Published January 4, 2024
Data processing is essential for the effective use of network optimization tools for supply chain strategies. The success of these software applications relies on the quality of data utilized to build and run the models. Optimal solutions and meaningful insights are derived from accurate and well-processed data. Below are the necessary steps for successfully processing data when using network optimization tools:
1. Data Collection
- Data Consistency: Ensure data is up to date, accurate and consistent across all sources. Inconsistencies and inaccuracies can cause modeling errors or result in suboptimal solutions.
- Data Centralization: Integrate data from all sources, such as ERP systems, transportation management systems (TMS) and warehouse management systems (WMS). Centralized data ensures a single source of truth, improves accuracy, eliminates redundancy and facilitates informed decision-making.
- Data Cleaning: Data cleaning involves removing duplicates, outliers and irrelevant information while also identifying nulls, zeros and missing data. Preprocess the data to establish uniformity by standardizing formats, units, terms and other variables. Ensure data compatibility for analysis.
- Data Completeness: Ensure that all available data has been collected and check the completeness of each individual dataset. The more data provided, the more accurate the modeling results.
2. Data Analysis
- Assumptions: The use of assumptions to fill data gaps or replace missing data is common practice in data analysis and modeling when working with incomplete datasets. It is critical to be mindful and transparent when developing assumptions, as they can influence the results of subsequent analyses and modeled solutions.
- Historical Trends: Examine historical data to understand trends, patterns and seasonality in demand, service, lead times and other variables. Utilize this data to create a baseline upon which alternative solutions can be created and tested. This analysis helps in developing more accurate models and leads to more informed decision-making.
- Costs and Constraints: Incorporate cost-related data such as transportation, production, labor, lease and inventory carrying costs. Include constraints such as capacity on warehouse space or fleet availability, service level agreements, distribution limitations, and temperature requirements that affect the optimization model.
3. Data Mapping
- Geospatial Data: Accurately process geographical data such as locations of facilities, customers and suppliers. Make sure to consider transportation routes, distances and travel times. Transportation optimization and facility location decisions require precise geospatial data.
- Data Transformation: Transform the data into a suitable format and connect to the corresponding destinations in the optimization model. Align the data with the appropriate variables and parameters of the specific network optimization tool.
This is the first post in a four-part series dedicated to helping supply chain professionals navigate the four main phases of a network optimization project. Visit our newsroom to read the rest of the series and contact us today to learn how our team can help you design the optimal network strategy for your unique needs and goals.