Product innovation is often thought of and reported on a grand scale or very specifically related to a new product feature. As an example, we know quite a bit about the introduction of the Apple iPhone in 2007 and how that has transformed our everyday lives. More specifically, in 2017, the inclusion of facial recognition, used for secure unlock of our phones, was a great workaround for the challenges password management can present. These are strong examples of product innovation we all recognize, but the untold story of the continuous innovation required to achieve these ultimate product goals is hidden in processes every company must manage every day.
To get to the point of product launch, thousands of hours can be invested in analyzing sales data, competitive pricing and features, demographics, social trends and so on. For companies innovating a completely new product or category like the iPhone, this analysis can be even more time consuming and require more risk taking. However, most companies launch new products in line with existing products or services for customers they have worked with and understand; time to launch can be a critical success factor.
If marketing analysis projects a profitable market and significant demand, the supply planning and finance teams must design the sourcing, manufacturing, distribution and inventory strategies, negotiate contracts, and plan capacity for a profitable launch. In companies with robust integrated business planning processes, these activities will fall into a well-tested cadence, and are more likely have a positive outcome at launch.
Product innovation, therefore, has its own supply chain of supporting activities, its own requisite bill of materials and often requires updates to and from product data managed in product lifecycle management and ERP systems. To be successful, PLM and product master data management (MDM) systems must be connected to existing operational and IT systems (such as supply chain planning and execution). This is sometimes referred to as “the digital thread”, especially when working with IoT-connected devices. The required interoperability of multiple systems can be a major hindrance to the speed and effectiveness of product launches.
Oracle Supply Chain Planning, as part of the Oracle Cloud, shares common data models and user interfaces with Oracle Product LifeCycle Management (PLM) and Oracle Enterprise Resource Planning (ERP). Pre-built integration eases data management challenges between planning bills of material and product master data, a key success factor for accelerated product innovation.
Oracle Cloud Applications can also improve the success of product innovation with our built-in machine learning algorithms, which can perform rapid and insightful analysis on sales, planning and marketing data. The system can recommend product features that you could combine to create new products or categories, and even suggestions of when and where to launch. Some companies are even successfully mining social media feeds for ideas to innovate faster and more accurately.
In 2020, Oracle introduced the Oracle Planning Advisor feature as part of both Oracle Cloud Demand Management and Supply Planning. Artificial intelligence supports Planning Advisor fusing pre-built machine learning algorithms to give supply chain planners insight from big data problems, like those associated with product innovation and launch. If the pre-built algorithms aren't an exact fit for your needs, we provide tools and educational resources to help you build new algorithms better-suited to your business.
Supply planners can use Planning Advisor to determine likely supply or capacity bottlenecks—using data from operational systems, including Internet of Things (IOT) data, tracking logistics, or production status. You can explore multiple scenarios and plans to determine actions that offer the least risk or cost.
Supply chain and finance executives who want to accelerate and refine innovation should evaluate the benefits of a unified platform vs. the challenges of multiple integrated systems, likely involving multiple cloud strategies.