| Literature DB >> 24340248 |
Muhammad Imran Qureshi1, Mehwish Iftikhar, Mansoor Nazir Bhatti, Tauqeer Shams, Khalid Zaman.
Abstract
In recent years, inventory management is continuous challenge for all organizations not only due to heavy cost associated with inventory holding, but also it has a great deal to do with the organizations production process. Cement industry is a growing sector of Pakistan's economy which is now facing problems in capacity utilization of their plants. This study attempts to identify the key strategies for successful implementation of just-in-time (JIT) management philosophy on the cement industry of Pakistan. The study uses survey responses from four hundred operations' managers of cement industry in order to know about the advantages and benefits that cement industry have experienced by Just in time (JIT) adoption. The results show that implementing the quality, product design, inventory management, supply chain and production plans embodied through the JIT philosophy which infect enhances cement industry competitiveness in Pakistan. JIT implementation increases performance by lower level of inventory, reduced operations & inventory costs was reduced eliminates wastage from the processes and reduced unnecessary production which is a big challenge for the manufacturer who are trying to maintain the continuous flow processes. JIT implementation is a vital manufacturing strategy that reaches capacity utilization and minimizes the rate of defect in continuous flow processes. The study emphasize the need for top management commitment in order to incorporate the necessary changes that need to take place in cement industry so that JIT implementation can take place in an effective manner.Entities:
Keywords: Cement industry; Just-in-time management; Pakistan; Production process; Structural equation model
Year: 2013 PMID: 24340248 PMCID: PMC3858593 DOI: 10.1186/2193-1801-2-645
Source DB: PubMed Journal: Springerplus ISSN: 2193-1801
Figure 1Research framework. Source: Self extract.
Demographic characteristics of operations managers
| Respondent’s characteristics | Frequency distribution | Percentages |
|---|---|---|
|
| ||
| Male | 392 | 98% |
| Female | 08 | 2% |
|
| ||
| Production/Operation manager | 208 | 52% |
| Inventory mangers | 124 | 31% |
| Plant mangers | 68 | 17% |
|
| ||
| 31–35 | 160 | 40% |
| 36–40 | 136 | 34% |
| 41–45 | 80 | 20% |
| 46–50 | 16 | 4% |
| Above | 08 | 2% |
|
| ||
| Punjab | 128 | 32% |
| Sindh | 96 | 24% |
| Baluchistan | 48 | 12% |
| Khyber Pakhtunkhwa | 68 | 17% |
| Federal | 60 | 15% |
Constructs of reliability analysis
| Construct | No. of Items | Cronbach-alpha | Kaiser-Meyer-Olkin (KMO) test |
|---|---|---|---|
| Production design | 3 | 0.72 | 0.59 |
| TQC | 3 | 0.78 | 0.70 |
| Inventory | 3 | 0.71 | 0.71 |
| Supply chain integration | 3 | 0.79 | 0.72 |
| Production plan | 3 | 0.67 | 0.52 |
| JIT implementation | 6 | 0.66 | 0.57 |
| Over all | 21 | 0.72 | 0.64 |
Total variance explained
| Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | |
| 1 | 6.817 | 32.460 | 32.460 | 6.817 | 32.460 | 32.460 | 3.095 | 14.740 | 14.740 |
| 2 | 1.988 | 9.468 | 41.928 | 1.988 | 9.468 | 41.928 | 3.065 | 14.595 | 29.335 |
| 3 | 1.662 | 7.916 | 49.844 | 1.662 | 7.916 | 49.844 | 2.195 | 10.453 | 39.787 |
| 4 | 1.297 | 6.178 | 56.022 | 1.297 | 6.178 | 56.022 | 2.135 | 10.167 | 49.954 |
| 5 | 1.184 | 5.639 | 61.661 | 1.184 | 5.639 | 61.661 | 2.026 | 9.648 | 59.602 |
Extraction Method: Principal Component Analysis.
Rotated component matrix
| Component | |||||
|---|---|---|---|---|---|
| Product design | TQC | Product planning | Inventory | Supply chain integration | |
| Analysis | .710 | ||||
| Concept | .508 | ||||
| Synthesis | .782 | ||||
| Process quality | .646 | ||||
| Product quality | .675 | ||||
| Customer satisfaction | .754 | ||||
| EOQ | .707 | ||||
| Continuous ordering | .784 | ||||
| Periodic ordering | .737 | ||||
| Distribution network configuration | .670 | ||||
| Distribution strategy | .433 | ||||
| Information | .541 | ||||
| Resource utilization | .566 | ||||
| Coordinated work activities | .622 | ||||
| Labor productivity | .679 | ||||
| Variation | 14.740% | 14.595% | 10.453% | 10.167% | 9.648% |
Rotation Method: Varimax with Kaiser Normalization.
Figure 2JIT implementation success model and its practical application. Source: Self Extract.
Figure 3Factor loaded fish bone.
Descriptive statistics and correlation matrix
| JIT factors | Mean | Std. deviation | Product design | Total quality control | Inventory management | Supply chain integration | Product planning | Just in time implementation |
|---|---|---|---|---|---|---|---|---|
| Product design | 3.65 | 0.673 | 1.000 | |||||
| Total quality control | 3.70 | 0.813 | 0.450 | 1.000 | ||||
| Inventory management | 3.87 | 0.790 | 0.312 | 0.610 | 1.000 | |||
| Supply chain integration | 3.63 | 0.745 | 0.510 | 0.539 | 0.437 | 1.000 | ||
| Product planning | 3.52 | 0.671 | 0.504 | 0.344 | 0.356 | 0.571 | 1.000 | |
| Just in time implementation | 3.62 | 0.774 | 0.468 | 0.456 | 0.385 | 0.497 | 0.362 | 1.000 |
Note: correlation is significant at the 0.01 level (2 tailed).
Model fit summary
| Fitness indices | Standard values | Achieved values |
|---|---|---|
| CFI | 0.90 | .933 |
| NFI | 0.90 | .894 |
| RMSEA | p < 0.08 | .048 |
| GFI | 0.80 | .877 |
| AGFI | 0.80 | .794 |
Figure 4Structural model of JIT implementation.
Summary of hypothesis
| Hypothesis | Description | Result |
|---|---|---|
| H1 | Production design has a significant positive relationship with JIT. | Accepted |
| H2 | TQC has a significant positive impact on JIT. | Accepted |
| H3 | Inventory has a significant positive relationship with JIT. | Accepted |
| H4 | Supply chain has a significant positive impact on JIT. | Accepted |
| H5 | Production plan has a significant and positive relationship with JIT. | Accepted |