The Connection Between Six Sigma and CRM

Six Sigma is an industrial business strategy directed at improving the quality of process outputs by eliminating errors and system variables. The end objective is to achieve a state where 99.99966% of events are likely to be defect free. This would yield a statistical rating of Sigma 6 hence the name.

The process itself is thankfully more user-friendly. It presents a model for evaluating and improving customer relationships based on data provided by an automated customer relations management (CRM) system. However in the nature of human interaction we doubt the 99.99966% is practically achievable.

Six Sigma Fundamentals

The basic tenets of the business doctrine and the features that set off are generally accepted to be the following:

  1. Continuous improvement is essential for success
  1. Business processes can be measured and improved
  1. Top down commitment is fundamental to sustained improvement
  1. Claims of progress must be quantifiable and yield financial benefits
  1. Management must lead with enthusiasm and passion
  1. Verifiable data is a non-negotiable (no guessing)

Steps Towards the Goal

The five basic steps in Six Sigma are define the system, measure key aspects, analyse the relevant data, improve the method, and control the process to sustain improvements. There are a number of variations to this DMAIC model, however it serves the purpose of this article. To create a bridge across to customer relationships management let us assume our CRM data has thrown out a report that average service times in our fast food chicken outlets are as follows.

<2 Minutes 3 to 8 Minutes 9 to 10 Minutes >10 Minutes
45% 30% 20% 5%
Table: Servicing Tickets in Chippy?s Chicken Caf?s

Using DMAIC to unravel the reasons behind this might proceed as follows

  • Define the system in order to understand the process. How are customers prioritised up front, and does the back of store follow suit?
  • Break the system up into manageable process chunks. How long should each take on average? Where are bottlenecks most likely to occur?
  • Analyse the ticket servicing data by store, by time of day, by time of week and by season. Does the type of food ordered have a bearing?
  • Examine all these variables carefully. Should there for example be separate queues for fast and slower orders, are there some recipes needing rejigging
  • Set a goal of 90% of tickets serviced within 8 minutes. Monitor progress carefully. Relate this to individual store profitability. Provide recognition.

Conclusion

A symbiotic relation between CRM and a process improvement system can provide a powerful vehicle for evidencing customer care and providing feedback through measurable results. Denizon has contributed to many strategically important systems.?

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Big Energy Data Management

Recent times have seen the advent of cloud based services and solutions where energy data is being stored in the cloud and being accessed from anywhere, anytime through remote mobile devices. This has been made possible by web-based systems that can usually bring real-time meter-data into clear view allowing for proactive business and facility management decisions. Some web based systems may even support multi utility metering points and come in handy for businesses operating multiple sites.

Whereas all this has been made possible by increased use of smart devices/ intelligent energy devices that capture data at more regular intervals; the challenge facing businesses is how to transform the large data/big volume of data into insights and action plans that would translate into increased performance in terms of increased energy efficiency or power reliability.

A solution to this dilemma facing businesses that do not know how to process big energy data, may lie in energy management software. Energy management software?s have the capability to analyse energy consumption for, electricity, gas, water, heat, renewables and oil. They enable users to track consumption for different sources so that consumers are able to identify areas of inefficiency and where they can reduce energy consumption, Energy software also helps in analytics and reporting. The analytics and reporting features that come with energy software are usually able to:

? Generate charts and graphs ? some software?s give you an option to select from different graphs

? Do graphical comparisons e.g. generate graphs of the seasonal average for the same season and day type

? Generate reports that are highly customisable

While choosing from the wide range of software available, it is important for businesses to consider software that has the capacity to support their data volume, software that can support the frequency with which their data is captured and support the data accuracy or reliability.

Energy software alone may not make the magic happen. Businesses may need to invest in trained human resources in order to realise the best value from their big energy data. Experts in energy management would then apply human expertise to leverage the data and analyse it with proficiency to make it meaningful to one?s business.

Which Services to Share?

It often makes sense to pool resources. Farmers have been doing so for decades by collectively owning expensive combine harvesters. France, Germany, the United Kingdom and Spain have successfully pooled their manufacturing power to take on Boeing with their Airbus. But does this mean that shared services are right in every situation?

The Main Reasons for Sharing

The primary argument is economies of scale. If the Airbus partners each made 25% of the engines their production lines would be shorter and they would collectively need more technicians and tools. The second line of reasoning is that shared processes are more efficient, because there are greater opportunities for standardisation.

Is This the Same as Outsourcing?

Definitely not! If France, Germany, the United Kingdom and Spain has decided to form a collective airline and asked Boeing to build their fleet of aircraft, then they would have outsourced airplane manufacture and lost a strategic industry. This is where the bigger picture comes into play.

The Downside of Sharing

Centralising activities can cause havoc with workflow, and implode decentralised structures that have evolved over time. The Airbus technology called for creative ways to move aircraft fuselages around. In the case of farmers, they had to learn to be patient and accept that they would not always harvest at the optimum time.

Things Best Not Shared

Core business is what brings in the money, and this should be tailor-made to its market. It is also what keeps the company afloat and therefore best kept on board. The core business of the French, German, United Kingdom and Spanish civilian aircraft industry is transporting passengers. This is why they are able to share an aircraft supply chain that spun off into a commercial success story.

Things Best Shared

It follows that activities that are neither core nor place bound – and can therefore happen anywhere ? are the best targets for sharing. Anything processed on a computer can be processed on a remote computer. This is why automated accounting, stock control and human resources are the perfect services to share.

So Case Closed Then?

No, not quite. ?Technology has yet to overtake our humanity, our desire to feel part of the process and our need to feel valued. When an employee, supplier or customer has a problem with our administration it’s just not good enough to abdicate and say ?Oh, you have to speak to Dublin, they do it there?.

Call centres are a good example of abdication from stakeholder care. To an extent, these have ?confiscated? the right of customers to speak to speak directly to their providers. This has cost businesses more customers that they may wish to measure. Sharing services is not about relinquishing the duty to remain in touch. It is simply a more efficient way of managing routine matters.

Directions Hadoop is Moving In

Hadoop is a data system so big it is like a virtual jumbo where your PC is a flea. One of the developers named it after his kid?s toy elephant so there is no complicated acronym to stumble over. The system is actually conceptually simple. It has loads of storage capacity and an unusual way of processing data. It does not wait for big files to report in to its software. Instead, it takes the processing system to the data.

The next question is what to do with Hadoop. Perhaps the question would be better expressed as, what can we do with a wonderful opportunity that we could not do before. Certainly, Hadoop is not for storing videos when your laptop starts complaining. The interfaces are clumsy and Hadoop belongs in the realm of large organisations that have the money. Here are two examples to illustrate the point.

Hadoop in Healthcare

In the U.S., healthcare generates more than 150 gigabytes of data annually. Within this data there are important clues that online training provider DeZyre believes could lead to these solutions:

  • Personalised cancer treatments that relate to how individual genomes cause the disease to mutate uniquely
  • Intelligent online analysis of life signs (blood pressure, heart beat, breathing) in remote children?s hospitals treating multiple victims of catastrophes
  • Mining of patient information from health records, financial status and payroll data to understand how these variables impact on patient health
  • Understanding trends in healthcare claims to empower hospitals and health insurers to increase their competitive advantages.
  • New ways to prevent health insurance fraud by correlating it with claims histories, attorney costs and call centre notes.

Hadoop in Retail

The retail industry also generates a vast amount of data, due to consumer volumes and multiple touch points in the delivery funnel. Skillspeed business trainers report the following emerging trends:

  • Tracing individual consumers along the marketing trail to determine individual patterns for different demographics and understand consumers better.
  • Obtaining access to aggregated consumer feedback regarding advertising campaigns, product launches, competitor tactics and so on.
  • Staying with individual consumers as they move through retail outlets and personalising their experience by delivering contextual messages.
  • Understanding the routes that virtual shoppers follow, and adding handy popups with useful hints and tips to encourage them on.
  • Detecting trends in consumer preferences in order to forecast next season sales and stock up or down accordingly.

Where to From Here?

Big data mining is akin to deep space research in that we are exploring fresh frontiers and discovering new worlds of information. The future is as broad as our imagination.?

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