New technologies are changing and accelerating digital transformation, intervening in existing and established business models, offering new opportunities and challenges for the market and the automotive industry.
Keeping up with the technological evolution of new models brought to market requires that manufacturers balance a general contraction in profit margins with the necessity to continue to invest in research and development, design and product innovation in order to maintain competitiveness in the eyes of consumers. Therefore, all of these changes are adding complexity to the automotive supply chain, taking it to unprecedented levels, driving an increasing need for automation and collaboration and, above all, highlighting any weaknesses in the supply chain.
sedApta covers all the needs of the industry: from manufacturers, known as OEMs “Original Equipment Manufacturers,” to parts companies known as “after-markets,” and suppliers, supporting the entire supply chain to better address and overcome the daily challenges.
How do automotive companies know what to produce? Quite often these find themselves having to make sales forecasts not only on the item they want to produce, but rather taking into account forecast levels on a sales volume basis.
To fulfill sales forecasts, they need to rely on a support structure to manage the cooperative’s inventory, demand, supply and production planning processes.
Not only the demand side, but also the sales network and interpersonal contacts could be a critical issue for companies in the industry: dealers, branches, etc., are all players that need to be taken into account in order to optimize Sales and Operation Planning operations, enabling companies to achieve their business goals.
The sedApta approach, based on the concept of process orchestration, enables synchronization and coordination of all the process actors, transferring the right information at the right time, to the right role, to efficiently conduct a specific process activity and aligning planning, scheduling and execution.
The pandemic, the increasing interdependence among industries, adverse events, the complexity of global supply chains, and shipping capacity constraints have all contributed to material shortages in many industries, including automotive.
Material shortages generate a planning problem both in the case of delay and in the case of lack of materials leading the company to make different decisions from what it had originally planned.
Both cases have as a common denominator the need to orchestrate internal business processes as debates will open up among chain actors in order to find the optimal solution.
In this way, material tracking activities will be integrated with planning processes, supporting companies to generate a short-term plan for vehicle assembly.
The pursuit of production planning has become one of the company targets: planning on a short-term, finite-capacity basis so as to understand the “amount of work” that can be produced in a certain period of time, and figuring out how to manage assembly sequences, is one of the other challenges in the automotive industry. A challenge that, if unsuccessful, would put the company in serious difficulty.
To be able to handle “last minute” changes due to material shortages, bottlenecks and manpower capacity, fast and effective responsiveness is increasingly required: Digital twin, orchestrated processes, Analytics Bricks, Simulative Control Tower-which enables structured process collaboration between people, activities, and tools across the supply chain in real time-are the core elements of the sedApta platform. Specifically, these make it possible to synchronize and coordinate all process actors, providing them all the necessary information, at the right time, to efficiently conduct a specific process activity.
Factory data detection
Another hot topic affecting the automotive supply chain is the integration 4.0 between factory tools and the actors involved in the process, for the collection of production data and real-time control of progress.
This is a key element of the digitization process, to which all manufacturing companies, including those in the automotive sector, must adapt because of the constant demand variations that make it necessary for companies to be able to react quickly.
Identifying, predicting, and preventing process inefficiencies by transforming data into usable information are the goals sedApta sets with its artificial intelligence solution in machine learning algorithms. Autonomously analyzing and understanding the available data, quickly processing all possible scenarios, identifying among various alternatives the least impactful solutions, and returning an appropriately selected subset of scenarios to other process players are the perfect mix and match to support automotive companies in their business.