Faced with increasing factory complexity, some people, not wanting to take any risks, choose to oversize their factory with the consequence of an overinvestment that can be worth millions of euros. Some others rely on organizational theories that are sometimes satisfactory but not always, like all theories (e.g., theory of constraint). At Dillygence, we use science and technology to tackle factories' challenges. No more dice!
In 2016, when we looked at existing tools, we saw products with very long runtime, too slow to allow the exploration of thousands of options needed to find optimal solutions. Access to these tools was limited to experts, and their usage was restricted to factory design - no possible use in operation. The situation drastically limited the use of a scientific approach in factory design and function, thereby feeding the success of organizational theories.
Solving complex problems and delivering user-friendly solutions requires people with expertise and different backgrounds who produce and churn ideas that lead to superior solutions. Our team is uniquely equipped to this end. Most importantly, they all share a deep understanding of factory physics.
A former scholar at MIT, an expert in industrial performance and Industry 4.0, with more than 20 years of experience in expert and management positions in large companies - HEC degree, Engineer in Robotics and AI, Ph.D. in Automation.
An expert in simulation and advanced analytics, he co-founded Analytics Operations Engineering and has worked on behalf of many large companies in the United States - an MIT graduate.
A graduate of both the University of California, Berkeley and Centrale Paris, he is an expert in data science and a tireless problem solver.
A world-renowned expert in production systems, operations research, and advanced analytics, he was a Senior Research Scientist at the Department of Mechanical Engineering and at the Laboratory for Manufacturing and Productivity at MIT since 1988 - Ph.D. in applied mathematics from Harvard
More than 25 years of expertise in operational performance, supply chain management, and management of large industrial and IT projects acquired in the automotive industry and in consulting. He is also an expert of supply chain planning, demand management and of inventory management.
Dillygence’s work demonstrated that with the assumptions previously made during the process study, our factory could not deliver the required production. Dillygence then offered smart suggestions to improve the performance of the factory while drastically reducing inventories and improving the productivity of the factory. Additional benefits of the mission included the centralization and the validation of data, and the summary of the factory’s production flows in a powerful single visual. In addition, its clarity and simplicity also made it the perfect document to be used for internal communication and training. Following this project, we decided to introduce the use of digital twins as a mandatory step in the design of our future factories.
Florian Pallot
Chargé de Process Industriels
RATP
In the context of the transformation of the automotive sector and the transition to all-electric, Dillygence has accompanied us from the general functional analysis and today in the construction of our digital twin, essential to the launch and operation of our Gigafactory in Billy-Berclau. What was great about the work we did with Dillygence was the validation of our simulation model, the reduction of inventories - divided by two - the reduction of the lead time, and the improvement the whole Gigafactory flow.
Luc Macherel
Industrial-Support Pole Manager
Automotive Cells Company
When you have to manage the performance of fifty or so assembly cells, you cannot do so without having a clear vision of the priorities to be set for your teams, and this often starts with identifying the bottleneck of the factory, which everyone can find with a simple spreadsheet and some operation data. After identifying this bottleneck, the most difficult part is to determine everyone's contribution to the expected improvement. Manufacturing teams need to reduce operating losses, and maintenance teams need to improve reliability, but there are also times when cycle times or buffer sizes need to be reduced, which can require decision-making and financial resources. Today, thanks to DispoX and the sensitivity analysis that is included, we can clearly identify our short-term (reliability and maintainability) and medium-term (cycle time, inventory) action plans. Thank you to DILLYGENCE for this user-friendly tool, which also allows us to visualize our factory’s problems on a daily basis.
Marc Breuillot
Support Tech Press & Body
Stellantis
To introduce a new platform, we went through a major redesign of our factory. Then when the production restarted, we struggled to reach our throughput target. DispoX is an excellent tool for visualizing the impact of actions on the performance of the lines to be improved, identifying, and prioritizing the most impactful actions. It allows you to see what the factory will look like with the improvements made. Examples of proposed actions include cycle time increase, use of overtime, and buffer size optimization... Thanks to DispoX we were able to converge very quickly towards the target. We saved plenty of time and money by implementing the right-sized solutions. DispoX is an even more efficient tool if it is introduced at the factory design stage!
Didier Delsarte
Manager Press welding
Toyota Motor Manufacturing France
