Q 211. VUCA - Volatility, Uncertainty, Complexity, Ambiguity - These seem to be interesting situations that the corporate world finds itself in. VUCA situations have been studied and explored to some extent. Explain each element of VUCA with three different Industry examples. Also explain how the four situations can be tackled with the help of Business Excellence approaches. 

 

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Benchmark Six Sigma Expert View by Venugopal R

 

VUCA is an acronym coined by American military for addressing certain extreme war time situations. The fast-paced world has led to corporate leaders finding VUCA applicable to many corporate situations. I would like to share my thoughts and experiences as below.

 

Example1

One of the common VUCA situation in the corporate world is during mergers and acquisitions.

 

Volatility can be expected with several changes happening simultaneously. viz. New senior leadership formation, Organization reshuffle, New Clients, Products & Services, New geographies, New Processes and Operating platforms and so on.

 

Uncertainty will prevail among employees, shareholders and other stakeholders. Long term customers and suppliers could get perturbed and could be left wondering about whether they would be impacted by the change.

 

Complexity may arise out of need for integrating different workflows, processes and platforms. Synergizing operations, sites, technologies and functions would also present complexities.

 

Ambiguity crops up due to possibilities of multiple interpretations of same messages, policies and procedures and reporting structures. Another area of ambiguity that is likely to hover for some time  could be the name of the combined organization.

 

Example2

Another situation that comes to my mind is during the time when the city was affected by severe floods.

 

We saw volatility in the form of our normal life getting disrupted overnight and the damages spurting up in various forms. Our office sites became inaccessible, all communications were disrupted, all modes of power failed, employees got stranded, some critical equipment got submerged and so on.

 

We were uncertain about how long it would take to get back to normalcy. We did not know how much worth of damages have happened and what more may happened. We did not know about the safety of employees and had no ways of communication. We did not know how much our customers would have got impacted due to delivery issues and how they would look upon us.

 

What were the complexities that we confronted? Our BCP / DRM strategies swung into action. We had to divert our work to other sites that were not affected and to sites overseas. This resulted in employing of different resources to handle new work, with their limited familiarity and training. Damage to critical equipment that was part of the back up process resulted in data not being accessible to provide support to the remote locations. Since the entire city was impacted, rescue and draining equipment were of high demand and not easily available. Many employees had emergencies at their home as well and balancing of priorities between saving home and office was challenging. Arranging logistics to safely move employees, who were available to workable locations from their homes and providing them food proved complex in such a disastrous situation.

 

Ambiguity prevailed with multiple inputs and information coming in about the status of the conditions of or sites and homes of employees. It was difficult to get to agreement on priorities with the limited resources available. Providing any sort of commitment to clients about returning to normalcy was very difficult with inconsistent and ambiguous inputs.

 

Example3

Moving to another scenario, we had once faced a situation when there was a mass exodus of employees from a particular division, most of them being lured by a competitor in the city who suddenly setup an operation for the same services.

Volatility was seen as the speed at which we were losing employees. With more people leaving and rumors spreading, it became contagious and in a very short time, more than 80% of the employees in that division had disappeared.

Uncertainty was further fueled by the volatility, with rumors spreading about possible closure of division and many employees were in a dilemma whether to continue or to quit. The management was uncertain about how much of loss that would be caused and whether we would be able to retain the customers.

Complexities included inducting employees from other divisions and to frantically look for hiring new employees, getting them trained and exposed to the client requirements and processes without disrupting deliveries and accuracy levels.

Ambiguous inputs were received in terms of the number of employees being lost and what may be potentially lost. The cause for getting led into such a situation was being presented and interpreted by various people in different ways, whether it is the company, the competition or the customer who is responsible for causing the exodus.

 

Dealing with VUCA

Coming to discuss about the Business Excellence practices that could help during VUCA situations, we may look at each of the 4 components of VUCA. However some of the practices would help across VUCA.

 

One of the essential practices is to have a good Business Continuity Plan and Disaster Recovery Management plans. This will be useful for unexpected situations that may emerge due to natural and other causes. Volatility coupled with uncertainty can result in ‘Not knowing the unknown’ and hence a risky situation. Anticipate potential failures and get prepared for worst situation. PFMEA might help us to assess each potential risk and classify their priorities and help building our preparedness.

 

Proactive Multiskilling and multi-tasking of resources would help in situations where we have heavy fluctuations in product sales or on employee turnover.

 

Complexities can arise out of interconnection between processes and activities and interdependence of multiple decision factors. Entity relationship diagrams will help to define and depict relationships between multiple entities. We may use SIPOC for high level depiction of a process in a complex situation and if we are able to obtain sufficient data, we may use multiple regression models.

 

Ambiguities arise because we have a room for multiple interpretations of the same data coupled with insufficient data. One of the approaches in statistical decisioning is by attaching a confidence level for any inference. Thus even though we are unable to decide something with 100% certainty, we are able to take a practically applicable interpretation by attaching a quantified risk. The risk levels could decrease with improved quantum of  inputs and over time.