Quantitative Data
Quantitative data measure the topic being studied through “hard” numbers that can be measured, verified and manipulated for statistical analysis.
When conducting a supply chain analysis, quantitative data should be collected for the following elements of a supply chain:
- Forecasting/preparation time- Forecasting costs
- Production time
- Production amount/size/weight
- Current and future production capacity
- Cost of production
- Current inventory levels
- Capacity of warehouse
- Shipping/transport times
- Shipment methods
- Shipment routing details
- Shipping size/weight
- Shipping costs
- Customs processing times
- Customs processing costs
- Historic performance on delivery times
- Capacity of current delivery methods
Strategic Issues in Quantitative Data
Quantitative analysis is important in a supply chain analysis because it helps provide “hard” data to support views expressed by stakeholders through the survey, interviews, or meetings. The usefulness of quantitative data relies on the accuracy with which they have been collected. Knowing the origin of the data can help the project team better assess this. For instance, were shipping dates entered in the database using an automated system or were they entered by hand? Data entered by hand, when compared to data collected through an automated system, tend to have more data inaccuracies.
As data are being collected they should combined into a single source. Data collected from different sources sometimes include repetitions, and by creating one source, overlapping data can be cross-referenced and assessed for accuracy. This data condensation also eases the data processing step, because all data are easily accessible and relationships that perhaps had not been evident during data collection process may become more clearly highlighted.