In this Supply Chain Matters blog commentary, we wanted to 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 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 that vein, this Editor wanted to elaborate further on 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.


Roadmap Essentials

The roadmap for teams seeking to instill more advanced analytics capabilities follows the proven change management aspects of process-people-technology.

In this and subsequent postings, we will share perspectives for each of these areas.

We will begin with process.


Process is coming to the realization that for most organizations today, teams are data rich and insights poor, especially when it comes to supply chain operational and execution information. There is simply too much transactional and operational focused data being tracked. Organizations often describe their frustration in not having the ability to ascertain early warning or detect changes in business operations until after the fact.

Required is a meaningful context to constantly changing external demand and supply network conditions or to being able to directly assess supply chain impacts to customer service level, targeted revenue or business profitability. It is often the Pareto grounded realization that a limited, more concise level of tracked information has the most context and influence on outcomes. Thus, a cohesive data sourcing, mapping and management strategy is a must.

The effects of the COVID-19 pandemic have distorted existing product forecasting, planning or lead time information since businesses, suppliers, and customers have been restricted by external and cascading disruptive events. Planning and decision-making now entails sensing of changing patterns and definitive scenarios, given a set of possible outcome options.

There is the added reality that for many organizations, there remains too many islands of functional, business or important external information. Legacy applications that predicated decision making needs on aggregating historical business transactional information were limited in the ability to context any external structured or unstructured information related to business and operational resource needs.

Cross functional teams have always sought to have information aggregated in one place, but prior to Cloud based applications and today’s more advanced data management and analytics capabilities, the challenge turned out to be a complex, arduous and often expensive. This is why IT teams were increasingly called upon to cobble together required insights, often times requiring customization of data feeds which added more complexity. Inherent knowledge of SQL data inquiry techniques were often compounded by trial and error, or misinterpretations of what was really required in the analysis because of a lack of user sponsor specifics and business process knowledge Time and added cost added to frustrations since teams were dealing with primarily data warehousing vs. analytics focused applications.

An analytics road map therefore requires an emphasis on continuous sensing of relevant internal and externally sourced information, where such data originates, with solid data analysis and management techniques accessible to broad based functional and business users.

In follow-on postings, we will additionally address people and technology considerations for achieving analytics-driven decision making.

Bob Ferrari

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