Cognitive Control Towers: Start Small, Think Big and Move Fast

Cognitive Control Towers: Start Small, Think Big and Move Fast

Build resilience, reduce costs, and plan ahead with end-to-end visibility for supply chains.

The typical challenges facing a supply chain have always included balancing the needs of the customer with the supplier network, available workforce, and the inherent financial risks in both over- or under-production.

What is changing through modernization is the interconnectedness of the various elements; in fact, Deloitte now calls it the Digital Supply Network rather than the Digital Supply Chain. Theoretically this interconnectedness improves the ability to incorporate improved risk management, but only if the information can be aggregated in real-time.

In the era of COVID-19, all sorts of businesses faced the same supply chain challenges:

  • Respond to the market changes
  • Recover from the unexpected
  • Thrive in the aftermath

The only way to meet these challenges is with end-to-end visibility in the chain, also referred to as “actionable visibility.”

This way, companies can get out of “firefighting” mode and anticipate needs before a crisis point.

Adaptable Plans for An Uncertain World

COVID-19  and the ensuing challenges only served to highlight the inadequacies in the planning processes of many large enterprises. Most reactions have been based on primitive market sensing, which leaves a lag (say, waiting until a purchase order arrives to turn supply chain back on, at which point it is too late).

The challenge is to figure out what indicators mean the market is about to change, and old forecasting models haven’t stood the test of time.

There has long been a pervasive mindset that the “perfect” plan will work, when in fact Jeff Bezos has wryly noted that, “Any plan won’t survive its first encounter with reality.”

Therefore the key is to shift mindsets toward adaptability. Having a plan may have mostly worked in a pre-COVID-19, steady-state world, but now everything is shifting, and companies have to respond in kind.

Organizations tend to focus on profitability and liquidity, which can mean spending too much time identifying issues and then scrambling to triage. In fact, making data more visible and accessible, and assuming pivots will be necessary, frees up time to respond to inevitable issues.

Intelligent Execution with Cognitive Control Towers

A Cognitive Control Tower (CCT) is defined by certain characteristics:

  • It sits on top of an existing management system
  • It is applied to a specific issue (typically available to promise, on a supply chain) to focus on exceptions from the norm
  • It has real-time data connectivity
  • It drives process improvements via insights enabled by advanced analytics.

A CCT allows for intervention before a problem becomes critical. A successful CCT will track the signals of the specific business issue from the first symptom to the root cause and enable decision-making using Cognitive Automation.

Four pillars of Cognitive Automation necessary to achieve a successful CCT

  • Understanding of what is happening, achieved with data which is readable through machine learning and then harmonized/indexed. This is done by bringing together and synthesizing information that is typically siloed.
  • Recommendation, where applied science enables an evaluation of potential responses to the identified deviation, along with cost/benefit analysis, and these algorithms should also continuously adjust based upon which recommendations are utilized and which are rejected.
  • Impact Prediction, with real-time ROI analysis via a fully digitized process.
  • Action can be taken in the form of appropriate operational change.

Cognitive Control Towers create benefits such as margin improvement, increased revenue, and greater asset efficiency.

In order for CCTs to be successful, however, organizations have to be willing to change the way they work.

A CCT can’t be plopped down on a system while business continues as usual; it requires a workforce shift away from data accumulation and toward dynamic decision making.

Algorithms alone, no matter how intelligent, will not replace people – they’ll simply allow workers to be as well-informed as possible before making key decisions.

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