An Innovative Risk Matrix Model for Warehousing Productivity Performance
Abstract
:1. Introduction
2. Literature Review
2.1. Risk Assessment and Its Applications
2.2. Warehouse Operation Risk Categories
Author(s) | External Risk | Internal Risk | ||||||||
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PER | MarR | RegR | OR | HR | FR | IR | ResR | SR | MagR | |
[13] | √ | √ | √ | √ | √ | √ | √ | |||
[38] | √ | √ | √ | √ | √ | √ | √ | √ | √ | |
[39] | √ | |||||||||
[40] | √ | √ | ||||||||
[41] | √ | √ | √ | √ | √ | √ | ||||
[42] | √ | √ | √ | |||||||
[43] | √ | |||||||||
[44] | √ | √ | √ | √ | ||||||
[45] | √ | √ | √ |
2.3. Risks Assessment Methods
3. Construction of the Innovative Risk Matrix Model for the Warehouse Productivity Performance Risk Assessment
3.1. Risk Matrix Model
- Object to Analyse: Identifies the risk category and risk factor used to evaluate the impact and severity of the risk towards warehouse performance productivity.
- Impact: The occurrence of undesirable consequences falls into five categories and is addressed using a scale ranging from one to five, used in this study. The illustrative definition is to recognise the risk category level related to warehouse operations from a productivity perspective, as shown in Table 2. Level one denotes having no impact or incurring no losses because of an incident on productive performance, whereas level five denotes having the greatest impact, resulting in a performance failure or fatalities (people).
- Probability: Probability or frequency of occurrence is divided into five categories, as shown in Table 3. The range of probability or frequency is presented as a percentage in which an illustrative interpretation is used to evaluate the likelihood of occurrence, of the risk factors, under each risk category.
Probability Category | Range | Interpretation |
---|---|---|
Unlikely (A) | 0–10% | Very Unlikely to Occur |
Seldom (B) | 11–40% | Unlikely to Occur |
Occasional (C) | 41–60% | May Occur About Half of the Time |
Likely (D) | 61–90% | Likely to Occur |
Frequent (E) | 91–100% | Very Likely to Occur |
- Risk Rating Scale: Table 4 illustrates 5 × 5 matrix cells with irregularly shaped risk zones. The matrix comprises a square divided into several boxes, with each box representing a different underlying estimation of risk [64]. The main reason for using a risk matrix is to access and prioritise a list of risks at the same level. The use of blue, green, yellow, orange, and red colours reflects the categorisation of the risk into negligible, low, medium, high, and extreme, respectively.
Critical | Medium | High | High | Extreme | Extreme |
Serious | Medium | Medium | High | High | Extreme |
Moderate | Low | Medium | Medium | High | Extreme |
Minor | Low | Low | Medium | High | Extreme |
Negligible | Negligible | Low | Medium | High | Extreme |
Origin | 0.00–0.10 | 0.10–0.04 | 0.04–0.60 | 0.60–0.90 | 0.90–1.00 |
- Analysis and Results: The final step is to verify the most critical risks according to the results obtained from the risk rating scale, Borda rank and AHP ranking.
3.2. Borda Order Value Method
3.3. Analytical Hierarchy Process (AHP)
3.3.1. Building a Judgement Matrix
3.3.2. Calculating the Weight Value and Checking Consistency Ratio
4. Application of the Risk Matrix Model
4.1. Establish the Architecture of Warehouse Operation Risk Set
4.2. Data Collection and Processing
4.3. Risk Matrix Assessment
4.3.1. Borda Count Assessment
4.3.2. AHP Methods Procedures
5. Discussion
6. Conclusions
6.1. Research Implication
6.2. Practical Implication
6.3. Limitation and Future Research Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Impact Category | Scale | Description |
---|---|---|
Negligible (N) | 1 | Risk had almost no effect on the warehousing productivity performance. |
Minor (Mi) | 2 | Risk had slightly affected but could meet the objectives. |
Moderate (Mo) | 3 | Moderate risk affected the warehousing productivity, but part of the objectives can be achieved. |
Serious (S) | 4 | Serious risk led to a significant decrease in warehousing productivity. |
Critical (C) | 5 | Critical risk directly affects the poor performance of warehousing productivity. |
Saaty’s Scale | Definition |
---|---|
1 | Equally important (E. Imp) |
3 | Weakly important (W. Imp) |
5 | Fairly important (F. Imp) |
7 | Strongly important (S. Imp) |
9 | Absolutely important (A. Imp) |
2, 4, 6, 8 | The intermittent values between two adjacent scales |
n | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 |
39.9676 | 42.7375 | 45.5074 | 48.2774 | 51.0473 | 53.8172 | 56.5872 | 59.3571 | |
RI | 1.5978 | 1.6086 | 1.6181 | 1.6265 | 1.6341 | 1.6409 | 1.6470 | 1.6526 |
n | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 |
62.1270 | 64.8969 | 67.6669 | 70.4368 | 73.2067 | 75.9767 | 78.7466 | 81.5165 | |
RI | 1.6577 | 1.6624 | 1.6667 | 1.6706 | 1.6743 | 1.6777 | 1.6809 | 1.6839 |
n | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 |
84.2864 | 87.0564 | 89.8263 | 92.5962 | 95.3662 | 98.1361 | 100.9060 | 103.6759 | |
RI | 1.6867 | 1.6893 | 1.6917 | 1.6940 | 1.6962 | 1.6982 | 1.7002 | 1.7020 |
Subject | Risk Category | Risk Factor |
---|---|---|
Risk Factors for Warehouse Operation | Physical Environment Risk (C1) The physical environment such as natural disasters would affect warehouse operations resulting in the interruption of service, damage to cargo and warehouse facilities | F1: Natural Disaster: Flood, Earthquake, Windstorm |
F2: Epidemic, Disease | ||
F3: Fire | ||
F4: Temperature | ||
Operational Risk (C2) This results from the breakdown of internal procedures, systems, and people when the factors directly affect the process of internal warehouse operations | F5: Information System Shutdown: Warehouse Management System, RFID | |
F6: Unexpected order change | ||
Human Risk (C3) Warehouse labour/staff with insufficient knowledge to carry out the logistics services | F7: Manual Handling Injuries | |
F8: Lack of skills and knowledge | ||
F9: Ignorance and negligence | ||
F10: Wrong estimation or judgement resulting in poor planning of the warehouse operations | ||
Market Risk (C4) The company may suffer a loss due to the warehouse’s market situation and customer preference | F11: Market Change | |
F12: Loss of key customers | ||
Resource Risk (C5) The warehouse may suffer a loss due to the unavailability of resources | F13: Utility Failure | |
F14: Machinery and equipment breakdown | ||
F15: Storage space utilisation resulting in limited space in accommodating inbound quantities | ||
F16: Ageing of facilities, equipment, and machinery | ||
Managerial Risk (C6) Refers to poor managerial skills of senior management and insufficient conceptual skills to solve the problems and complex situations related to the warehouse | F17: Change in future business development direction | |
F18: Culture gap | ||
F19: Miscommunication | ||
Financial Risk (C7) Refers to the cash flow problem of a warehouse. | F20: Non-filling the warehouse | |
F21: Delay in customer payment | ||
F22: Poor financial planning | ||
Security Risk (C8) Security concerns such as anti-theft facilities and security of the IT system are important to protect the customers’ goods, especially high-value goods and ensure the safety of confidential customer information. | F23: Criminal Activities (e.g., stolen cargoes) | |
F24: Information security | ||
Regulatory Risk (C9) An unfavourable change in regulations and policy would bring pressure and risk when the warehouse tries to fit in with the new environment. | F25: War, Civil Disobedience | |
F26: Import and Export Regulations | ||
Inventory Risk (C10) Dealing with the security risk classification of Stock Keeping Units (SKU) located in the warehouse. | F27: Damage to the product | |
F28: Damage to stuff | ||
F29: Property Damage | ||
F30: Terrorism | ||
F31: Material Smuggling | ||
F32: Human Smuggling |
Name | Designation/Position | Years of Experience | Warehouse Services (*) |
---|---|---|---|
Expert 1 | Warehouse Manager | 10 years | D; C; CD |
Expert 2 | Warehouse Operation Manager | 20 years | I; D |
Expert 3 | Warehouse Manager | 10 years | D; CD |
Expert 4 | Warehouse Assistant Manager | 3 years | D; C; CD |
Expert 5 | Senior Manager | 10 years | I; D; R; C; E; CD |
Expert 6 | Warehouse Executive | 23 years | I; D |
Expert 7 | Director of Business and Development | 19 years | D; E |
Expert 8 | Branch Manager | 20 years | D |
Expert 9 | Warehouse Manager | 16 years | D; C |
Expert 10 | Warehouse Manager | 12 years | D; R; E; CD |
Expert 11 | Operation Manager | 10 years | D |
Expert 12 | Head of Warehouse | 15 years | CD; T |
Severity | Impact | Ms | ks | Probability | Probability Range | Np | kp |
---|---|---|---|---|---|---|---|
4–5 | Critical | 0 | N/A | 5 | 91–100% | 0 | 0 |
3–4 | Serious | 0 | N/A | 4 | 61–90% | 13 | 7 |
2–3 | Moderate | 32 | 16.5 | 3 | 41–60% | 11 | 19 |
1–2 | Minor | 0 | N/A | 2 | 11–40% | 8 | 15.5 |
0–1 | Negligible | 0 | N/A | 1 | 0–10% | 0 | N/A |
Risk Category | Risk Factor | Impact | Probability | Risk Rating Scale | rs | rp | Borda Count Value bi | Borda Rank | ||
---|---|---|---|---|---|---|---|---|---|---|
Category | Scale | Category | Scale | |||||||
C1 | F1 | Moderate | 3.58 | Seldom | 0.23 | Medium | 16.50 | 15.5 | 32 | 13 |
F2 | Moderate | 3.58 | Seldom | 0.30 | Medium | 16.50 | 15.5 | 32 | 13 | |
F3 | Moderate | 3.58 | Occasional | 0.43 | High | 16.50 | 19 | 29 | 21 | |
F4 | Moderate | 3.58 | Seldom | 0.36 | Medium | 16.50 | 15.5 | 32 | 13 | |
C2 | F5 | Moderate | 3.50 | Likely | 0.61 | High | 16.50 | 7 | 41 | 0 |
F6 | Moderate | 3.50 | Likely | 0.70 | High | 16.50 | 7 | 41 | 0 | |
C3 | F7 | Moderate | 3.42 | Occasional | 0.41 | High | 16.50 | 19 | 29 | 21 |
F8 | Moderate | 3.42 | Occasional | 0.55 | High | 16.50 | 19 | 29 | 21 | |
F9 | Moderate | 3.42 | Likely | 0.67 | High | 16.50 | 7 | 41 | 0 | |
F10 | Moderate | 3.42 | Likely | 0.65 | High | 16.50 | 7 | 41 | 0 | |
C4 | F11 | Moderate | 3.42 | Likely | 0.71 | High | 16.50 | 7 | 41 | 0 |
F12 | Moderate | 3.42 | Likely | 0.70 | High | 16.50 | 7 | 41 | 0 | |
C5 | F13 | Moderate | 3.75 | Likely | 0.61 | High | 16.50 | 7 | 41 | 0 |
F14 | Moderate | 3.75 | Likely | 0.66 | High | 16.50 | 7 | 41 | 0 | |
F15 | Moderate | 3.75 | Likely | 0.72 | High | 16.50 | 7 | 41 | 0 | |
F16 | Moderate | 3.75 | Likely | 0.63 | High | 16.50 | 7 | 41 | 0 | |
C6 | F17 | Moderate | 3.42 | Occasional | 0.54 | High | 16.50 | 19 | 29 | 21 |
F18 | Moderate | 3.42 | Seldom | 0.39 | Medium | 16.50 | 15.5 | 32 | 13 | |
F19 | Moderate | 3.42 | Occasional | 0.59 | High | 16.50 | 19 | 29 | 21 | |
C7 | F20 | Moderate | 3.50 | Occasional | 0.58 | High | 16.50 | 19 | 29 | 21 |
F21 | Moderate | 3.50 | Likely | 0.73 | High | 16.50 | 7 | 41 | 0 | |
F22 | Moderate | 3.50 | Occasional | 0.58 | High | 16.50 | 19 | 29 | 21 | |
C8 | F23 | Moderate | 3.58 | Occasional | 0.58 | High | 16.50 | 19 | 29 | 21 |
F24 | Moderate | 3.58 | Likely | 0.62 | High | 16.50 | 7 | 41 | 0 | |
C9 | F25 | Moderate | 3.58 | Seldom | 0.23 | Medium | 16.50 | 15.5 | 32 | 13 |
F26 | Moderate | 3.58 | Likely | 0.61 | High | 16.50 | 7 | 41 | 0 | |
C10 | F27 | Moderate | 3.58 | Occasional | 0.60 | High | 16.50 | 19 | 29 | 21 |
F28 | Moderate | 3.58 | Occasional | 0.50 | High | 16.50 | 19 | 29 | 21 | |
F29 | Moderate | 3.58 | Occasional | 0.48 | High | 16.50 | 19 | 29 | 21 | |
F30 | Moderate | 3.58 | Seldom | 0.18 | Medium | 16.50 | 15.5 | 32 | 13 | |
F31 | Moderate | 3.58 | Seldom | 0.40 | Medium | 16.50 | 15.5 | 32 | 13 | |
F32 | Moderate | 3.58 | Seldom | 0.28 | Medium | 16.50 | 15.5 | 32 | 13 |
Risk Category | Risk Factor | Weight Value | Rank |
---|---|---|---|
C1 | F1 | 0.0236 | 2 |
F2 | 0.0236 | 2 | |
F3 | 0.0053 | 6 | |
F4 | 0.0236 | 2 | |
C2 | F5 | 0.0633 | 1 |
F6 | 0.0633 | 1 | |
C3 | F7 | 0.0053 | 6 |
F8 | 0.0051 | 7 | |
F9 | 0.0633 | 1 | |
F10 | 0.0633 | 1 | |
C4 | F11 | 0.0633 | 1 |
F12 | 0.0633 | 1 | |
C5 | F13 | 0.0633 | 1 |
F14 | 0.0633 | 1 | |
F15 | 0.0633 | 1 | |
F16 | 0.0633 | 1 | |
C6 | F17 | 0.0049 | 8 |
F18 | 0.0187 | 3 | |
F19 | 0.0047 | 9 | |
C7 | F20 | 0.0046 | 10 |
F21 | 0.0633 | 1 | |
F22 | 0.0044 | 11 | |
C8 | F23 | 0.0042 | 12 |
F24 | 0.0633 | 1 | |
C9 | F25 | 0.0126 | 4 |
F26 | 0.0633 | 1 | |
C10 | F27 | 0.0041 | 13 |
F28 | 0.0039 | 14 | |
F29 | 0.0037 | 15 | |
F30 | 0.0083 | 5 | |
F31 | 0.0083 | 5 | |
F32 | 0.0083 | 5 | |
TOTAL | 1.000 |
Risk Category | Risk Factor | Risk Matrix | Borda Count | AHP |
---|---|---|---|---|
C1 | F1 | |||
F2 | ||||
F3 | ✓ | |||
F4 | ||||
C2 | F5 | ✓ | ✓ | ✓ |
F6 | ✓ | ✓ | ✓ | |
C3 | F7 | ✓ | ||
F8 | ✓ | |||
F9 | ✓ | ✓ | ✓ | |
F10 | ✓ | ✓ | ✓ | |
C4 | F11 | ✓ | ✓ | ✓ |
F12 | ✓ | ✓ | ✓ | |
C5 | F13 | ✓ | ✓ | ✓ |
F14 | ✓ | ✓ | ✓ | |
F15 | ✓ | ✓ | ✓ | |
F16 | ✓ | ✓ | ✓ | |
C6 | F17 | ✓ | ||
F18 | ||||
F19 | ✓ | |||
C7 | F20 | ✓ | ||
F21 | ✓ | ✓ | ✓ | |
F22 | ✓ | |||
C8 | F23 | ✓ | ||
F24 | ✓ | ✓ | ✓ | |
C9 | F25 | |||
F26 | ✓ | ✓ | ✓ | |
C10 | F27 | ✓ | ||
F28 | ✓ | |||
F29 | ✓ | |||
F30 | ||||
F31 | ||||
F32 |
Risk Category | Risk Factor | Risk Control (Action Plan) |
---|---|---|
C2 | F5 |
|
F6 |
| |
C3 | F9 |
|
F10 |
| |
C4 | F11 |
|
F12 |
| |
C5 | F13 |
|
F14 |
| |
F15 |
| |
F16 |
| |
C7 | F21 |
|
C8 | F24 |
|
C9 | F26 |
|
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Md Hanafiah, R.; Karim, N.H.; Abdul Rahman, N.S.F.; Abdul Hamid, S.; Mohammed, A.M. An Innovative Risk Matrix Model for Warehousing Productivity Performance. Sustainability 2022, 14, 4060. https://doi.org/10.3390/su14074060
Md Hanafiah R, Karim NH, Abdul Rahman NSF, Abdul Hamid S, Mohammed AM. An Innovative Risk Matrix Model for Warehousing Productivity Performance. Sustainability. 2022; 14(7):4060. https://doi.org/10.3390/su14074060
Chicago/Turabian StyleMd Hanafiah, Rudiah, Nur Hazwani Karim, Noorul Shaiful Fitri Abdul Rahman, Saharuddin Abdul Hamid, and Ahmed Maher Mohammed. 2022. "An Innovative Risk Matrix Model for Warehousing Productivity Performance" Sustainability 14, no. 7: 4060. https://doi.org/10.3390/su14074060
APA StyleMd Hanafiah, R., Karim, N. H., Abdul Rahman, N. S. F., Abdul Hamid, S., & Mohammed, A. M. (2022). An Innovative Risk Matrix Model for Warehousing Productivity Performance. Sustainability, 14(7), 4060. https://doi.org/10.3390/su14074060