For many organizations, control towers are designed as war-room-like environments that focus heavily on visualization and dashboards, but few are leveraged as analytics-driven, decision-support tools. They are still more functionally siloed in their setup and do not provide the anticipated end-to-end (E2E) visibility, control and decision-making support, representing huge gaps.
What We See Today
Many companies lack E2E visibility, process orchestration and aligned decision making. This is mostly still a vision that applies across many industries, although some are further along in maturity. In order to overcome these challenges, companies have started to implement islands of domain-specific control tower capabilities — but these are not connected and integrated to create an E2E view. Technology-based control towers are often seen in functions such as logistics & transportation or supply chain planning, sometimes called “command centers,” but not as often in customer fulfillment and rarely in procurement.
Supply-chain-control-tower-type capabilities can yield efficiency gains and savings across the supply chain. These impact multiple business value drivers, including lowered cost of goods sold (COGS), selling, general and administrative (SG&A), and therefore increased operating profit (increased revenue and reduced cost), reduced capital expenditure and improved capital allocation. However, these benefits are severely impeded by the functional and/or domain focus of control tower offerings.
What Organizations Look For
Visibility is a necessary foundation and first step for organizations, but then there is a need for advanced deep analytics (predicting), providing scenario-based options for the next best action (prescriptive) and decision support to optimize the outcome (simulating and responding).
Companies with a complex and diverse application architecture and landscape are often implementing data lakes with the desire to achieve this E2E visibility and control. Different data sources are hereby leveraged and then analytics are applied. However, the downside is that such data lakes create multiple versions without a “single version of the truth.” And when further applying analytics — or data science — it is without any association of the data into an E2E digital representation of the supply chain.
As a result, data lakes provide an endpoint for collecting transactional, detailed data (and possibly other types of data), specifically to support the execution of analytic workloads. However, note that they are mainly descriptive, diagnostic and functional in scope, not predictive, descriptive and E2E. Additionally, data lakes do not offer scenario planning and advanced collaboration features. They also do not offer real-time collaboration or to identify and contact other users for the purposes of sharing, chatting about and annotating content — all of which are needed for intelligent E2E decision making.
When talking to companies that have gone down the path of establishing data lakes, one found that it had created a “data lake monster” with another company, having moved from a data lake to a data ocean and facing what it called a “data swamp.”
What Would Lay The Foundation For an End-to-End Supply Chain Control Tower?
Businesses are looking for a capability that simulates preparedness and response to disruption, and provides insights into the E2E dependencies and impacts across units and partners; then acts on those insights so that decisions become actions. This represents the main characteristics Gartner references in its frameworks for supply chain visibility (SCV) and E2E planning, to ultimately achieve supply chain convergence. The following are the main characteristics in Gartner’s SCV framework:
- See — Get real-time or close to real-time visibility and clarity
- Understand — Leverage and analyze signals from the digital ecosystem
- Act — Intelligently optimize response for best outcome
- Learn — Continuously learn from the above three steps
What is needed to move from visibility and/or visualization toward intelligent and optimized decision support is, therefore, a digital representation of the physical supply chain. This is what Gartner calls the “digital supply chain twin.” The aim would be to have a digital representation of the E2E supply chain. As of now, we see occurrences mainly in areas such as factory lines, warehouses or specific processes such as order to cash. The digital supply chain twin is built from granular data to form a dynamic, synchronized, real-time and time-phased representation. The digital twin represents the various associations between the data objects and entities that ultimately describe and make up how the E2E physical supply chain integrates and operates.
All this requires the domains to work together — all using this digital supply chain twin. This leads to that three-layer model of E2E visibility, E2E process orchestration and E2E aligned decision making. This might honestly be the death of plan, source, make, deliver — the well-known and established Supply Chain Operations Reference (SCOR) — as we need to think in layers and stop thinking in terms of functions and/or domains.
In conclusion, no company has found that Holy Grail — a single tool with the above described capabilities available on the market. No one has found that one tool which enables analytics and visualization to drive better alignment with decisions based on the same set of relevant information which comes from different functions, providing E2E SCV combined with E2E intelligent decision support.
A software occurrence where this concept is starting to be applied is an advanced planning solution or a multienterprise supply chain business network with planning capabilities included. If fully erected, properly configured and comprehensively used, such a cohesive platform could provide the anticipated digital supply chain twin as a foundation in support of a real E2E control tower.
Gartner Supply Chain
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