Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 78 AN INVESTIGATION ON PRODUCTION PLANNING APPROACH FOR
REMANUFACTURING IN SMALL AND MEDIUM SCALE ENTERPRISES (SMES) THROUGH ISM AND AHP
Priti Shah
Department of Industrial Engg. & Management, Takshshila Institute of Engineering &
Technology Jabalpur (M.P.) Prof. Alok Agrawal
Department of Mechanical Engineering, Takshshila Institute of Engineering & Technology Jabalpur (M.P.)
1 INTRODUCTION
This thesis deals with the use of principles of production planning and controlling for those industries which remanufactures the used product and resale in the market. These are the small and medium scale enterprises (SME), which are having lack of resources and funds therefore faces lots of challenges and market pressure. This is an endeavour to tackle these challenges and present a mathematical model to hierarchically attempt these challenges.
The following chapter will introduce the reader to the background of the thesis and the focus the topic of remanufacturing.
1.1 Production Planning and Control The need of production planning arises out of the observation that many SMEs loses its customers due to lack of proper planning of production and inventory at critical stages. The shortage of material, its planning and scheduling results in heavy losses. The research in the thesis focus upon the challenges in production planning of remanufactured product on four core areas viz. Forecasting, logistics, shop floor planning/ scheduling and inventory control and management.
Production planning and control (PPC) activities aim to define what, how much, and when to produce, buy, and deliver so that the company can match manufacturing performance with customer demands. Therefore, PPC is a value-adding process of the manufacturing activity. PPC needs to continually adapt to operational and strategic environments, complex customer requirements, and new supply chain opportunities. Thus, PPC needs to be dynamic, adaptive, and integrative. The rapid industrial environmental changes impose an evolutionary and integrative perspective in operations management
and, consequently, in the PPC function.
Thus, the PPC function considers, for example, material requirements planning (MRP), enterprise resource planning (ERP), just-in-time manufacturing, and collaborative planning, forecasting, and replenishment, among other activities.
With the development of economy, many environmental and resource issues related to the industrialization are emerging, such as the acceleration of global climate warming and the resource shortage. These environmental and social pressures have led to the rise of one advanced manufacturing paradigm–
sustainable manufacturing (SM), which produces products in a sustainable manner while maintaining global competitiveness and coping with environmental and resource problems.
1.2 The concept of sustainable manufacturing
In recent decades, “sustainable manufacturing” had been an increasing interest topic due to the growth of public awareness with the end of life products and its unsustainable disposal method.
Sustainable manufacturing was derived from the concept of sustainable development. It was first introduced in the 1980s to resolve the economic, environmental, and social concerns simultaneously in the human development process. Today, there is no standard definition of sustainable manufacturing among scholars and literature as different researchers defined sustainable manufacturing in a different perspective of view.
1.3 Research Objectives
The main objective of this research is to present an approach for small and medium enterprises (SMEs) involved in remanufacturing to select the scheduling
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 79 of inventory, shop floor planning and
production planning. vendors for medium scale and small scale enterprise not only on the basis of lowest one in cost but with respect to other various criteria. Another one of the most important objective of this research is to capture both the subjective and the objective evaluation measures in order to solve process selection especially when different organizations have different combinations of qualitative and quantitative criteria and sub-criteria. To meet that, it is necessary to develop the model to tackle the complexities in remanufacturing business setting based on Interpretive Structural Model (ISM) since it is a simple, interpretive and most suitable one for these kinds of selection problem. Another approach used in this research to validate the model is Analytical Hierarchy Process (AHP).
The other objectives of this research related to developing ISM model and AHP validation are:
To determine the most important priorities to be adopted in the production planning process of remanufactured goods.
To enable decision makers to examine the weightage of each criterion by comparing them with respect to appropriate criteria and inter- relationship amongst them.
To calculate the driving and dependence power of each criterion.
To establish the framework that helps the remanufacturing SMEs to decide the priorities in production planning.
2 LITERATURE REVIEW
This chapter of thesis deals with the review of literature on production planning particularly for remanufacturing. We present the comprehensive state-of-the-art review of the literature related to production planning and control, and discuss the specific needs for future research and also the factors that complicate production planning and control for remanufacturing firms in the next section.
To ensure the quality of the literature, survey has been carried out from the peer reviewed journal articles only. The search have been conducted using various keywords in combination like Production planning and control, production planning and control +
inventory management, production planning + remanufacturing from Emerald and Science Direct.
The review in this chapter is divided into three parts the roadmap of which is shown in figure 2.1.
Fig. 2.1: Roadmap for literature review 3 RESEARCH DESIGN
In an attempt to achieve the objectives of this research, the methodology adopted for the research consisted of five main stages:
Stage 1: Selection of Criteria from literature and experts‟ interviews. The available literature on production process
and remanufacturing were
comprehensively searched and studied to identify the major complicating criteria.
These criteria and methods were described in chapter two.
Stage 2: Rating of Criteria by using questionnaire filled by experts from industry and academia. The selection criteria that were identified in stage one were used as a basis for formulating the first questionnaire form. Since there was no research done in the remanufacturing industries, located at the central India in the field of production planning, criteria arbitration and assessment were conducted, where remanufacturing experts, purchasing managers and academics were contacted. The main objectives of this stage were:
To make sure that the important criteria for the production planning and inventory management of remanufacturing were identified, and comprehensively covered.
To add more possible important criteria which were not included.
Finalize the questionnaire form.
Stage 3: Development of hierarchy. For this, the questionnaire forms were distributed to panel of twenty five experts from academics, research scholars and experts from industry. Also the questionnaire was distributed to the five suppliers of photocopying machines. They were asked to rate the selection criteria in
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 80 order of importance and to assess the
current system in selecting the suppliers.
Stage 4: Analysis and Synthesis of questionnaire have been conducted and the ISM based model has been developed based on the driving and dependence power of each criteria of PPC of remanufacturing.
Stage 5: The validation of ISM based model have been conducted in this stage using AHP technique. The pair-wise comparison of each criterion has been conducted and weightage of each criterion is calculated. The consistency of the result is calculated. It includes the analysis of the result whether the consistency ratio is matching with the guidelines according to the Saaty.
Stage 6: Final decision making model and roadmap to tackle the complicating characteristics of the production planning for remanufacturing is developed.
Figure 3.1 The methodology adopted for the research.
4 INTERPRETIVE STRUCTURAL MODELING
This chapter explains how the data from the survey were analyzed and discusses the results of this analysis. The selection criteria in order of importance are identified and the first objective of the research was accomplished.
Enlargement of the supplier selection model includes the establishment of supplier selection criteria, development of the respondent sample base, construction of the ISM based model, design of an evaluation questionnaire, respondent interview, analysis of the questionnaire result. This chapter discusses the development and formulation of the supplier selection model (SSM).
4.1 Data Measurement
In order to be able to select the appropriate method of analysis, the level of measurement must be understood. For each type of measurement, there is/are an appropriate method/s that can be
applied and not others. The selected criterion and sub-criterions are mapped in ISM based model. The process of model development is explained in article 4.3 and the illustration of model development for criteria and same is followed for sub- criterions in table 4.2.
4.2 ISM methodology and model development
ISM is a qualitative tool that was developed by Warfield (1974) to understand and comprehend the complex interrelationships between elements.
Through a sequential and systematic methodology, it aims at developing an arrangement, wherein a set of elements related directly and indirectly are structured into a model, after analyzing the complex relationships amongst them.
The model ultimately depicts order and direction on the complexity of relationships amongst the various elements, based on primacy, priority, and, cause and effect. It ultimately leads to a portrayal of the direct and indirect relationships among the various elements in a system, through a multi-level structural model.
The various steps involved in ISM are as follows:
Step-I identifying of the criterions that are applicable to the problem. This can be done by any group technique.
Step-II Establishing the contextual relationship between the criterions for relationship indication.
Step-III Developing a structural self- interaction matrix (SSIM) of the criterions, this stand for pair-wise relationship between the criterions of the system.
Step-IV Developing a reachability matrix (1s and 0s) from the SSIM format (V, A, X and O), and make sure the matrix for transitivity (if element A is related to B and B is related to C, then A is necessarily related to C). Then on the basis of reachability matrix ISM initial digraph formed.
Step-V Partitioning the reachability matrix into different levels according to reachability and antecedent set for each criterion.
Step-VI Based on the relationships in the reachability matrix, drawing a final (digraph) and removing the transitive links.
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 81 Step-VII Convert final digraph into an
ISM-based model by substituting criterions nodes with the statements.
Step-VIII Review for checking conceptual inconsistency and make the necessary modifications of the ISM model.
4.2.1 Structural Self-interaction Matrix (SSIM)
ISM methodology suggests the use of the expert opinions based on various management techniques such as brain storming, nominal group technique etc in developing the contextual relationship among the variables. Keeping in mind the
contextual relationship for each criterion, the existence of a relation between any two criterions (i and j) and the associated direction of the relation are questioned.
Four symbols are used to denote the direction of relationship between the criterions (i and j):
V = Criterion i will help achieve criterion j;
A = Criterion j will be achieved by criterion i;
X = Criterion i and j will help achieve each other; and
O = Criterion i and j are unrelated.
Table 4.1 Complexity characteristics details for ISM analysis S. No. Complexity No. Details of Complexity characteristics
1 CC-1 The uncertain timing and quality of returns 2 CC-2 The need to balance returns with demands 3 CC-3 The disassembly of returned products
4 CC-4 The uncertainty in materials recovered from returned items 5 CC-5 The requirement for a reverse logistics network
6 CC-6 The complications of material matching restriction
7 CC-7 The problems of stochastic routings for materials and highly variable processing times
Table 4.2 Self interaction matrix
Criterion No. CC7 CC6 CC5 CC4 CC3 CC2
CC1 A A A A A X
CC2 A A O O O
CC3 V V O V
CC4 V V O
CC5 A A
CC6 X
CC7 X
4.2.2 Reachability matrix
To develop the reachability matrix from SSIM, two sub-steps were followed. In the first sub-step, the SSIM table is converted into the initial reachability matrix by transforming the information of each cell of SSIM into binary digits „0s‟ and „1s‟ in the initial reachability matrix.
The rules for the substitution are as follows.
If the (i, j) entry in the SSIM is V, then the (i, j) entry in the reachability matrix becomes 1 and the (j, i) entry becomes 0.
If the (i, j) entry in the SSIM is A, then the (i, j) entry in the reachability matrix becomes 0 and the (j, i) entry becomes 1.
If the (i, j) entry in the SSIM is X, then the (i, j) entry in the reachability
matrix becomes 1 and the (j, i) entry also becomes 1.
If the (i, j) entry in the SSIM is O, then the (i, j) entry in the reachability matrix becomes 0 and the (j, i) entry also becomes 0.
Entry in SSIM V A X O Entry in reachability matrix
(i, j) 1 0 1 0
Entry in reachability matrix
(j, i) 0 1 1 0
In the second sub-step, final reachability matrix is obtained by incorporating the transitivity as explained in step V of the ISM methodology. The final reachability matrix will then consist of some entries from the pair-wise comparison and some inferred entries. After incorporating the transitivity concept as described earlier, the final reachability matrix is obtained.
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 82 Table 4.3 Reachability matrix
Criterion No. CC1 CC2 CC3 CC4 CC5 CC6 CC7
CC1 1 1 0 0 0 0 0
CC2 1 1 0 0 0 0 0
CC3 1 0 1 1 0 1 1
CC4 0 0 0 1 0 1 1
CC5 1 0 0 0 1 0 0
CC6 1 1 0 0 1 1 1
CC7 1 1 0 0 1 1 1
4.3.3 Level partitions
Level partitions are consisting of Reachability set, Antecedent set, Intersection Set and ranking of levels.
Based on the suggestions of Warfield and Sage, the reachability and antecedent set for each criterion are obtained from final reachability matrix. The reachability set for a particular criterion consists of the criterion itself and the other criterions, which it may help to achieve. The antecedent set consists of the criterion
itself and the other criterion, which may help in achieving them. Subsequently, the intersection of these sets is derived for all criterions. The criterion for which the reachability and the intersection sets are the same is assigned as the top-level criterion in the ISM hierarchy as it would not help achieve any other criterion above their own level. After the identification of the top-level element, it is discarded from the list of remaining criterions and the process continues.
Table 4.4 Reachability matrix after removing transitivity Criterion No. CC1 CC2 CC3 CC4 CC5 CC6 CC7 Driving power
CC1 1 1 0 0 0 0 0 2
CC2 1 1 0 0 0 0 0 2
CC3 1 1* 1 1 0 1 1 6
CC4 1* 1* 0 1 1* 1 1 6
CC5 1 1* 0 0 1 0 0 3
CC6 1 1 0 0 1 1 1 5
CC7 1 1 0 0 1 1 1 5
Dependent Power 7 7 1 2 4 4 4 29
Table 4.5 Level partition
Criterion No. Reachability Set Antecedent Set Intersection Level
CC1 1,2 1,2,3,4,5,6, 7 1,2 Level - I
CC2 1,2 1,2,3,4,5,6, 7 1,2 Level - I
CC3 1,2,3,4,5,6 3 3
CC4 1,2,4,5,6,7 3,4 4
CC5 1,2,5 4,5,6,7 5
CC6 1,2, 5,6, 7 3,4,6,7 6, 7
CC7 1,2, 5,6, 7 3,4,6,7 6, 7
Table 4.6 Level partition
Criterion No. Reachability Set Antecedent Set Intersection Level
CC3 3,4,5,6 3 3
CC4 4,5,6,7 3,4 4
CC5 5 4,5,6,7 5 Level - II
CC6 5,6, 7 3,4,6,7 6, 7
CC7 5,6, 7 3,4,6,7 6, 7
Table 4.7 Level partition
Criterion No. Reachability Set Antecedent Set Intersection Level
CC3 3,4,6 3 3
CC4 4,6,7 3,4 4
CC6 6, 7 3,4,6,7 6, 7 Level - III
CC7 6, 7 3,4,6,7 6, 7 Level - III
Table 4.8: Level partition
Criterion No. Reachability Set Antecedent Set Intersection Level
CC3 3,4 3 3
CC4 4 4 4 Level - IV
Table 4.9 Level partition
Criterion No. Reachability Set Antecedent Set Intersection Level
CC3 3 3 3 Level – V
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 83 4.3.4 Formation of ISM initial digraph
The structural model is generated from initial reachability matrix. If there is a relationship between the criterions i and j, this is presented by an arrow which points from i to j. This graph is called as an initial directed graph, or initial digraph. As there is a relationship between the criterions, an arrow represents this. For example there is a relationship between criterion 1 and criterion 5, i.e. criterion 1 is achieved by criterion 5, this are represented by arrow which points from 5 to 1.
Figure 4.1 Initial ISM Diagraph 4.3.5 Formation of ISM final digraph
and model
From the final reachability matrix, the structural model is generated by means of vertices, or nodes, and edges. A relationship between the criterion i and j is shown by a directional arrow which from criterion i to criterion j. This graph is called a directed graph or digraph. After removing the transitivity, the digraph is finally converted into an ISM.
Figure 4.2 Final ISM diagraph
4.4 Summary
The ISM allows group decision making, where group members can use their experience, values and knowledge to break down a problem into a hierarchy and solve it by the ISM steps.
Brainstorming and sharing ideas and insights (inherent in the use of Expert Choice in a group setting) often leads to a more complete representation and understanding of the issues. Group discussion is the preferred approach when judgments must be made about the value of different alternatives. In fact, brainstorming and sharing ideas and insights often lead to a more complete representation and understanding of the issues than would be possible for a single decision-maker.
5 ANALYTIC HIERARCHY PROCESS Analytic Hierarchy Process is a decision- making technique developed in the 1970s by mathematician Thomas L. Saaty, a professor at the University of Pittsburgh‟s Katz School. AHP can be used in making decisions that are complex, unstructuredused in making decisions that are complex, unstructured described by these criteria do not fit in a linear framework; they contain both physical and psychological elements.AHP allows better, easier and more efficient- identification of selection criteria, their weighting and analysis. It reduces drastically the decision cycle. AHP allows organization to minimize common pitfalls of decision making process, such as lack of focus, planning, participation or ownership which ultimately are costly distraction that can prevent teams from making the right choice. AHP provides a method to connect that that can be quantified and the subjective judgment of the decision maker in a way that can be measured. In applying AHP to benchmarking, Parotid describes the process in three broad steps: the description of a complex decision problem as a hierarchy, the prioritization procedure and the calculation of results.AHP is “a method of breaking down a complex, unstructured situation into its components parts; arranging these parts, or judgments on the relative importance of each variable; and synthesizing the judgments to determine which variables have the highest priority
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 84 and should be acted upon to influence the
outcome of the situation”
5.1 The AHP Applications
The applications of AHP to complex decision situations have numbered in the thousands and have produced extensive results in problems involving alternative selection, planning, resource allocation, and priority setting. AHP can also be applied to a group decision where judgments made by all the individuals in a group are combined. Broad areas where AHP has been successfully employed include: selection of one alternative from many; resource allocation; forecasting;
total quality management; business process re-engineering; quality function deployment, and the balanced scorecard.
5.2 AHP Steps
The AHP approach, as applied to the supplier selection problem, consists of the following five steps:
1. Specify the set of criteria for evaluating the supplier‟s proposals, and then construct a decision hierarchy by breaking down the decision problem into a hierarchy of its elements.
2. Obtain the pair-wise comparisons of the relative importance of the criteria in achieving the goal, and compute the priorities or weights of the criteria based on this information.
3. Obtain measures that describe the extent, to which each supplier achieves the criteria, then determine whether the input data satisfy a consistency test; if not, redo the pair- wise comparisons.
4. Using the information in step 3, obtain the pair-wise comparisons of the relative importance of the suppliers with respect to the criteria, and compute the corresponding priorities.
5. Using the results of steps 2 and 4, a final priority vector of each supplier is obtained by synthesizing all the
priority vectors to achieve the goal of the hierarchy.
Figure 5.1 General guidelines for constructing hierarchy
The completed hierarchy can be modified as needed to accommodate new and important elements that were not included during the development of the hierarchy. The overall depth of detail of the hierarchy depends on the person‟s experience and familiarity with the subject, which will determine what to include and where to include it. When constructing hierarchies one must include enough relative detail.
5.2.1 Pair-wise Comparison:
Once the hierarchy has been structured, the next step is to establish the priorities for elements (criteria and alternatives) presented in the hierarchy. The AHP uses the pair-wise comparison to do this. The first step is to make pair-wise comparison. It is to compare the elements in pairs against a given criterion.
A set of comparison matrices of all elements in a level of the hierarchy with respect to an element of the immediately higher level are constructed so as to prioritize and convert individual comparative judgments into ratio scale measurements. The preferences are quantified by using a nine-point scale.
The meaning of each scale measurement is explained in Table 5.1
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 85 Table 5.1 Scale of preference between two elements
Intensity of
importance Definition Explanation
1 Equal importance Two activities contribute equally to the objective.
3 Moderate Importance Experience and judgment slightly favour one activity over another
5 Strong Importance Experience and judgment strongly favour one activity over another.
7 Very strong importance An activity is favoured very strongly over another; its dominance demonstrated in practices.
9 Extreme importance The evidence favouring one activity over another is of the highest possible order of affirmation.
2,4,6,8 For compromises between the
above values. Sometimes one needs to interpolate a compromise judgment numerically because there is no good word to describe it.
5.2.2 The Synthesis of Priorities:
The pair-wise comparisons generate a matrix of relative rankings for each level of the hierarchy. The number of matrices depends on the number of elements at each level. The order of the matrix at each level depends on the number of elements at the lower level that it links to. After all matrices are developed and all pair-wise comparisons are obtained, eigenvectors or the relative weights (the degree of relative
importance among the elements), global weights, and the maximum Eigen-value (emax) for each matrix are calculated.A copy of detailed solution and procedure is enclosed in to the section of appendices.
Table 5.2 shows the value of the random consistency index (RCI) for matrices of order 1.10 obtained by approximating random indices using a sample size of 500.
Table 5.2 Average Random Consistency Index (RCI) based on matrix size
N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
RCI 0 0 0.52 0.89 1.11 1.25 1.35 1.4 1.45 1.49 1.51 1.52 1.56 1.57 1.59 According to Saaty, the acceptable CR
range varies according to the size of matrix i.e. 0.05 for a 3-by-3 matrix, 0.08 for a 4-by-4 matrix and 0.1 for all larger matrices, n ≥ 5. If the value of CR is equal to, or less than that value, it implies that the evaluation within the matrix is acceptable or indicates a good level of consistency in the comparative judgments represented in that matrix. In contrast, if CR is more than the acceptable value, inconsistency of judgments within that matrix has occurred and the evaluation process should therefore be reviewed, reconsidered and improved. In general, a CR value of 10% or less is acceptable. But any CR value of more than 10% is not acceptable and the judgments in D matrix table should be reconsidered to resolve inconsistency judgments provided in pair wise comparison. An approximation to the Eigen-value can be calculated by multiplying the total of each column in a judgment matrix by its corresponding vector of weights. The approximation is exact when the exact vector of priorities is used. The consistency index of the entire hierarchy is obtained by multiplying the Consistency Index of each matrix by the
priority of the criterion used for the comparisons, and all such quantities. To check the consistency of the entire hierarchy, compare the CI of the hierarchy with its counterpart when the consistency indices of all matrices are replaced by average random judgment consistency indices for matrices of the same size (Table 5.2). The CR should not exceed l0%. If it is more than l0%, then the quality of the judgments should be improved, perhaps by revising the manner in which the questions are asked in making the pair-wise comparisons. If this fails to improve consistency, then it is likely that the problem should be more accurately structured, that is, grouping similar elements under more meaningful criteria. A return to priority setting would be required, although only the problematic parts of the hierarchy may need revision.
5.3 Advantages of AHP
To deal with complex problems, we must get away as much as possible from complicated manners and methods anticipate the solution of complex problems. Unlike many traditional
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 86 decision-making methods used to deal
with these complex problems, the proposed process should not require a constant specialized expertise to layout the appropriate steps leading to the required solutions. The AHP approach offers several advantages over the other techniques, despite certain drawbacks such as rank reversal and the number of judgment elicitations that are needed.
5.4 Disadvantages of AHP A number of criticisms have been launched at AHP over the years:
Use of this statistical method is clearly not straightforward for most users and it makes the process quite cumbersome.
Consensus may need to be reached by reviewing of the models with team members, i.e. it may be a time- consuming activity.
There is no single hierarchy for most supplier selection problems.
Heavily based on the principle that experience, knowledge and judgment of decision-makers are at least as valuable as the data they use, but human judgment is always subjective and has bias towards their own intuitive thought processes.
Characteristic property of AHP is that it is fully compensatory that this might not always be realistic. In addition, the assumption of comparability is not valid due to lack of information or unwillingness to compare two alternatives with respect to some criterion, i.e. it is costly to obtain the necessary information.
5.5 Making Group Decisions:
The AHP allows group decision making, where group members can use their experience, values and knowledge to break down a problem into a hierarchy and solve it by the AHP steps.
Brainstorming and sharing ideas and insights (inherent in the use of Expert Choice in a group setting) often leads to a
more complete representation and understanding of the issues. Group discussion is the preferred approach when judgments must be made about the value of different alternatives. As stated by the well-known scientist Saaty. The AHP can be used successfully with a group. In fact, brainstorming and sharing ideas and insights often lead to a more complete representation and understanding of the issues than would be possible for a single decision-maker.
But group sessions can also pose special problems." When the analytic hierarchy process is used in a group session, the group members structure the problem, provide the judgments, debate the judgments, and make a case for their values.
5.6 Performing Pair-wise Comparisons:
After constructing the hierarchy, pair- wise comparisons were performed systematically to include all the combinations of criteria and sub-criteria relationships. The criteria and sub- criteria were compared according to their relative importance with respect to the parent element in the adjacent upper level. Prior to the study, it is hoped to go through pair-wise comparisons together with the decision makers. It was not possible due to the differences among the schedule of the managers. Hence, questionnaire including all possible pair- wise comparison combinations were distributed to the decision makers. They first made all the pair-wise comparisons using semantic terms from the fundamental scale and then translated them to the corresponding numbers, separately. After performing all pair wise comparisons by the decision-makers, the individual judgments were aggregated using the Eigen-vector approach as Saaty suggested. The judgments were based upon the gathered information through the questionnaires. The results are then combined.
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 87 Table 5.3 Pair-wise comparison of complicating characteristics
CC-1 CC-2 CC-3 CC-4 CC-5 CC-6 CC-7
CC-1 1 1 1/9 1/7 1/3 1/5 1/5
CC-2 1 1 1/9 1/7 1/3 1/5 1/5
CC-3 9 9 1 3 7 5 5
CC-4 7 7 1/3 1 5 3 3
CC-5 3 3 1/7 1/5 1 1/3 1/3
CC-6 5 5 1/5 1/3 3 1 1
CC-7 5 5 1/5 1/3 3 1 1
5.7 Pair-Wise Analysis
According to Saaty (1980) the judgment of a purchasing manager is accepted if CR ≤ 0.10. The results of the pair-wise comparison of deliberations were presented back to the expert group. The mean values of the Eigenvector comparisons were calculated. The inconsistencies in the results were
explained. Discussions were held on the inconsistencies. The results of each evaluator were sent back again to be reconsidered. They were requested also to carefully evaluate the weighted vector compared to other purchasing manager‟s results and to the overall average results.
All purchasing managers were free to make suitable amendments.
Table 5.4 Pair-wise comparison of complicating characteristics
CC-1 CC-2 CC-3 CC-4 CC-5 CC-6 CC-7
CC-1 1.000 1.000 0.111 0.143 0.333 0.200 0.200 CC-2 1.000 1.000 0.111 0.143 0.333 0.200 0.200 CC-3 9.000 9.000 1.000 3.000 7.000 5.000 5.000 CC-4 7.000 7.000 0.333 1.000 5.000 3.000 3.000 CC-5 3.000 3.000 0.143 0.200 1.000 0.333 0.333 CC-6 5.000 5.000 0.200 0.333 3.000 1.000 1.000 CC-7 5.000 5.000 0.200 0.333 3.000 1.000 1.000 31.000 31.000 2.098 5.152 19.667 10.733 10.733 Table 5.5 Normalized matrix for complicating characteristics
CL-1 CL-2 CL-3 CL-4 CL-5 CL-6 CL-7 Weightage Rank CL-1 0.032 0.032 0.053 0.028 0.017 0.019 0.019 0.028 5 CL-2 0.032 0.032 0.053 0.028 0.017 0.019 0.019 0.028 5 CL-3 0.290 0.290 0.477 0.582 0.356 0.466 0.466 0.418 1 CL-4 0.226 0.226 0.159 0.194 0.254 0.280 0.280 0.231 2 CL-5 0.097 0.097 0.068 0.039 0.051 0.031 0.031 0.059 4 CL-6 0.161 0.161 0.095 0.065 0.153 0.093 0.093 0.117 3 CL-7 0.161 0.161 0.095 0.065 0.153 0.093 0.093 0.117 3
1.000 λmax 0.883 0.883 0.878 1.191 1.162 1.260 1.260 7.515
Table 5.6 Weightage calculation and Ranking of each complicating characteristics
CL-1 CL-2 CL-3 CL-4 CL-5 CL-6 CL-7 Weightage Rank
CL-1 0.032 0.032 0.053 0.028 0.017 0.019 0.019 0.028 5
CL-2 0.032 0.032 0.053 0.028 0.017 0.019 0.019 0.028 5
CL-3 0.290 0.290 0.477 0.582 0.356 0.466 0.466 0.418 1
CL-4 0.226 0.226 0.159 0.194 0.254 0.280 0.280 0.231 2
CL-5 0.097 0.097 0.068 0.039 0.051 0.031 0.031 0.059 4
CL-6 0.161 0.161 0.095 0.065 0.153 0.093 0.093 0.117 3
CL-7 0.161 0.161 0.095 0.065 0.153 0.093 0.093 0.117 3
0.88 0.88 0.88 1.19 1.16 1.26 1.26 λmax =7.52
5.8 Consistency Test
CI = (λmax – n)/ (n-1) = 0.09 CR = CI/ RI = 0.0636 < 0.1 5.9 Summary
The objective of AHP was to validate the ISM based model developed based on the driving and dependence power of each complicating characteristics of production planning for remanufacturing. The AHP analysis has been conducted based on the feedback from the panel of experts. The
analysis of feedback revealed the fact that the hierarchy developed through the ISM analysis resembles with the weightage calculated through AHP analysis.
6 DISCUSSION, CONCLUSION AND FUTURE SCOPE
When an organization is dealing with the remanufacturing of goods, the major challenges are to plan the shop floor activities and scheduling of inventory.
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 88 Selecting the best supplier to deliver a
goods or service, scheduling of the inventory, shop floor planning, balancing of demands from the market, selection of the parts for retrofitting and forecasting decision can often be very complex. All the above said problems are multi-criteria problems which have many qualitative and quantitative concerns. This research has presented the ISM as a decision analysis tool in tackling the challenges of production planning. An ISM model was proposed to select the best hierarchical approach to tackle the complexities in remanufacturing production planning and inventory control. The hierarchical approach developed by ISM based model has been validated through the AHP model, which has assigned weightage to each complexity. This chapter summarized the major findings, conclusion and recommendations that were derived from combined results of literature review, methodology and the case study.
6.1 Major Findings
As results of the data analysis about the supplier selection process in engineering institution were analyzed, the following findings were noted:
1) The research revealed that there are a numerous case studies and production planning model for remanufacturing are available in the literature. Most of them accepts that the Seven complexities arises in the
production planning of
remanufacturing under four criteria viz. Forecasting, logistics, scheduling shop floor control and inventory control and management.
2) The Seven complexities in production planning in remanufacturing business setting are:
The uncertain timing and quality of returns
The need to balance returns with demands
The disassembly of returned products
The uncertainty in materials recovered from returned items
The requirement for a reverse logistics network
The complications of material matching restriction
The problems of stochastic routings for materials and highly variable processing times
3) ISM was se1ected as a methodological basis for this study. This research proposes an ISM model for the tackling the production planning and inventory management complexity in remanufacturing based on its driving and dependence power. The major advantages of this research are that it can be used for both qualitative and quantitative criteria. The results show that the model has the capability to be flexible and apply in all remanufacturing setting.
4) The driving power of „The disassembly of returned products‟ (CC-3) has highest driving power and lowest dependence power hence it is placed at lowest level while „The uncertain timing and quality of returns‟ (CC-1) and „The need to balance returns with demands‟ (CC-2) has highest dependence power and lowest driving power hence placed at level 1.
5) The ISM method of ranking suppliers suffers from some shortcomings. The first is that although the ISM helps to stay consistent when assigning inter- relational dependence, a great deal of subjectivity remains embedded in the method the second is that if a new criterion is added, the classification might be modified. The third is that the method does not consider situations where multiple complexities may be used.
Table 6.1 Comparison of Complexity ranking by AHP and ISM Sr.
No. Complexity No. Weightage by
AHP Ranking by
AHP Level by ISM
1 CC-1 0.028 5 5
2 CC-2 0.028 5 5
3 CC-3 0.418 1 1
4 CC-4 0.231 2 2
5 CC-5 0.059 4 4
6 CC-6 0.117 3 3
7 CC-7 0.117 3 3
Vol. 07, Issue 01,January 2022 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 89 Graph 6.1 Weightages of Complexity
6.2 Conclusion
The Major intention of the research was to identify the common complexities in production planning for remanufacturing business setting. The various case researches and thorough literature research resulted into seven complexities which are major hurdles in production planning and inventory management for remanufacturing industries.
The second objective was to prepare the strategic framework to tackle those complexities to make the production scheduling for remanufacturing. This task was accomplished by ISM model which has established the hierarchy to tackle those complexities, based on their driving and dependence power.
Further to validate this model an Analytical Hierarchy Process was used to calculate the weightage of each criterion.
The ranking of all complexities calculated by ISM and AHP resembles.
6.3 Recommendations
Although the study proves the ranking calculated are foolproof and helpful for remanufacturing industries, following are the few recommendations:
The model can be used in the evaluation and selection of the best schedule of shop floor and inventory control in remanufacturing setting to optimise the funds and fulfil the market demand to achieve the competitive leverage.
The model can represent a framework that can be used in the private, semi- government and government sector.
The model ensures fast but accurate evaluation and successful production planning and inventory control
The flexibility of the model enables the user to modify it as required based on the selection criteria of project, available funds and the choice of customer.
6.4 Future Researches
This research may be expanded in a number of different directions. First of all, this is the primary model for production planning and scheduling problem for remanufacturing SMEs; further research is required to review the suitability of the hierarchy structure, the validity and significance of the selection criteria and sub-criteria, and their weights. This will help in further verification and fine tuning of the model. Thirdly, there are some limitations of the approach. ISM is an interpretive technique so the model generation is based on the interpretive judgment of the team of experts. The further analysis can be done on the basis of Structural equation modelling (SEM).
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