In this multi-part Supply Chain Matters blog series, we aide businesses and associated supply chain managements teams in their focus on the now critical imperative for instilling data and information driven decision-making processes among business processes and having a meaningful advanced supply chain analytics roadmap.
In our prior commentaries we have highlighted continuing efforts on the part of Cloud based software technology providers in expanding offerings in advanced analytics, either in augmenting existing supply chain business process focused applications or in supporting broader and more-timely cross-functional and business-based decision-making.
As an example, last week we highlighted Oracle’s announced general availability of enhanced analytics capabilities specifically purpose-built for linking insights and information relationships among supply chain business processes and contextual business wide and external information sources.
One of the key messages that Oracle reinforced in its announcement is that Oracle Fusion SCM Analytics is designed to serve as the “system of insights” for analytics data related to supply chain performance, key business outcomes or operational scenarios. The notion of “insights” and internal and external based information sourcing was key.
In our prior part one commentary on this topic, we introduced the importance for supply chain management teams in having an advanced supply chain analytics roadmap and architecture, especially now when decision-making has become far more challenged with so many different product demand or supply network disruptive forces. We specifically addressed the process challenges related to an overall roadmap.
In this part two commentary, we will address the workforce readiness challenges.
The roadmap for teams seeking to instill more advanced analytics capabilities follows the proven change management aspects of process-people-technology.
One of the more difficult aspects of strategies related to people or collective teams having enhanced analytics capabilities centers on the understanding of prior approaches as compared to what is really implied by advanced analytics processes.
Process transformation brings considerations for nurturing and instilling inherent advanced analytics capabilities among broader supply chain functional, line-of-business or integrated business planning teams. In today’s advanced approaches, individual workers and work teams are empowered to conduct their own analysis of information and data insights utilizing Cloud based analytics software tools that pre-integrate connections to data and information. Such applications now come with ready-to-use key performance indicator (KPI) as well as built-in advanced analytics capabilities that more easily empower users with inherent data science technology.
Previous existing approaches have manifested specialized in-house IT or data management teams serving as gateways to particular predictive or prescriptive insight request needs.. While organizations will vary in degree or scope of data management, we advocate consideration of the transformational approach because it can better leverage the intrinsic supply chain physical, process and intuitive knowledge of existing analysts, planners or management teams.
A transformational approach can aide in analysis of inherent data across the Source, Plan, Make and Deliver businesses process, both internal and external, transactional, structural or unstructured in nature.
As an example, the COVID-19 pandemic significantly distorted prior historic product demand, supplier and transportation lead information. In some industries, the pandemic provided new market opportunities of product demand that were not previously planned for. Having the ability of individuals and teams to be able to analyze and interrelate information across these broader internal and external process and market dimensions can accelerate planning and execution resource planning and readiness.
In times of actual process disruption, these same tools provide the means to conduct a series of what-if analysis utilizing existing available information, to ascertain a series of potential decisions options based on their specific impact to customer or business impacts, including customer service levels, business revenue or margin impact, or other factors. Embedded machine learning and artificial intelligence technology can further provide early warning to patterns of information that extend beyond defined process parameters, allowing for a time-sensitive response.
Such advanced decision-making capabilities allow individuals and team members to be able to work together spanning existing functional, product or line-of-business business islands of information. That brings teams much closer to true integrated business planning.
Some technology providers are now empowering advanced supply chain analytics with conversational or bot focused interfaces that allow users to interact in their natural language with information needs thru conversation. As an example, Oracle’s newly announced Fusion Cloud SCM Analytics supports 28 different languages for interaction.
In the next iteration of these series Supply Chain Matters will further explore the Technology aspects of advanced supply chain analytics.
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