How Continuous Improvement Can Support Logistics: A Reflection of Best Practices

How Continuous Improvement Can Support Logistics: A Reflection of Best Practices

Brian J. Galli (Assistant Professor and Graduate Program Director, Master of Science in Engineering Management Industrial Engineering, Hofstra University, USA)
Copyright: © 2018 |Pages: 23
DOI: 10.4018/IJoSE.2018010101
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This article introduces the basic idea of continuous improvement and its implications regarding logistics. Using a meta-analysis research methodology, the author analyzes the relationship and role that continuous improvement methods can play in the field of logistics. The study finds that the logistics field can benefit from using different forms of continuous improvement. These different methods to implement continuous improvement to logistics along with their pros and cons are discussed. Those methods include Lean, Six Sigma and PDCA cycle, which are explained in detail. Other methods are briefly introduced and reviewed. Some future ideas for further research are discussed in length. Although there are an infinite number of benefits in implementing continuous improvement to logistics, there are various limitations. Thus, this research will also explain each deficiency in depth.
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Before going into the detailed discussion, let’s first define logistics and how it correlates to continuous improvement. The word logistics is of French origin as “logistique,” or “art of calculating” (Myerson, 2004; Dannecker, 2015). Logistics is characterized as the itemized association and execution of a complex operation. In a general marketing prudence, logistics is the administration of the stream of things between the point of origin and the purpose of utilization, keeping in mind the goal to meet prerequisites (Rio, 2006). The assets overseen in logistics can incorporate physical things, such as nourishment, materials, creatures, and fluids, including time and data. The logistics of physical objects include reconciliation of the data stream; content taking care of, generation, bundling, stock, transportation, warehousing, and security.

Continuous improvement is defined as a progressing push to enhance items, administrations, or procedures (Dannecker, 2015). These endeavors can either be “incremental” change after some time or “leap forward” change at the same time. According to research by Millard and KaiNexus (2017), “Improvement-mode ‘Continuous improvement’ is a method for identifying opportunities for streamlining work and reducing waste” (Myerson, 2004). This practice was formalized by the popularity of Lean / Agile / Kaizen in the manufacturing and business industries. Now, it is used by thousands of companies around the world to identify savings opportunities (Dannecker, 2015; Rio, 2006; Myerson, 2004).

Here is a summary of the six principles that continuous improvement employs:

  • 1.

    Increases are based on small changes; not major paradigm shifts or new inventions;

  • 2.

    Ideas come from employees;

  • 3.

    Incremental improvements are typically inexpensive to implement;

  • 4.

    Employees take ownership and are accountable for improvement;

  • 5.

    Development is reflective;

  • 6.

    Improvement is measurable and potentially repeatable.

The main objective of this research study was to analyze the relationship and role that continuous improvement methods can play in the field of logistics. The study sought to explore the role that different continuous improvement methods play in logistics as well as the advantages and disadvantages of using these continuous improvement methods in the field of logistics.


Research Methodology

This paper employed Kothari’s (2009) book to decide which methods and techniques to utilize in studying the impact of logistics on continuous improvement. We chose to use a meta-analysis instead of solely a traditional literature review. Only recently has meta-analysis been applied to the social sciences with regards to experimental research (Vemer et al., 1989; Waldorf & Byrun, 2005; Weichselbaumer & Winter- Ebmer, 2005; Amato & Keith, 1991). The reason it has grown in popularity and application is that of its ability to evaluate an abundance of empirical evidence from studies that may oppose each other.

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