Stage 1 – Reacting
Reacting is where the organization is able to respond to events. It has visibility into system state and alarms.
Reporting of essential variables is focused on the past (last month/ week, yesterday) and the static. Information is on the local or functional level with at best partial integration. Some KPIs exist and are tracked, often monthly and sometimes weekly. Actions are taken when these KPIs are missed; hence this stage is called “Reacting”.
Many organizations are in this stage, even organizations that use advanced Business Intelligence (BI) tools typically still operate in a reactive mode. The accessibility and quality of available data is another reason why companies get stuck in this level in that there is no governance model that able to determine efficacy. Without the ability to bring together data from different sources, both internal and external, reports and analyses are often not accepted across the organization.
Stage 2 – Anticipating
The anticipating stage is possible due to the fact that the organization has processes and procedures in place to react quicker and more effectively. This is mainly achieved by identifying what can go wrong with suppliers, transportation, distribution, etc. and preparing for each of these situations. Applying the Failure Mode and Effects Analysis (FMEA) technique is usually a great starting point as it structures the process to identify what can go wrong, what the possible impacts are (including the possible criticality), and how to respond. The result is a set of processes that align functions both globally and locally. Causal Loop modelling provide further impact on cause and effect as well as emerging properties.
In addition to a conceptual approach, organizations also dive deeper into all their data to analyze past behavior and quantify impacts. This analysis usually leads to better data consolidation across functions and locations. Deeper analysis also helps to identify Key Performance Predictors (KPP). These are KPIs that can function as early warning signs. For example, the turn-around time for suppliers to acknowledge your requested delivery dates can be an indicator of your suppliers’ capacity load and possible future delivery or quality performance.
Stage 3 – Collaborating
In this phase, the SCT starts to become a reality as a central coordination point. At this stage organizations share data with relevant system entities in order to allow management focus is shifted from just anticipating issues to preventing them.
Identification of negative trends, which are the build-up to a possible problem – are shared near real time using alerts and workflows that route information automatically and directly to decision makers, including external decision makers. Any corrective actions are now taken collaboratively to ensure an optimal outcome across the multi-echelon system.
Stage 4 – Orchestrating
In this phase organizations are orchestrating system operational management with the help of a centralized SCT. System operations execution is monitored in the Control Tower and corrective actions are triggered by predictive techniques. Results of all actions and decisions are fed back into the system (first manually, later automatically) and allow for continuous improvements.
Through the integration of analytics capabilities, most issues are detected early and resolved before they ever become issues.