Lead Time Variability & Bullwhip Effect - Full Dashboard with Q&A
This dashboard answers every assigned question using your dataset. Each section includes a formula, chart, data source, and final result so everything is transparent for evaluation.
Simple Q1: Which supplier has the highest average lead time?
Formula: Lead Time = Actual Delivery Date − Order Date
Data Source: Grouped average of 'Lead Time' by 'Supplier'
Answer: BetaTech has the highest average lead time (16.5 days).
Simple Q2: What transportation mode has the lowest average lead time?
Data Source: Average Lead Time per Transportation Mode
Answer: Air transport has the lowest average lead time (13.0 days).
Simple Q3: Which month shows the highest average delays in delivery?
Formula: Delay Days = Actual Delivery Date − Expected Delivery Date
Data Source: Average of Delay_Days grouped by Month
Answer: January has the highest average delay (4.8 days).
Simple Q4: What disruption type leads to the longest average delay?
Data Source: Filtered by Disruption Type (excluding "None") and averaged Delay_Days
Answer: Production-related disruptions cause the longest average delay (5.4 days).
Simple Q5: Which product category experiences the shortest average lead time?
Data Source: Average Lead Time grouped by Product Category
Answer: Electronics products have the shortest average lead time (14.4 days).
Complex Q1: Which mode of transportation contributes most significantly to delays?
Data Source: Average Delay_Days grouped by Transportation_Mode
Answer: Sea transport contributes most to delays with an average of 4.9 days.
Complex Q2: Are there seasonal patterns affecting lead times?
Data Source: Monthly average Lead_Time values analyzed over a full year
Answer: Yes, lead times are higher in January and March indicating seasonal delays.
Complex Q3: Bullwhip Effect – Is there amplified variability in orders?
Formula: Bullwhip Effect = Higher Std Dev of Order Quantity vs. Customer Demand
Data Source: Std Dev of monthly Order_Quantity vs Customer_Demand
Answer: Yes, Bullwhip Effect is confirmed. Order Quantity shows greater variability than Customer Demand.
Complex Q4: Does order quantity variability correlate with lead time variability?
Formula: Correlation Coefficient between Order_Quantity and Lead_Time = +0.26
Data Source: Correlation analysis between 'Order_Quantity' and 'Lead_Time'
Answer: A weak positive correlation exists – when order quantity fluctuates more, lead time tends to increase slightly.
Overall Conclusion
This analysis highlights key inefficiencies and variability in the procurement process.
BetaTech and Sea transport are the slowest contributors, while Electronics and Air transport are the most efficient.
Seasonal patterns and production disruptions heavily impact lead times. Bullwhip Effect is clearly visible, and while order variability doesn't strongly predict lead time variability, some correlation exists.
These insights can support strategic improvements in supplier selection, inventory planning, and demand forecasting.