This posting is a continuation of a multi-part Supply Chain Matters market education focused series. The theme is to aide businesses and associated supply chain management 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 part one commentary in this specific series, we cited the importance for supply chain management as well as integrated business planning teams in having an advanced supply chain analytics roadmap and architecture. This is especially important now when decision-making has become far more challenged with so many different product demand or supply network disruptive forces. We specifically addressed the area of process challenges related to an overall roadmap. We emphasized what comes to the fore in numerous interviews and survey data, that most organizations today believe they are data rich but insights poor, when it comes to more proactive supply chain decision-making. There is the added reality that there remains too many islands of functional or line-of-business information, often lacking outside-in and externally related information.
Our part two commentary was focused on workforce readiness and the specific area of the sustaining talent elements for having an advanced analytics decision-making capability. We reinforced that process transformation brings considerations for nurturing and instilling inherent forward-looking analytics driven decision-making among broader supply chain, line-of-business and integrated business planning teams. We pointed out that a transformational roadmap should umbrella Source, Plan, Make and Deliver business processes, both internal and external, and structural and unstructured in context.
In this part-three commentary we will focus on advanced technology considerations.
Leveraging of Advanced Technology
When considering where technology plays an enabling role, the likely most important consideration is in analytics software that has both been developed and pre-integrated for the ability of many levels of business and functional users to be able to readily adopt the applications capability.
The goal of the technology should be in the ability to readily support teams in transforming to data-driven insights decision-making. MIT Professor David Simchi Levi describes a further goal of transforming from assessing key performance indicators (KPI’s) to key performance predictors (KPP), namely sensing and identifying what is likely to occur in specific time dimensions, and what demand or supply network actions will best buffer the impacts
Pre-integrated is especially crucial in the ability to embed data science and machine learning knowledge within the software, and in automated means to source contextual outside-in data and information related to changing product demand, market changes, or out-of-balance supply network conditions. This helps in being able to readily garner a more unified view of product demand, supply, and market conditions.
Automated workflow and user guidance support is important as-well. The ability to now be able to leverage embedded machine-learning and artificial intelligence capabilities to readily identify patterns or root causes of exceptions, to identify early warning of anticipated disruption are a further benefit of today’s advanced analytics technology.
In today’s global supply chain landscape, outside-in implies not only enterprise level, but the reality of collections of behind-the-firewall or hybrid B2B Cloud platform applications and data stores that can provide added context and insights in decision-making. The now increased clock speed of changes that are occurring across global and nearshored supply chains further require an architecture that supports capturing streams of data rather than batching or customized runs.
As with all planning and decision-making support applications, ease-of-use is a crucial consideration. Some rightfully argue that ease of use is the most crucial determinant to decision-making enhancements. It includes not only pre-built business performance indicators and information dashboards, but increasingly conversational based interaction with the application allowing for interactive dialogue of a system of insights to help users refine their requests or better context the specific information desired.
Quicker time to value and broad based cross functional and business adoption accelerates the roadmap as users gain confidence in the tool and its capabilities. Timetable of available functionality, the actual Cloud based platform (s) hosting advanced analytics, along with overall cost needs to be weighted by selection teams.
Some ERP, best-of-breed supply chain or core artificial intelligence technology providers currently offer different timetables and approaches. In the case of Cloud based applications, an innovation cycle of continuous quarterly releases of added features and functionality assures that time-to-vale needs can be supported in a timelier manner. In many cases, weighting may be more centered on partnering with a more customer-centric partner that is vested in your organization’s success in manifesting advanced analytics decision-making across the business.
As an example, Oracle recently announced general availability of enhanced analytics capabilities specifically purpose-built for supply chain needs. During the Future of Business online event, a recorded eight-minute video of interview with the CIO of Corsair Gaming provides a depiction of the challenges that were encountered in better dealing with supply chain disruptions that were directly impacting the company’s revenue potential. The video describes how business process and a systems environment that were heavily customized became transformed by standard out-of-the box advanced analytics capabilities. If further provides one customer’s experiences with empowering work teams in these capabilities.
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