Factors Affecting Reverse Logistics Systems, Case Study Example

In Knemeyer’s (2002) study, he demonstrates the understanding of qualitative research designs in regards to logistics problems. He studies a reverse logistics system that will be used to give a second life to refurbished computers. He conducts interviews through a standardized approach that examine whether or not the stakeholders can participate in the methodology. The results showed that the “qualitative technique proved valuable in obtaining industry-sensitive stakeholder data, which allowed the researchers to more thoroughly analyze the feasibility of the proposed reverse logistics system” (Knemeyer, 2002, p. 1).

Shao et al’s (2010) study examines economical systems, and advanced methods they use in order to analyze it better. Shao et al. use modeling, multi-agent modeling, complex/adaptive system modeling, and qualitative modeling. Using a baseline of supply demands for a supply-chain store, Shao et al., apply these different methods in order to improve upon the system as a whole. The researchers used qualitative methods to improve the quantitative “forecasting results” (p. 59). By comparing the quantitative methods and the combined method the researchers found that the combined method was a more effective method as it “is far more accurate. Not only is the inventory cost lower, but the forecasting accuracy is also better” (p. 59).

Knemeyer (2002) conducted research using a qualitative research approach in order to examine factors that “surrounded the proposed reverse logistics model” (p. 462) (the paper delineates here and goes into the background history of qualitative research and the lack of support it’s fostered from the academic world – a far more unnecessary delineation than was needed for the paper). Knemeyer used a modified version of a long-standing customer visit program where a qualitative interview is “used to identify the special needs of stakeholders who could potentially participate in a reverse logistics system for EOL computers” (p. 462). The methodology proves to be a viable source of information for data collection and it permitted the researchers to collect visual data along with the interviews. Based on these interviews, the research team was able to compile a more comprehensive and thorough analysis of what permits for influences to a reverse logistics system for the EOL computers.

Shao et al. (2010) discussed the different methods of gathering data (sort of a history lesson on research studies) and posited the benefits and drawbacks of linear vs. nonlinear research methods as well as the benefits of qualitative research and theoretical vs. empirical studies. Their results show that the combination of modeling in research analysis was the most productive way to conduct research. Their method consisted of presenting the basic theory (e.g. quantitative forecasting and qualitative algorithms). Then, by using a bracket series, tested these models against and with one another; “we present the market demand qualitative analysis based on the maximum Lyapunov exponent forecasting results” (p. 59). The paper goes through each modeling research approach, defines it, then utilizes it. By combining such research methods the team was able to find that “in order to make accurate market demand forecasting, [they] …first used the maximum Lyapunov exponent forecasting methodto carry out quantitative forecasting about product demandaccording to the historic information. We selected 470 daysof demand information, which is the information given to thesuppliers by the retailers” (p. 62). The team came across some drawback to their method such as forecasted results not matching patterns established by former real demand data through their qualitative methodology.

Conclusion

For either research team, it seems that what was firstly important was taking a historic approach to their chronology by presenting former research methods and their applicability. It seems that both research teams learned that different approached were needed for various tasks as with Shao et al’s (2010) approach, they made an antithetical discovery in which qualitative forecasting as a method did not provide any quantitative demand data which was expected since the quantitative forecasting does do this. Knemeyer discovered that qualitative research methodology also allowed for some positive anomalies such as using data collected in interviews to inform the study on a very different level.

Either teams research methods were thoroughly thought-provoking and provide ample information for various uses and ways in which reverse logistics were beneficial in and outside of a vacuum (Shao’s research was far more inclusive than Knemeyer’s). With these two varying points of methodology it is obvious that Shao’s team would benefit from Knemeyer’sresearch as their approach was more of an umbrella approach in which all avenues of research were employed, vetted, and analyzed in order to come to the correct decision, or at least, to find out the positives and foibles of each design. Shao’s discovery (or re-discovery) of the bullwhip effect may be beneficial to Knemeyer’s study as it suggests that larger inventory or swings and is forecast driven, something that Knemeyer’s study showcases amply enough.

References

Knemeyer, A.M. (2002). A qualitative examination of factors affecting reverse logistics systems for end-of-life computers. International Journal of Physical Distribution & Logistics Management 32(6). Pp. 455-479.

MeBookshelf. (2005). Mechanical Engineering59. Pp. 1.

Shao, C., Wang, L., Xiao, L., &Jinliang, W. (2010). Qualitative phase space reconstruction analysis of supply-chain inventory times series. South African Journal of Science422.