A Methodology and Case Study to Assess SCOR-Make Agility Measures Under Uncertainties

A Methodology and Case Study to Assess SCOR-Make Agility Measures Under Uncertainties

Piyanee Akkawuttiwanich (International Academy of Aviation Industry, King Mongkut's Institute of Technology Ladkrabang, Thailand) and Pisal Yenradee (Sirindhorn International Institute of Technology, Thammasat University, Thailand)
Copyright: © 2020 |Pages: 16
DOI: 10.4018/IJKSS.2020070101
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Abstract

An assessment of agility is not an easy task since agility has been differently defined in literature, and it is not convenient to measure by experiment in practice. The objective of this paper is to propose a methodology to assess the agility performance under uncertainties based on level 1 of SCOR-Make process metric including the upside make flexibility (AG1.1), upside make adaptability (AG1.2), and downsize make adaptability (AG1.3). The proposed methodology consists of predictive models, which are a deterministic linear programming (LP) model and LP model with uncertainties, and algorithms to assess the agility measures. A case study of a bottled-water factory is conducted to demonstrate the application of the proposed methodology. The case study shows that the proposed methodology can effectively determine the agility measures. It can also be adapted to answer other agility related practical questions that are different from the SCOR definition.
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Introduction

Flexibility or Agility is defined as ability to response to a change in conditions in the shortest time span with no or little effect on cost and performance (Upton, 1995, Purvis et al., 2014). Many companies have viewed flexibility as a key strategic success in manufacturing systems. However, one of the most difficult tasks in performance assessment is the measurement of flexibility or agility. In practice, questions about flexibility are often raised because the requirements of the customers are uncertain. Companies that fail to recognize the degree of agility and cannot deliver products and services according to their commitment will be subject to loss of customers, reputation, and opportunity costs that could affect the profit and image of the company in the long run. To date, there are still attempts to define and propose different scientific approaches to measure the agility in manufacturing system. Many theories were established to describe the manufacturing agility (Zhang, 2011), and the agility in different industry (Yusuf et al., 2014). Xu et al., (2016) established the evolutionary mechanism to model the agility via the complex network theory and simulate the analysis with Matlab. For the assessment of agility, Koste et al., (2004) developed an instrument for measuring flexibility. Hop and Kawtummachai (2005), Chuu (2007), and Das and Caprihan (2008) presented the fuzzy logic approach to estimate flexibility. Wang (2009) developed the evaluation of agility using 2-tuple linguistic computing, and Seebacher and Winkler (2014) proposed a method that is readily applied to measure agility in a discrete manufacturing system.

There are reasons that the assessment of agility is not an easy task:

  • 1.

    There are many different definitions of agility, and they have pros and cons. When the definitions are different, it is difficult to develop a general method to assess the agility. Fortunately, APICS established the SCOR model, which is the reference model that is widely accepted as the global model for measuring supply chain efficiency. A part of the SCOR model is related to the agility;

  • 2.

    The agility is inconvenient to measure by a real experiment on a manufacturing system. It needs predictive models that can do what-if analysis;

  • 3.

    The agility measures are significantly affected by various types of uncertainties associated with the manufacturing system. Thus, the method to evaluate agility should incorporate the effect from environmental uncertainties.

From the reasons being mentioned above, the objectives of this paper are:

  • 1.

    To propose a practical method to evaluate the supply chain agility by focusing only on the SCOR-Make process;

  • 2.

    To demonstrate the applicability of the proposed method by implementing it to a case study of a bottled-water factory in Thailand.

The paper applies the following scopes and concepts to develop a method to assess the SCOR-Make agility measures:

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