Figure 1. Methodology
The increasing intensity of competition in global markets in the present time is the major challenge that organizations are facing, especially the manufacturing ones. To remain at the competitive edge, the companies need to be continuously striving for innovative and effective material management techniques. Just In Time (JIT) is one of such material management techniques that increases the productivity of the enterprise by elimination of all kind of wastes through continuous improvement. This paper deals with the identification and ranking of performance indicators based on several criteria for successful JIT Implementation in Manufacturing Sector. A deterministic quantitative model based on Simple Additive Weighting (SAW) technique has been developed for identifying and ranking the performance indicators. This concept has not been applied previously in the available literature for the selection problem. Six performance indicators, namely-Lead Time Reduction, Productivity, Profit, Product Quality, Difficulty in Implementation, and Inventory have been considered for this study. Improper selection could affect a company's competitiveness in terms of the Productivity of its facilities and Quality of its products.
Just In Time (JIT) is an integrated set of activities formulated to achieve maximum production using minimal inventories. JIT is just not a technique or a set of techniques, but an overall philosophy which consists of both new and old techniques and offer a wide range of benefits by remodelling of present manufacturing system. JIT involves the elimination of waste and excess by acquiring resources and performing activities only as customers need them at the next stage in the process. It was first developed and perfected within the Toyota manufacturing plants by Taiichi Ohno as a means of meeting consumer demands with minimum delays. It has been widely reported that the proper use of JIT manufacturing has resulted in increase in quality, productivity and efficiency, improved communication and decreases in costs and wastes. The potential of gaining these benefits has made many organizations aware and consider this approach to manufacturing sector.
Multiple Criteria Decision Making (MCDM) is concerned with structuring and solving decision and planning problems involving multiple criteria. The purpose is to support decision makers facing such problems. Typically, there does not exist a unique optimal solution for such problems and it is necessary to use decision maker's preferences to differentiate between solutions. MCDM is divided into Multi-Objective Decision Making (MODM) and Multi-Attribute Decision Making (MADM).
MODM studies decision problems in which the decision space is continuous. A typical example is the mathematical programming problems with multiple objective functions. On the other hand, MADM concentrates on problems with discrete decision spaces. In these problems, the set of decision alternatives has been predetermined. A decision making process involves the following steps to be followed:
MADM is an approach applied to solve problems involving selection from among a finite number of alternatives. This method specifies how attribute information is to be processed in order to arrive at a choice. MADM methods require both inter- and intraattribute comparisons and involve appropriate explicit trade-offs. Numerous researchers have applied extensive MADM and MODM methods to present a feasible and effective solution to different real ranking problems ( Garg, et al., 2006 ; Garg, et al., 2007; Lin and Lin, 2007; Kwak et al., 2009 ; ; Kumar and Garg, 2010; Agarwal et al., 2011 ; Cheraghi et al., 2011; Jarial and Garg, 2012 ; Kalubanga, 2012; Kristianto et al., 2012; Gupta et al., 2013 ; Gupta et al., 2014 and Singh and Tiwari., 2015).
A JIT system aims at optimization of all the processes and procedures by continuous waste reduction. Against this backdrop, this paper aims at selection and classification of performance indicators and their elements in relevance to successful implementation of JIT in Manufacturing Sector.
Akbar et al. (2013) state that Just In Time (JIT) production system identified the hidden problems in the value chain and reduces the production waste of the system while increasing the throughout (Sales-Raw Material Cost). Even though the JIT system seems to be interesting and less complicated, it requires a lot of coordination with supply chain to avoid delays in the production schedule. This paper discusses in depth the implementation of JIT manufacturing. The objective was to acquaint the reader with the overall JIT concept and the factors necessary for its implementation; the concepts represent the ideal principles and methods of implementation.
Wen et al. (2014) proposed a solution framework based on discrete-event simulation, Sequential Bifurcation (SB) and Response Surface Methodology (RSM) to address a multi-response optimization problem inherent in an auto parts supply chain. The objective is to identify the most efficient operating setting that would maximize the logistics performance after the expansion of the assembly plant's capacity due to market growth. In the proposed framework, they first construct a comprehensive simulation as a platform to model the physical flow of the auto parts operations. They then applied the SB to identify the most important factors that influence system performance. To determine the optimal levels of these key factors, they employed RSM to develop meta models that best describe the relationship between key decision variables and the multiple system responses. They adapted the Derringer–Suich's desirability function to find the optimal solution of the meta models. Computational study shows that their method enables the greatest improvement on system performance.
Singh et al. [2015] discussed Knowledge-Based System (KBS) for selection of JIT elements pertaining to the automobile sector. JIT system is claimed as one of the best production systems which made Toyota Motors as best car manufacturer in the world. This study was conducted to explore and figure out the elements of JIT implementation in Indian automobile sector. In this study, JIT elements were ranked for automobile industries. The results may help in quick and right decision making, since an organization concentrates on a particular problem. The study also revealed the relative importance of various JIT elements to a particular performance measure and difficulty level of their implementation in Indian automobile sector.
Alcaraz et al. [2016] reported about 31 benefits obtained by companies after a successful JIT implementation. However, this research reduced the list by means of a data reduction technique to identify those essential benefits that must be pursued. On one hand, a validation process and descriptive analysis were carried out for every benefit by considering their median values as a measure of central tendency and interquartile range values as a measure of dispersion. On the other hand, data reduction was achieved by means of a factor analysis based on principal components and varimax rotation. Four main factors related to JIT benefits were identified after the factor analysis, which explain 67.27 % of total variance of data. Identified factors concern inventory management, production process, human resources, and economic benefits.
Darius and Ghorbanali (2016) discussed that Mixed-Model assembly lines are often used in manufacturing based on Just In Time techniques. The effective utilization of these lines required a schedule for assembling the different models to be determined. The objective was to minimize the total deviation of actual production rates from the desired production rates. Mathematical method with the optimization algorithm was proposed here to solve the given problem. To prove the efficiency of the proposed algorithm, a number of test problems were solved. The results showed that the proposed algorithm was an efficient and effective algorithm which gave better results with the large problem sizes. This paper presented a practical procedure to minimize total product variation rates, and easy to use by practitioner.
Figure 1 presents the methodology followed in the present paper. Simple Additive Weighting (SAW) technique is the simplest and still the widest used MADM technique. Here, each attribute is given a weight, and the sum of all weights must be one. Each alternative is evaluated with respect to every attribute.
Figure 1. Methodology
SAW technique is employed for ranking of the six performance indicators based on 15 JIT elements. On the basis of the survey, the data is collected and simplified as shown in Table 1.
Table 1. Performance Indicators vs. JIT Elements in SAW Technique
Simple Additive Weighting (SAW) Technique is applied for ranking of six Performance Indicators based on fifteen criteria which will be helpful in decision making process. Here, each attribute is given a weight, and sum of all weights must be equal to one. Each alternative is assessed with regard to every attribute. In this paper, all the JIT elements carry equal weightage, i.e., 0.0667(1/15). The overall or composite performance score of an alternative is given by Equation (1).
It is used only when the decision attributes can be expressed in identical units of measure. However, if all the elements of decision table are normalized, then SAW can be used for any type and any number of attributes. In that case, Equation (1) will take the following form:
Where (mij) normal represents the normalized value of mij and Pi is the overall or composite score of alternative Ai. The alternative with the highest value of Pi is considered as the best alternative.
The attribute can be beneficial or non- beneficial. When objective values of the attribute are available, normalized values are calculated by (mij)K/(mij) L , where (mij)K is the measure of the attribute for the Kth alternative, and (mij) L is the measure of the attribute for the Lth alternative that has the highest measure of the attribute out of all the alternatives considered.
The ratio is valid for beneficial attribute only. A beneficial attribute means its higher measures are more desirable for the given decision making problem. By contrast, nonbeneficial attribute is that for which the lower measures are desirable, and the normalized values are calculated by (mij)K/(mij)L . Table 2 shows the Normalized Matrix and Table 3 shows the Weighted Normalized Matrix.
If the restriction that the sum of all weights is equal to one is relaxed, then Equation (3) can be used and this method is called a simple multiple attribute rating technique.
Table 2. Normalized Matrix
Table 3. Weighted Normalized Matrix
Table 4 shows the ranking of Performance Indicators based on several criteria which would be beneficial in developing a DSS (Decision Support System) for successful implementation of JIT in Manufacturing Sector.
Table 4. Weighted Sum Matrix
The major finding of the work has been the identification of JIT criterion for the selection of Performance Indicators. This has been done after extensive study of literature and establishing the relevance of Performance Indicators of Supply Chains in Business Development. The next contribution has been the development of a DSS for evaluation, selection and ranking of Performance Indicators of Supply Chain for Manufacturing Industries using DBA & SAW Techniques. As far as practical relevance is considered, the work can be highly useful because it facilitates selection of right JIT elements for a specific Performance Indicator of Supply Chain which is one of the major problems in JIT context.