Report introduces concept of “Speedsters” — companies that have excelled at compressing time to market
A new report from Accenture details how new technologies are helping industrial enterprises compress the time it takes to design, develop and deliver products to customers, also known as “speed to market.”
The report, “Industrial Speedsters: How advanced technologies can turbocharge your speed to market,” is based on a survey of 1,200 executives in the industrial and electrical equipment, heavy equipment, industrial supplier and consumer durables sectors across 13 countries. As part of the research, Accenture examined three processes of the time-to-market cycle:
- Idea to Product — which includes idea generation, concept planning and prototyping, testing, design validation and requirement development to prepare for the start of production;
- Plan to Produce — comprising production planning, production scheduling and production execution (a.k.a. manufacturing operations);
- Demand to Deliver — which includes demand and sales planning, order intake and scheduling, final distribution, and installation/commissioning at the customer/client site.
Accenture identified companies that had the shortest processes within each of the three processes, then analyzed which had leveraged advanced technologies — including machine learning and other artificial intelligence (AI) technologies, cloud, digital twins, and high-performance computing, among others — to reduce time and costs.
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Accenture then broke the companies into three categories: Those that reduced time and increased efficiencies the most were labeled “Speedsters” (14% of companies); those that reduced time and increased efficiency the least — accounting for nearly two-thirds (63%) of companies — were labeled “Starters;” and those in the middle were labeled “Accelerators” (23% of companies).
The research found that Speedsters achieved greater time and cost reductions due to a significantly higher technology leverage in all three speed-to-market processes. For instance, through the use of machine learning, Speedsters achieved time and cost savings more than seven and 12 times that, respectively, of Starters. For instance, Speedsters that used Machine Learning were seven times faster and 12 times more cost-effective than the Starters. The use of automated guided vehicles enabled Speedsters to achieve time savings four times that of Starters — and cost savings approximately 30 times that of Starters. Speedsters that used automated guided vehicles were four times as fast and 30 times as cost-effective as the Starters.
Just as important, the research found that Speedsters outperform both Starters and Accelerators in terms of financial performance. For instance, for the five-year period from 2016-2021, Speedsters achieved four percent higher annual growth than Accelerators and 18% higher than that of Starters. Speedsters also achieved on average, higher operating margins than both Starters and Accelerators.
“A company’s ability to produce and deliver more goods in less time and at lower cost is a key competitive advantage,“ said Thomas Rinn, who leads Accenture’s Industrial practice globally. “Our research shows that advanced technologies such as AI, cloud, digital twins and high-performance computing play a critical role in enabling this.”
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The report makes several notable recommendations for how companies can most effectively utilize advanced technologies in each of the three stages. These include:
- Idea to Product. Companies should use feedback and usage data from digital product twins, as well as data from IoT along the lifecycle of connected products, to improve products, software and services. Additive manufacturing and 3D printing can accelerate producing physical prototypes based on their virtual engineering efforts. In the near future, they should take advantage of cloud-based high-performance computing (HPC) and quantum computing to support simulations.
- Plan to Produce. To address the challenges in this area, companies should use simulation and virtual commissioning tools to enable product engineering to work concurrently with production engineering. They should also create an overarching digital twin of production processes that can help them to simulate and optimize operations for overall equipment effectiveness, yield and efficiency on an ongoing basis. Looking further ahead, they could leverage AI for predictive supply chain and production management; and use extended reality and the industrial metaverse to create opportunities for more effective training and guidance for production employees.
- Demand to Deliver. Companies should focus on integrating manufacturing with the supply chain to create a “digital thread” that enables the seamless flow of data across organizations and silos; enables better distribution, supply chain network optimization as well as supply chain planning; and opens the door to create a digital twin of products and operations to support collaboration in the value chain. In the longer term, companies should explore AI-enabled, request-for-proposal processing, and the use of the commercial metaverse in digital sales processes.
