Business Process Improvement using Cause-and-Effect Analysis and Design of Experiments

InfoManagement Direct, July 2005

Nari Kannan

Business process improvement is the holy grail of any company's operations. Improvements in business processes translate directly to better profits by cutting costs and increasing competitiveness at the same time. In many cases, business process improvements have accelerating cumulative effects on the company's profits. If an insurance company can underwrite policies or settle claims faster, they can provide better service; compete better with nimbler, smaller online competitors; and cut costs, which again help them compete better!

Unfortunately, any company has a limited amount of money to spend on business process improvement and that budget has to compete with many other priorities within the company. If the operations person had the choice, they would put in new hardware, software, applications; hire better qualified people; provide them more training; and have a better working environment. However, practical considerations always force companies to pick and choose the monies they can spend on the right priorities. Now the question is - what are the right priorities? How do you know that the training course on people skills is actually making a difference in customer satisfaction? How do you know that the expensive CRM system you are considering acquiring is going to make any difference? How do you know which one to do first?

This is where a combination of process modeling with cause-and-effect analysis combined with careful design of experiments (DOE) can help a company decide how and where they can allocate the monies they have to maximize their salutary effects on the company. Companies perform design of experiments informally anyway, without realizing it. For example, a smaller group of agents may try out a new customer relationship management (CRM) software application before deciding on acquiring it. Or you may decide to send a small group of your customer service agents to a new training course to test to see if it makes any difference to the quality of service provided. A combination of cause-and-effect analysis and design of experiments will help realize a systematic and scientific approach to doing the same things you already have been doing informally.

Cause-and-effect analysis is a systematic way of generating and sorting hypotheses about possible causes of a problem. Once the root causes of problems are identified, they can be addressed rather than just the symptoms.

A DOE is a structured, organized method for determining the relationship between factors affecting a process and the output of that process. The output of the process is the dependent variable that depends upon the independent variables that determine its outcome.

Let us consider for example a customer service business process. We are trying to track down the causes for poor customer service and fix them. A simple cause and effect analysis for this could look something like this:

Figure 1: Cause-and-Effect Analysis for Customer Service

Here the root causes that determine how good or how bad the end product of customer service might be are hypothesized and sorted in to a standard 3M & P model:

Methods - Methods are the processes and procedures used by customer service to deliver its services. These could be:

• Call Workflow - Poor customer service, real or perceived by the customer, could be an artifact of how call workflow is implemented within the organization. How many times have we complained about waiting on hold or being passed on from person to person when we call for customer service?
• Call Assignment - How are calls assigned? Does the customer interact with the right person within the company that can solve the problem, the very first time?
• Call Escalation - If the first customer service person we talk to cannot solve our problem, whom do we talk to next? Did that help?

Materials - In the context of customer service, these are the policies, work environment, incentive and reward structures set up for the customer service agents within the company.

• Work Environment - Customer service is bound to be poor if the work environment of the person delivering it is poor.
• Incentive Structure - Metrics drive behavior. If the customer service agent is measured on how fast they close calls alone (average handling time), their incentive is to close calls whether or not they have solved your problem, the customer!

Machine - In the context of customer service, these are the tools available to the service agents to do their jobs.

• CRM Application - Most customer service agents use a CRM system these days to keep track of all of their interactions. How good customer service is depends upon how well the CRM system is set up and fulfills the precise needs of the agents when providing service.
• Problem Knowledgebase - Many organizations use a problem knowledgebase to see if the same problem has been solved for another customer.

People - For customer service to be good, there are some skills that the agents need to have:

• Domain Skills - A customer service person trying to resolve a computer hardware problem needs to have the particular domain knowledge to be of help.
• Problem Solving Skills - Customer service delivered over the phone requires a rather systematic approach to problem solving, eliminating obvious causes for a problem in narrowing it down to the root causes.
• People Skills - Perception of the quality of customer service depends upon the people skills of the agent to a large extent.

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