Abstract
Trainings imparted to the company employees are prerequisite for organizational transformation. Impact of the trainings appears in the form of changed behavior and attitude of the employees that contribute significantly for enhancement of the supply chain score of the focal firm. This chapter discusses the types of trainings generally categorized in soft skills and hard skills. Training need analysis is the best proven method utilized to identify the competency gaps of current employees. Soft skills trainings and hard skills trainings are designed for capacity building in order to reduce the gap and raise the employee productivity toward the sustainable supply chain management. Soft skills trainings not only change the attitude and behavior of the employee but as well enhance the motivational level of the employees that ultimately contribute in terms of better product quality and waste reduction. Hard skills trainings improve the technical capabilities of the workers. Reduced waste percentage, improved process settings, declining cost of quality, mistake proofing in product design, and enhanced productivity are the contributing factors for sustained supply chain performance.
Training need analysis is the most appropriate method in the case company for assessing the competency gap. Training budget is allocated accordingly to reduce the competency gap. The objective of this chapter is to design the plausible policies for enhanced supply chain performance conducting experimentation with the simulated system dynamics model. What type of the training is required more and how significantly these training impact the supply chain score for enhanced supply chain performance are the research questions being explored. Experimentation with the model unveils the underlying symptoms and keeps on playing with the model to make the system better behaved. Training which is usually considered as an expenditure can be a valuable asset if its effectiveness improves the supply chain performance.
System dynamics simulated model is developed to design the policy streams for improved supply chain performance.
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Appendices
Appendix A
Appendix B
List of variables
Variables | Description | UOM | Equation type | Parametric value |
|---|---|---|---|---|
SSTCT | Soft skills training conduction time | Hours per trainer per month | C | 8 |
HSTCT | Hard skills training conduction time | Hours per trainer per month | C | 8 |
SS training | Soft skills training stock | Hours imparted | L | |
HS training | Hard skills training stock | Hours imparted | L | |
Initial SS training | Initial soft skills training | Hours imparted | C | 104 |
Initial HS training | Initial hard skills training | Hours imparted | C | 320 |
Trainer cost | Trainer cost | Rupees | C | 100,000 |
Pressure on management | Pressure on management | Percentage | C | 0–100 |
Training budget | Training budget | Rupees | C | 1,000,000 |
SS training need | Soft skills training need | Dimensionless | C | 0.4 |
No of training SS days | Number of training soft skills days | Number of training days per trainer | C | 1 |
No of training HS days | Number of training hard skills days | Number of training days per trainer | C | 1 |
SS participants | Soft skills training participants | Number of persons in a training | C | 20 |
HS participants | Hard skills training participants | Number of persons in a training | C | 20 |
No of processes | Number of processes/machines | Number | C | 10 |
Product quality | Product quality | Percentage | A | |
SSTCR | Soft skills conduction rate | Hours per month | R | |
HSTCR | Hard skills conduction rate | Hours per month | R | |
On-job training | On-job training | Hours | C | 2 |
Outside country training | Outside country training | Hours | C | 10 |
Initial waste level | Initial waste level | Percentage | C | 10 |
Initial cost | Initial failure costs | Rupees | C | 500,000 |
Initial SCP | Initial supply chain performance score | Number | C | 10 |
Normal productivity | Normal machine productivity | Percentage per process per month | C | 0.025 |
NCR | Nonconformance rate | Rupees per month | R | |
SCP constant | Supply chain performance constant | Months | C | 10 |
Trainer competency | Trainer competency | Dimensionless | T | 1–5 |
Motivational level | Motivational level | Dimensionless | T | 1–5 |
Quality Incr due to motivation | Quality increase due to motivation | Dimensionless | T | 0–1 |
Process improvement | Process improvement | Dimensionless | T | 0–1 |
Quality rise due to technical skill | Quality rise due to technical skill | Dimensionless | T | 0–1 |
SCP | Supply chain performance score | Number | L | |
SCPI | Supply chain performance score increase rate | Number per month | R | |
Competency effect on productivity | Competency effect on productivity | A | ||
Process effect on productivity | Process effect on productivity | A | ||
WENSC | Waste effect on supply chain performance | T | ||
WENCOST | Waste effect on cost of failures | T | ||
WD | Waste deduction factor | Months | C | 180 |
Waste factor | Waste factor | A | ||
Waste reduction rate | Waste reduction rate | R |
Appendix C: Variable with Base Run and Policy Run Parametric Values
List of variables
Variables | Description | UOM | Base run parametric value | Policy run parametric value |
|---|---|---|---|---|
SSTCT | Soft skills training conduction time | Hours per trainer per month | 8 | |
HSTCT | Hard skills training conduction time | Hours per trainer per month | 8 | |
SS training | Soft skills training stock | Hours imparted | ||
HS training | Hard skills training stock | Hours imparted | ||
Initial SS training | Initial soft skills training | Hours imparted | 104 | |
Initial HS training | Initial hard skills training | Hours imparted | 320 | |
Trainer cost | Trainer cost | Rupees | 100,000 | 50,000 |
Pressure on management | Pressure on management | Percentage | 0–100 | |
Training budget | Training budget | Rupees | 1,000,000 | |
SS training need | Soft skills training need | Dimensionless | 0.4 | 0.8, 0.2 |
No of training SS days | Number of training soft skills days | Number of training days per trainer | 1 | |
No of training HS days | Number of training hard skills days | Number of training days per trainer | 1 | |
SS participants | Soft skills training participants | Number of persons in a training | 20 | |
HS participants | Hard skills training participants | Number of persons in a training | 10 | 30 |
No of processes | Number of processes/machines | Number | 10 | |
Product quality | Product quality | Percentage | ||
SSTCR | Soft skills conduction rate | Hours per month | ||
HSTCR | Hard skills conduction rate | Hours per month | ||
On-job training | On-job training | Hours | 2 | |
Outside country training | Outside country training | Hours | 10 | |
Initial waste level | Initial waste level | Percentage | 10 | |
Initial cost | Initial failure costs | Rupees | 500,000 | |
Initial SCP | Initial supply chain performance score | Number | 10 | |
Normal productivity | Normal machine productivity | Percentage per process per month | 0.025 | |
NCR | Nonconformance rate | Rupees per month | ||
SCP constant | Supply chain performance constant | Months | 10 | |
Trainer competency | Trainer competency | Dimensionless | 1–5 | |
Motivational level | Motivational level | Dimensionless | 1–5 | |
Quality Incr due to motivation | Quality increase due to motivation | Dimensionless | 0–1 | |
Process improvement | Process improvement | Dimensionless | 0–1 | |
Quality rise due to technical skill | Quality rise due to technical skill | Dimensionless | 0–1 | |
SCP | Supply chain performance score | Number | ||
SCPI | Supply chain performance score increase rate | Number per month | ||
Competency effect on productivity | Competency effect on productivity | |||
Process effect on productivity | Process effect on productivity | |||
WENSC | Waste effect on supply chain performance | |||
WENCOST | Waste effect on cost of failures | |||
WD | Waste deduction factor | Months | 180 | |
Waste factor | Waste factor | |||
Waste reduction rate | Waste reduction rate |
Appendix D
Programming for System Dynamics Simulation Model on STELLA Software
1.1 Note: Trainings_SCM Model
Top-Level Model: Cost(t) = Cost(t - dt) + (NCR) * dt INIT Cost = Initial_Cost INFLOWS: NCR = -CIF*WENCOST HS_Training(t) = HS_Training(t - dt) + (HSTCR) * dt INIT HS_Training = Initial_HS_Training INFLOWS: HSTCR = (HSTCT*HS_Traines*HS_Partiipants*Training_HS_days)+Onjob_Training+Outside_Country_Trainngs SCP(t) = SCP(t - dt) + (SCPI) * dt INIT SCP = Initial_SCP INFLOWS: SCPI = (WENSC+CCNSC)/SCP_Constant SS_Training(t) = SS_Training(t - dt) + (SSTCR) * dt INIT SS_Training = Initial_SS_Training INFLOWS: SSTCR = SSTCT*SS_Participants*Training_SS_days*SS_Trainers Waste_Level(t) = Waste_Level(t - dt) + ( - Waste_reduction_rate) * dt INIT Waste_Level = Initial_Waste_Level OUTFLOWS: Waste_reduction_rate = ((Process_Improvement+Product_Qaulity)/WD)+Waste_factor CCNSC = GRAPH(Cost) (0, 0.965), (52631.5789474, 0.871), (105263.157895, 0.698), (157894.736842, 0.535), (210526.315789, 0.347), (263157.894737, 0.297), (315789.473684, 0.287), (368421.052632, 0.267), (421052.631579, 0.252), (473684.210526, 0.223), (526315.789474, 0.203), (578947.368421, 0.188), (631578.947368, 0.168), (684210.526316, 0.158), (736842.105263, 0.153), (789473.684211, 0.149), (842105.263158, 0.139), (894736.842105, 0.134), (947368.421053, 0.119), (1000000, 0.099) CIF = 0.015 Competancey_effect_on_Productivity = GRAPH(Trainer_Competancy) (0.000, 0.318), (0.500, 0.346), (1.000, 0.389), (1.500, 0.436), (2.000, 0.464), (2.500, 0.493), (3.000, 0.526), (3.500, 0.588), (4.000, 0.635), (4.500, 0.678), (5.000, 0.701) Hired_Trainers = Training_Budget/Trainer_Cost HR_Projection = GRAPH(Training_Need_Analysis) (0.00, 0.00), (4.54545454545, 0.89), (9.09090909091, 1.85), (13.6363636364, 2.36), (18.1818181818, 4.72), (22.7272727273, 6.94), (27.2727272727, 13.59), (31.8181818182, 21.36), (36.3636363636, 29.85), (40.9090909091, 36.89), (45.4545454545, 44.42), (50.00, 50.00) HS_Partiipants = 10 HS_Traines = (1-SSTrainer_Need)*Trainer_Allocation HSTCT = 8 Initial_Cost = 500000 Initial_HS_Training = 320 Initial_SCP = 0 Initial_SCP_S = 10 Initial_SS_Training = 104 Initial_Waste_Level = 10 Machine_Productivity = Normal_Productivity*Process_effect_on_productivity*Competancey_effect_on_Productivity Motivalton_Level = GRAPH(Trainer_Competancy) (0.000, 0.000), (0.500, 0.470), (1.000, 0.767), (1.500, 1.163), (2.000, 1.460), (2.500, 2.104), (3.000, 2.599), (3.500, 3.069), (4.000, 3.564), (4.500, 3.911), (5.000, 5.000) No_of_processes = 10 Normal_Productivity = 0.025 Onjob_Training = (2000*8*25*.01/2000) Outside_Country_Trainngs = 10 Pressure_on_Management = GRAPH(HR_Projection) (0.00, 0.0), (5.00, 5.9), (10.00, 10.4), (15.00, 16.8), (20.00, 25.7), (25.00, 32.2), (30.00, 41.1), (35.00, 48.0), (40.00, 59.4), (45.00, 70.8), (50.00, 88.6) Process_effect_on_productivity = GRAPH(Process_Improvement) (0.000, 0.100), (0.100, 0.156), (0.200, 0.190), (0.300, 0.256), (0.400, 0.313), (0.500, 0.346), (0.600, 0.389), (0.700, 0.417), (0.800, 0.460), (0.900, 0.517), (1.000, 0.592) Process_Improvement = GRAPH(Technical_Skill) (0.000, 0.043), (0.100, 0.066), (0.200, 0.071), (0.300, 0.095), (0.400, 0.123), (0.500, 0.156), (0.600, 0.194), (0.700, 0.251), (0.800, 0.332), (0.900, 0.393), (1.000, 0.417) Product_Qaulity = (Quality_Increase_due_to_motivation*Quallity_rise_due_to_skill)*100 Quality_Increase_due_to_motivation = GRAPH(Motivalton_Level*(SS_Training/(Initial_SS_Training*100))) (0.000, 0.045), (0.500, 0.129), (1.000, 0.246), (1.500, 0.346), (2.000, 0.436), (2.500, 0.512), (3.000, 0.635), (3.500, 0.678), (4.000, 0.810), (4.500, 0.882), (5.000, 0.950) Quallity_rise_due_to_skill = Technical_Skill SCP_Constant = 10 SS_Participants = 20 SS_Trainers = Trainer_Allocation*SSTrainer_Need SSTCT = 8 SSTrainer_Need = 0.4 Technical_Skill = GRAPH(HS_Training/(Initial_HS_Training*10)) (0.00, 0.005), (1.00, 0.074), (2.00, 0.261), (3.00, 0.370), (4.00, 0.488), (5.00, 0.559), (6.00, 0.664), (7.00, 0.744), (8.00, 0.829), (9.00, 0.926), (10.00, 1.000) Trainer_Allocation = Hired_Trainers Trainer_Competancy = GRAPH(Trainer_Cost) (0, 0.124), (7142.85714286, 0.119), (14285.7142857, 0.178), (21428.5714286, 0.233), (28571.4285714, 0.421), (35714.2857143, 0.668), (42857.1428571, 1.015), (50000, 1.312), (57142.8571429, 1.609), (64285.7142857, 1.931), (71428.5714286, 2.302), (78571.4285714, 2.649), (85714.2857143, 2.995), (92857.1428571, 4.431), (100000, 5.000) Trainer_Cost = 100000 Training_Budget = GRAPH(Pressure_on_Management) (0.0, 0), (5.26315789474, 10000), (10.5263157895, 50000), (15.7894736842, 100000), (21.0526315789, 140000), (26.3157894737, 210000), (31.5789473684, 390000), (36.8421052632, 480000), (42.1052631579, 540000), (47.3684210526, 600000), (52.6315789474, 650000), (57.8947368421, 720000), (63.1578947368, 780000), (68.4210526316, 820000), (73.6842105263, 840000), (78.9473684211, 850000), (84.2105263158, 870000), (89.4736842105, 890000), (94.7368421053, 910000), (100.0, 920000) Training_HS_days = 1 Training_Need_Analysis = 10+STEP(10, 10)+RAMP(0.15, 10) Training_SS_days = 1 Waste_factor = No_of_processes*Machine_Productivity WD = 180 WENCOST = GRAPH(Waste_Level) (0.00, 32000), (2.00, 54000), (4.00, 106000), (6.00, 145000), (8.00, 237000), (10.00, 303000), (12.00, 353000), (14.00, 399000), (16.00, 433000), (18.00, 473000), (20.00, 493000) WENSC = GRAPH(Waste_Level) (0.0, 0.936), (4.7619047619, 0.861), (9.52380952381, 0.767), (14.2857142857, 0.584), (19.0476190476, 0.391), (23.8095238095, 0.337), (28.5714285714, 0.302), (33.3333333333, 0.287), (38.0952380952, 0.267), (42.8571428571, 0.243), (47.619047619, 0.223), (52.380952381, 0.208), (57.1428571429, 0.198), (61.9047619048, 0.188), (66.6666666667, 0.183), (71.4285714286, 0.178), (76.1904761905, 0.178), (80.9523809524, 0.168), (85.7142857143, 0.168), (90.4761904762, 0.168), (95.2380952381, 0.163), (100.0, 0.163) { The model has 53 (53) variables (array expansion in parens). In 1 Modules with 1 Sectors. Stocks: 5 (5) Flows: 5 (5) Converters: 43 (43) Constants: 21 (21) Equations: 27 (27) Graphicals: 13 (13)}
Appendix E
Base run and policy run 1 Engaging low-rate trainer from USD 1000 to USD 500 per day
Base run and policy run 1 Engaging low-rate trainer from USD 1000 to USD 500 per day
Base run and policy run 1 Engaging low-rate trainer from USD 1000 to USD 500 per day
Base run and policy run 1 Engaging low-rate trainer from USD 1000 to USD 500 per day
Base run and policy run 1 Engaging low-rate trainer from USD 1000 to USD 500 per day
Base run and policy run 1 Engaging low-rate trainer from USD 1000 to USD 500 per day
Base run and policy run 1 Engaging low-rate trainer from USD 1000 to USD 500 per day
Base run and policy run 1 Engaging low-rate trainer from USD 1000 to USD 500 per day
Base run and policy run 2 Focus on cultural and behavioral issue of organization (soft skills training need increased from 0.4 to 0.8)
Base run and policy run 2 Focus on cultural and behavioral issue of organization (soft skills training need increased from 0.4 to 0.8)
Base run and policy run 2 Focus on cultural and behavioral issue of organization (soft skills training need increased from 0.4 to 0.8)
Base run and policy run 2 Focus on cultural and behavioral issue of organization (soft skills training need increased from 0.4 to 0.8)
Base run and policy run 2 Focus on cultural and behavioral issue of organization (soft skills training need increased from 0.4 to 0.8)
Base run and policy run 2 Focus on cultural and behavioral issue of organization (soft skills training need increased from 0.4 to 0.8)
Base run and policy run 2 Focus on cultural and behavioral issue of organization (soft skills training need increased from 0.4 to 0.8)
Base run and policy run 2 Focus on cultural and behavioral issue of organization (soft skills training need increased from 0.4 to 0.8)
Base run and policy run 3 Policy on enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third is hard skills training participants increased from 10 to 30 persons; see the results of those policies
Base run and policy run 3 Policy on enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third hard skills training participants increased from 10 to 30 persons; see the results of those policies
Base run and policy run 3 Policy on enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third hard skills training participants increased from 10 to 30 persons; see the results of those policies
Base run and policy run 3 Policy on enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third hard skills training participants increased from 10 to 30 persons; see the results of those policies
Base run and policy run 3 Policy on enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third hard skills training participants increased from 10 to 30 persons; see the results of those policies
Base run and policy run 3 Policy on enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third hard skills training participants increased from 10 to 30 persons; see the results of those policies
Base run and policy run 3 Policy on enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third hard skills training participants increased from 10 to 30 persons; see the results of those policies
Base run and policy run 3 Policy on enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third hard skills training participants increased from 10 to 30 persons; see the results of those policies
Base run and policy run 3 Policy on the enhancing the technical base of employees by experimenting with the three parameters. First trainer cost is reduced from USD 1000 to USD 250 per trainer per day, second soft skills training need is reduced from 0.4 to 0.2 (40–20%), and third hard skills training participants increased from 10 to 30 persons; see the results of those policies
Graphical function between motivation level and trainer competency
Graphical function between process improvement and technical skill
Graphical function between pressure on management and HR projection
Graphical function between quality increase due to motivation and motivation level
Graphical function between cost of failures and waste level
Graphical function between effect on supply chain performance and waste level
Graphical function between effect on supply chain performance and cost of failures
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Yusuf, I., Azhar, T.M. (2018). Policy Design for Sustainable Supply Chain Through Training. In: Qudrat-Ullah, H. (eds) Innovative Solutions for Sustainable Supply Chains. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-94322-0_6
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