Industry Challenge
Today’s transportation and logistics companies want to implement a data-driven approach for more informed decision-making. However, the path to get there requires not only researching available technologies, but also analyzing your own operation and needs to ensure the best solution.
- What analytics tools and technologies are available?
- How do you use those tools to extract benefits?
- How are machine learning models and statistics used for answering business questions?
- Where should you start with using data analytics to improve your supply chain process?
Get the Power of Data Analytics
Data and the potential for new insights seems to be available everywhere. With so much data out there, the key is to determine where to focus your attention and how to use your data to see real value in profitability, cost reduction, and increased customer satisfaction.
Drive Supply Chain Efficiency in Two Steps
Improving your supply chain efficiency using data analytics can be done in two steps. The steps are closely tied to Davenport's Analytics Maturity Curve. In this theory, the first step is descriptive analytics - utilizing your data to understand past and current performance. The theory then focuses on moving to step two with predictive and prescriptive analytics - utilizing your data to anticipate future performance. Leveraging your data with advanced analytics tools allows you to make proactive, informed decisions.
1. Supply Chain Visibility
The first step is essential to shift from reactive to proactive decision-making. It focuses on using historic data (like stock replenishment, historic routes, and deliveries) to understand what is happening and performance over time, and learning from it to identify improvement areas. This is also called descriptive analytics. Examples are: Performance Analysis, Driver Scorecards and Driver Incentive Programs, Customer Scorecards, and Data Quality Analysis.
2. Analytics-Driven Improvements
Analytics-driven improvements go a step further than Supply Chain Visibility, using more advanced analytics solutions to support proactive decision-making. This process makes use of historic data with machine learning and optimization models to predict, analyze, and compare future scenarios, and make quantified decisions. This is also called predictive and prescriptive analytics. Examples are: Asset Analysis, Planned vs. Actual Analysis, Predictive Maintenance, and Cost to Serve - Customer Profitability Analysis.
Tips & Tools: How to Enable Data-Driven Decision-Making
There are a number of ways that companies can enable data-driven decision-making and provide analytics solutions to decision makers. Depending on your preferences, you can make use of existing interfaces, analytics models, and visualizations, or create new analytics solutions.
Read the E-guide.
An E-Guide for companies that want to improve decision-making with analytics. This guide looks at a practical approach to increase visibility and achieve a more effective supply chain with data.
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