Delay in the onboarding of new products
The client had planned to launch approximately 50,000 SKUs within a few weeks. It was crucial to their success as their success depends heavily on how they develop and market new products. But given their legacy PLM system, the approximate time needed for the same would stretch to months instead of weeks. As a result, it would lend their competition an unwanted advantage, and the brand would unwantedly lose market share.
Missing enterprise-wide data model and platform to harness data
The adoption of Industry 4.0 saw most industries looking to utilize PLM data with greater efficiency and for a myriad of purposes. As with this client of ours, it has acquired several companies since 2011, and there was a need for robust cloud-based software that could increase the number of users part of the system. In addition, there was a push to improve the way data was generated throughout their supply chain, and the data model was managed. But they did not have an enterprise-wide solution that could encompass all the data available and provide them with the requisite output.
Inability to curb the rising cost of data management
Data sharing among PLM entities is a great way to streamline and improve product lifecycle management, but there is always a risk of increased cost if the data is incorrectly captured, analyzed, or disseminated. For example, the client operated through multiple divisions and was trying to improve operational productivity, customer experience and ultimately drive growth/revenue. For this, they would require expedited commercial enablement only possible if their PLM processes across divisions and business units were in alignment. Unfortunately, they experienced a spike in costs when they tried doing it, and several management issues plagued the process.
Inefficiency in end-to-end data workflow management
Given the plethora of processes across the PLM chain and the presence of several divisions and business units, The Life Science leader struggled to improve their workflows, data models, and product class structure. In addition, they had a hard time finalizing the approval metrics for tracking the same.