construct the models. Generating good

“rules-of-thumb” to modellers would help to improve the quality of transportation and warehousing SIMDSS. Modellers themselves have a lot of tacit knowledge about the use of different simulation approaches with different cases, but a lot of work needs to be done

Otherwise the results of the model might be too optimistic, which could lead to bad decisions by the decision-maker.

Also, connectivity to other DSSs would greatly enhance the validity of the model.

This thesis has only briefly analyzed the benefits achievable by combining simulation models with some sort of a GIS. The initial experiences are encouraging, however.

to create explicit knowledge. This issue could be pursued by utilizing a focus group consisting of simulation experts who have good knowledge of all the major simulation approaches, as well as some knowledge about different optimization methods.

References

Acar, Y., Kadipasaoglu, S. and Schipperijn, P. (2010), “A decision support framework for global supply chain modelling: an assessment of the impact of demand, supply and lead-time uncertainties on performance”, International Journal of Production Research, 48(11), pp. 3245 – 3268.

Aissaoui, N., Haouari, M. and Hassini, E. (2007), ”Supplier selection and order lot sizing modeling: A review”, Computers and Operations Research, 34(12), pp. 3516 – 3540.

Anthony, R.N. (1965), Planning and Control systems: a Framework for Analysis. USA:

Graduate School of Business Administration, Harvard University.

Armaneri, Ö., Özda lu, G. and Yalçnkaya, Ö. (2010), “An integrated decision support approach for project investors in risky and uncertain environments”, Journal of Computational and Applied Mathematics, 234(8), pp. 2530 – 2542.

Arnott, D. and Pervan, G. (2008), “Eight key issues for the decision support systems discipline”, Decision Support Systems, 44(3), pp.657 – 672.

Ausubel, J.H. and Marchetti, C. (2001), “The evolution of transport”. The Industrial Physicist, 7(2), pp. 20 – 24.

Baker, P. and Canessa, M. (2009), “Warehouse design: A structured approach”, European Journal of Operational Research, 132(2), pp. 425 – 236.

Baker, P. and Halim, Z. (2007), “An exploration of warehouse automation implementations: cost, service and flexibility issues”, Supply Chain Management: An International Journal, 12(2), pp. 129 – 138.

Balci, O. (2003). “Verification, validation, and certification of modelling and simulation applications”, in Chick, S., Sánchez, P.J., Ferrin, D., and Morrice, D.J. (Eds), Proceedings of the 2003 Winter Simulation Conference, pp. 150 – 158.

Banks, J. (1998). “Principles of Simulation”, in Banks, J. (ed.), Handbook of Simulations – Principles, Methodology, Advances, Applications, and Practice. USA: John Wiley &

Sons, Inc.

Banks, J. and Gibson, R. (1997). “10 Rules for determining when simulation is not appropriate”. IIE Solutions, 29(9), pp. 30 – 33.

Banks, J., Carson II, J.S., Nelson, B.L and Nicol, D.M. (2005) Discrete-Event System Simulation. 5^th edition, USA: Pearson Education.

Bask, A.H. (2001), “Relationships among TPL providers and members of supply chains – a strategic perspective”, Journal of Business & Industrial Marketing, 16(6), pp. 470 – 486.

Beamon, B.M. (1998). “Supply chain design and analysis: Models and methods”, International Journal of Production Economics, 55(3), pp. 281 – 294.

References

Aissaoui, N., Haouari, M. and Hassini, E. (2007), ”Supplier selection and order lot sizing modeling: A review”, Computers and Operations Research, 34(12), pp. 3516 – 3540.

Anthony, R.N. (1965), Planning and Control systems: a Framework for Analysis. USA:

Graduate School of Business Administration, Harvard University.

Arnott, D. and Pervan, G. (2008), “Eight key issues for the decision support systems discipline”, Decision Support Systems, 44(3), pp.657 – 672.

Ausubel, J.H. and Marchetti, C. (2001), “The evolution of transport”. The Industrial Physicist, 7(2), pp. 20 – 24.

Baker, P. and Canessa, M. (2009), “Warehouse design: A structured approach”, European Journal of Operational Research, 132(2), pp. 425 – 236.

Banks, J. (1998). “Principles of Simulation”, in Banks, J. (ed.), Handbook of Simulations – Principles, Methodology, Advances, Applications, and Practice. USA: John Wiley &

Sons, Inc.

Banks, J. and Gibson, R. (1997). “10 Rules for determining when simulation is not appropriate”. IIE Solutions, 29(9), pp. 30 – 33.

Banks, J., Carson II, J.S., Nelson, B.L and Nicol, D.M. (2005) Discrete-Event System Simulation. 5^th edition, USA: Pearson Education.

Bask, A.H. (2001), “Relationships among TPL providers and members of supply chains – a strategic perspective”, Journal of Business & Industrial Marketing, 16(6), pp. 470 – 486.

Beamon, B.M. (1998). “Supply chain design and analysis: Models and methods”, International Journal of Production Economics, 55(3), pp. 281 – 294.

Beamon, B.M. (1999), “Measuring supply chain performance”, International Journal of Operations & Production Management, 19(3), pp. 275 – 292.

Ben-Daya, M., Darwish, M. and Ertogral, K. (2008), “The joint economic lot sizing problem: Review and extensions”, European Journal of Operational Research, 185(2), pp. 726 – 742.

Bertrand, JW.M. and Fransoo, J. (2002). “Operations management research methodologies using quantitative modelling”, International Journal of Operations &

Production Management, 22(2), pp. 241 – 264.

Bontekoning, Y.M., Macharis, C. and Trip, J.J. (2004), “Is a new applied transportation research field emerging?––A review of intermodal rail–truck freight transport literature”, Transportation Research Part A: Policy and Practice, 38(1), pp. 1 – 34.

Borshchev, A. and Filippov, A. (2004), “From System Dyanmics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools – Multimethod Simulation Software Tool Anylogic”, The 22^nd International Conference of the System Dynamics Society, July 25 – 29, Oxford, England

Bottani, E. and Montanari, R. (2010), “Supply chain design and cost analysis through simulation”, International Journal of Production Research, 48(10), pp.2859 - 2886.

Bowen, J.T. (2008), “Moving places: The geography of warehousing in the US”, Journal of Transport Geography, 16(6), pp. 379 - 387.

Box, G.E.P. (2012), quote [www-document], Wikquotes [Accesses 13.06.2012], Available from: http://en.wikiquote.org/wiki/George_E._P._Box

Brito, T.B., dos Santos Silva, R.C., Botter, R.C., Pereira, N.N. and Medina, A.C. (2010),

“Discrete event simulation combined with multi-criteria decision analysis applied to steel plant logistics system planning”, in Johansson, B., Jain, S., Montoya-Torres, J., Hugan, J. and Yücesan, E. (Eds.), Proceedings of the 2010 Winter Simulation conference, pp. 2126 – 2137.

Bucklin, L.P. (1965), “Postponement, speculation and the structure of distribution channels”, Journal of Marketing Research, 2(1), pp. 26 – 31.

Cagliano, A.C., DeMarco, Al., Rafele, C. and Volpe, S. (2011), “Using system dynamics in warehouse management: a fast-fashion case study”, Journal of Manufacturing Technology Management, 22(2), pp. 171 – 188.

Camm, J.D., Chorman, T.E., Dill, F.A., Evans, J.R., Sweeney, D.J. and Wegryn, G.W.

(1997), “Blending OR/MS, judgment, and GIS: Restructuring P&G’s supply chain”, Interfaces, 27(1), pp. 128 – 142

Cap Gemini (2007), 2007 Third-Party Logistics – Results and Findings from 12th Annual Study, <http://www.de.capgemini.com/m/de/tl/Third-Party_Logistics_2007.pdf>, [Retrieved: Jan 2011]