New data-driven solutions for improved delivery schedule accuracy and information sharing in supply chains
| Reference number | |
| Coordinator | Chalmers Tekniska Högskola AB - Institutionen för teknikens ekonomi och organisation |
| Funding from Vinnova | SEK 3 680 000 |
| Project duration | October 2018 - November 2021 |
| Status | Completed |
| Venture | FFI - Sustainable Production |
| Call | 2018-00535-en |
| End-of-project report | 2018-02695sv.pdf(pdf, 570 kB) (In Swedish) |
Important results from the project
The project is based on the automotive industry´s poor forecast and delivery plan quality with a negative impact on capital tied up, transport costs, volume flexibility and the environment throughout the supply chain. The goal is to develop new data-driven solutions that enable measurement, visualization and prediction of plan variations, and enable plan quality improvement and thereby increase the ability of companies and supply chain planning systems to compensate for and handle uncertainties, variations and disruptions in the increasingly complex production networks.
Expected long term effects
The data-driven solutions for measurement, prediction and root-cause analysis of schedule variations are expected to contribute to more stable planning conditions and increase productivity in production planning and supply processes. They are expected to shorten order and delivery lead times, as well as increase the ability to change through better connection and simpler communication and better mutual understanding via computerized metrics. The ability to analyze large amounts of data faster and with higher intelligence also enables faster production planning decisions.
Approach and implementation
The studies have been carried out at three OEM and five supplier companies. The first parts of the project analyze how current delivery plans vary in the automotive industry´s supply chains and explain its consequences and root causes (survey, case and quantitative analyzes of historical delivery plans and other company internal data). The latter parts develop new solutions for measuring, visualizing, predicting and improving plan quality in supply chains (in-depth quantitative data analyzes, data model development and pilot tests).