The case study that will be examined below will exclude specific information that is labeled as private. Some percentage of our case study content has been purposefully restricted in order to protect the privacy of our clients. Public access will not be granted to sensitive information such as company and/or employee names, specific business metrics and internal process descriptions in order to fully comply with the rights and demands of our clients and/or partners that chose to work with Challenge Advisory.
The objectives included the task of improving the average time it takes to manufacture a car from 14-17h period down to 12-13 hours, as this would allow them to manufacture 238 extra cars every single quarter, increasing their profits by an average of 15-20%. Finally, in order to achieve better product to end-consumer interaction, they required a strict set of new metrics to be achieved to gain better and more transparent insights into how their customers are utilizing their product. According to their perspective, Digital Twin Genie was a suitable and cost-effective solution for achieving these metrics and according to our stipulations, the software managed to meet and exceed their expectations – the results and outcomes will be elaborated at the end of the case study.
The final objectives and targets were set as follows:
When it comes to key performance metrics such as profit margins, the biggest hurdle that needed to be overcome by the client was the ever-increasing development and manufacturing costs that were a huge hindrance for growing profit. On the other hand, it was their most important, key internal process that was paramount for launching new model designs and testing their product performance. This was the biggest challenge from the very start of their newest model’s launch as the company was forced to meet their set deadlines from the launch year going forward.
Moreover, considering the pressing challenges that the automobile industry is facing for the long-term, such as adopting new solutions to improve sustainability and adopting a more eco-friendly approach in terms of automobile usage that caused a spike in electric car development, the industry’s competitiveness is forming a substantial gap between fuel-powered and electricity-powered car manufacturers. This causes a direct need for automobile manufacturers to stay on the cutting edge and ensure their brand’s uniqueness, by engineering new solutions such as Digital Twin to enhance productivity.
One of the biggest advantages of fuel-powered car manufacturers is their popularity due to the innate preference the average car driver has, which usually tends to gravitate from electric car manufacturers altogether. However, the competitive landscape is getting harsher and the internal board of our client’s company has made a firm decision to adopt Digital Twin Genie as their first solution to test – we will cover how Challenge Advisory has implemented the changes necessary to help the client reach the objectives they’ve set.
Those key distinctive features of Digital Twin Genie include:
During the total timeframe of the project (14 days) that was dedicated to meeting the initial objectives of our client in the automobile manufacturing industry, we have initiated the installation of Digital Twin Genie. Excluding the multiple meetings that were solely dedicated to covering our A-Z client-centered approach and strategy, the very first core action that was taken was the acquisition of the development kit, which is presented directly to our clients at the very beginning of each project.
However, in this case, the solution had to be completely unique and bespoke in order to fully meet the needs of the company we were working with. Meaning, that the Digital Twin sensors themselves had to be altered in order to have the capabilities of not just tracking the performance and manufacturing speed of the products themselves, but they also had to be specifically built into the core components of their car engines, in order to successfully monitor their performance and gain full clarity on the full spectrum of interactions the customers engaged in with the products themselves.
The second step was the installation of the Digital Twin Genie software into their internal team’s computing devices, that were responsible for controlling the main processes of manufacturing. This helped our Digital Twin experts gain the ability to monitor their equipment and gather data on vital processes, including:
The specific sensors that were used to gain access to these metrics consisted of analog and digital sensors, including temperature, IR, ultrasonic and proximity sensors – all built as bespoke equipment made specifically for the client itself. Moreover, these 4 main types of sensors had to directly cooperate with the software itself in order to make data aggregation available.
The third step revolved around successfully starting to accumulate data from the manufacturing equipment and determine the speed and motion angles using which the company’s tools performed. The motion and proximity sensors were heavily utilized in order to achieve the information we needed and we are going to explain how acquiring all of this information helped us to greatly exceed the expectations of our client.
Although seemingly impressive, the results that were displayed above didn’t represent the best case study that Challenge Advisory has managed to acquire by using Digital Twin Genie. The capabilities the software has due to its machine learning engine that is powered by concepts partially designed by Google (our strategic partner) exceed all current Digital Twin solutions that are in the current market. In addition, we will continue to improve the software, putting our main focus on the accuracy of the collected data.
The total average profit margin increase of 41-54 percent:
The biggest factor that gave us the circumstances to implement such a huge spike in profit margins was the accuracy of data and machine learning capabilities of our software. Meaning, that since our solution was capable of tracking every single move of the equipment that manufactures car parts, we were able to gain full clarity on what moves and actions can be shortened and made quicker – this resulted in lesser electricity consumption and faster performance.
Secondly, the overall downtime of the machines was reduced by 37%. This happened because, in most car manufacturing plants, developers do not have the real-time, direct digital vision of their equipment’s performance compared to the accuracy that Digital Twin technology has to offer. This presupposes that if the machinery breaks, it will need to be repaired, preventing it from producing automobile parts during that timeframe. Digital Twin Genie allowed us to completely eliminate this failure condition by monitoring which part of a specific piece of machinery was about to experience failure, making it easy to be replaced during non-production hours.
Average production time reduced to 9-10 hours:
Meeting this objective was fairly simple in comparison to the other two goals we have been challenged with. The Digital Twin Genie software has a built-in beta version of its custom-made machine learning algorithm. It is important to keep in mind, however, that the premise of the ML program is different than AI. By gaining data about the performance of our client’s processes and tools, we’ve quickly managed to realize that there are severe gaps between specific points of interaction.
The time it took for automated machines to travel from point A to point B took about 1.235 – 1.267 seconds. By utilizing Digital Twin Genie, we have shortened the amount of space the tools needed to cover in order to accomplish the same outcome, down to an average of 0.70 seconds. From a perspective that is extremely zoomed in, this might not seem like a lot, however, the machines constantly perform thousands of motions throughout the timeframe of an entire workday. Because of this, the small speed difference is multiplied thousands of times – the calculations added up to exactly 9-10h that their new process required to manufacture a fully functioning car model.
Enhancing the number of insights received from active customers:
One of the newest features of Digital Twin Genie we have recently built in (the date of the last update was: 03/Dec/2018) is called: predictive data stipulation. The main use of this feature was to track the activity of the physical twin in the digital twin interface even after both twins were disconnected.
This update is the single most important reason that allowed us to give our client the extra insights they needed to figure out how their customers interact with their product, gain full transparency into the average weekly drive times that were incorporated in hundreds of car models and most importantly, what features their manufactured cars are lacking. All of these extra features provided the company with in-depth metrics that were calculated in real-time via their Digital Twin Genie dashboard.