Figure 1
Figure 2
Figure 3
Summary of literature review_
| Literature | Factors | Lean | Agile | Capability to switch |
|---|---|---|---|---|
| Chase et al. (2009) | Demand analytics and reporting | √ | √ | X |
| Chase et al. (2009) | Inclusion of causal factors into forecasts | √ | √ | X |
| Lockamy and McCormack (2004), | Integrated collaborative forecasts with customers | √ | √ | X |
| Chase et al. (2009) | Scientific demand forecasting | √ | √ | √ |
| Lee et al. (2000) | Visibility of point-of-sales data | √ | √ | √ |
| Chen et al. (2000) | Customer demand visibility | √ | √ | √ |
| Achabal et al. (2000) and Yao et al. (2007) | Vendor-managed inventory | √ | X | X |
| Heinrich (2005), Sahin (2004), | Use of RFId, bar coding, etc. | X | X | √ |
| Sahin (2004), Rekik et al. (2008), and Vuyk (2003) | Correct warehousing | √ | √ | √ |
| Bollapragada and Morton (1999) and Vuyk (2003) | Fewer random-yield problems | √ | X | X |
| Chiou (2008) | Lateral inventory trans-shipment | √ | X | X |
| Paul and Anantharaman (2004) | Development-oriented appraisals of employees | X | X | √ |
| Paul and Anantharaman (2004) and Harel and Tzafrir (1999) | Comprehensive training | X | X | √ |
| Thakkar et al. (2009) and Dench (1997) | Technical competence of employees | X | X | √ |
| Hopp and Van Oyen (2004) and Herzenberg et al. (1998) | Multi-skilling of workforce | X | X | √ |
| Harel and Tzafrir (1999) | Motivation of employees | X | X | √ |
| Sherehiy et al. (2007) | Mechanistic/organic design of organization | X | √ | √ |
| Sun et al. (2008) | Inventory visibility | √ | √ | √ |
| Bhatia (2008) | Selective inventory control | √ | √ | X |
| Hanna et al. (2000) | Employee involvement | X | X | √ |
| Perry and Sohal (2000) | Proximity of suppliers | √ | √ | X |
Logistics applications of BBN_
| Literature | Contents |
|---|---|
| Soberanis and Elizabeth (2010) | Uses an extended BBN approach to analyse supply chain disruptions. The study is aimed at developing strategies that can reduce the adverse effects of disruptions and hence improve overall system reliability. |
| Li et al. (2006) | Model the supply chain as a BBN that depicts the operations centres, material, and material flow; use the network to ascertain the time and cost of a disruption. |
| Li and Gao (2010) | Use BBN to solve the collaborative efficiency of enterprises in a supply chain. |
| Anderson et al. (2004) | Use BBN to model a service-profit chain in the context of transportation service satisfaction. The BBN is used to arrive at probabilistic inferences concerning customer loyalties, service input variables and service recovery. |
| Sutrisnowati et al. (2015) | Analyse the lateness probability using a BBN by considering various factors in container handling. By this method, one can infer the activities' lateness probabilities and provide recommendations sequentially to port managers for improving existing activities. |
Factors affecting a dynamic lean–agile spare part replenishment system_
| Factor No./Name | States | Influenced by | Definition |
|---|---|---|---|
| F1/Forecasting | [Good, Average, Poor] | Collaborative Forecasting, Scientific Forecasting, Inclusion of Causal Events, Information and Communication Technology, Duration of each training, Frequency of each training | Ability to forecast the requirement of spare parts |
| F2/Collaborative Forecasting | [Yes, No] | Information and Communication Technology | Use of inputs from all stakeholders for forecasting |
| F3/Scientific Forecasting | [Yes, No] | N/A | Use of scientific methods to forecast |
| F4/Inclusion of Causal Events | [Yes, No] | N/A | Inclusion of causal events like training exercise into forecasts |
| F5/Information and Communication Technology | [Yes, No] | N/A | Presence for ICT for real time flow of information |
| F6/Inventory Management | [Good, Average, Poor] | Inventory Visibility, Use of Technology in Inventory Management | Use of correct inventory management techniques |
| F7/Inventory Visibility | [Yes, No] | Information and Communication Technology | Visibility of inventory to all stakeholders |
| F8/Use of Technology in Inventory Management | [Yes, No] | N/A | Use of modern technologies like RFId, Bar code scanning for warehousing |
| F9/Processes | [Good, Average, Poor] | Use of local suppliers, Vendor Managed Inventory, Lateral Trans-shipment, Human Resource Management | Use of industry best practices in supply management |
| F10/Use of local suppliers | [Yes, No] | N/A | Local suppliers for supply of spares |
| F11/Vendor Managed Inventory | [Yes, No] | N/A | Use of competitive advantage of using VMI |
| F12/Lateral | [Yes, No] | N/A | Ability of parallel shifting of spare parts |
| F13/Human Resource Management | [Good, Average, Poor] | Motivation, Technical competence of Workforce, Training, Duration of each training, Frequency of each training, Qualification, Working Environment, Salary, Job Security, Incentives | Status of human resource |
| F14/Motivation | [High, Mid, Low] | Salary, Job Security, Incentives | Level of motivation of the workforce |
| F15/Technical competence of Workforce | [High, Mid, Low] | Qualification, Working Environment | Ability of the workforce to stay technologically aware |
| F16/Training | [High, Mid, Low] | Duration of each training, Frequency of each training | Level of expertise of the workforce |
| F17/Duration of each training | [Short, Mid, Long] | N/A | Time period of each of the training capsule |
| F18/Frequency of each training | [Frequent, Rare] | N/A | Frequency of training for each of the worker |
| F19/Qualification | [High, Low] | N/A | Technical qualification of the workforce |
| F20/Working Environment | [Tech, Non Tech] | N/A | Presence of conducive technical learning environment at the workplace |
| F21/Salary | [High, Mid, Low] | N/A | Monetary remuneration to the workforce as compared to equivalent industry |
| F22/Job Security | [Yes, No] | N/A | Permanency of the job |
| F23/Incentives | [Yes, No] | N/A | Recognitions, Bonuses etc to reward better workers |
Military applications of BBNs_
| Literature | Contents |
|---|---|
| Johansson and Falkman (2008) | Develop a threat evaluation system in an air defence scenario. The BBN-based approach makes it possible to handle imperfect observations. |
| Wang et al. (2012) and Hou et al. (2010) | Use a dynamic BBN for Air Defence threat assessment. The advantage of using BBN is that it can modify the threat assessment knowledge repository dynamically, which enables the assessment model to possess better adaptability for producing more accurate assessment results. |
| Hudson et al. (2001) | Describe a software tool Site Profiler that assists antiterrorism planners at military installations to draw inferences about the risk of terrorist attack. |
| Jha (2009) | Develops a model to predict the likelihood of future terrorist activities at critical transportation infrastructure facilities. |
| Kruger et al. (2012) | Uses BBN for identification of a tracked object and assessment of its affiliation and threat potential in maritime surveillance. |
| Xiang et al. (2008) | Develop an intelligent decision support system for military situation assessment. Use BBN models as decision models that have the ability to model and reason under uncertainties. BBN is updated as the situation develops and fresh inputs are available. |
| Laskey et al. (2000) and Wright et al. (2002) | Develop a model to solve a common dilemma in the minds of military planners to segregate important information from within a large volume of available information from diverse sources during conflicts. |
| Gillies et al. (2010) | Introduce modelling and analysis techniques for sensor-enabled missions that quantify the uncertainty in the data and provide a means to estimate the quality of information using BBNs. |
| Falzon (2006) | Describes a centre of gravity (COG) analysis by military commanders. COG is affected by a number of critical capabilities (CCs), with each CC having a number of critical requirements (CRs), which in turn have critical vulnerabilities (CVs) that are targeted through a proper course of action. The authors use causal probabilistic networks to represent the relationships among the CCs and CRs for a COG construct. |