Spreadsheet Woes – Limited Features For Easy Adoption of a Control Framework

Like it or not, regulations are here to stay and for a company to comply with them, its IT and financial systems will have to be equipped with a suitable control framework. One common stumbling block to such an implementation is a company?s over-reliance on spreadsheets.

Why is it so difficult to adopt controls for a system that’s reliant on spreadsheets? To understand this, let’s pinpoint some of the strongest, most powerful attributes of these User Developed Applications (UDA).

By nature, spreadsheets are the epitome of simplicity: easy to develop, easily accessible and easily altered. All computers in your workplace will most likely have them and everyone in your organization may be sharing them, making their own versions, and storing them in personal folders.

Sad to say though, these strengths are also control weaknesses and constitute the very reasons why spreadsheets require effective risk management.

Easy to develop. Being easy to develop, most spreadsheet systems are created by non-IT users who have limited knowledge on best control practices. Being constantly under time pressure, these ?developers? may also relegate documentation, security, and data verification to the back burner in favour of coming up with a timely report.

Easy to access. Information in a spreadsheet can be opened by practically anyone within the organization?s network. Who accessed what? And when? If anything goes wrong, it would be difficult to identify the culprit, and the failure to pinpoint responsibility for erroneous data could lead to bigger, more costly mistakes.

Easy to alter. Lastly, if the information is easy to access, then it can also be easily altered, consequently making reports more prone to both accidental errors and fraudulent modifications.

The rise of multimillion dollar scandals due to accidental and intentional spreadsheet errors have prompted regulatory bodies to publish guidelines for mitigating spreadsheet-associated risks. These controls include:

  • Change control
  • Version control
  • Access control
  • Input
  • Security and data integrity
  • Documentation
  • Development life cycle
  • Backup and archiving
  • Logic inspection/Testing
  • Segregation of duties/roles, and procedures
  • Analytics

In theory, these controls should be able to bring down risks considerably. However, because of the inherent nature of spreadsheets, such controls are rarely implemented effectively in the real world.

Take for example Security and Data Integrity. One of the most common causes of spreadsheet error is due to ?hardwiring?. This happens when values are inadvertently entered into a formula cell, naturally changing the logic of the spreadsheet.

As a way of control, cell locking can be applied on the formula cells to prevent users without the proper authority from making any changes. However, when reporting deadlines approach drawing spreadsheets to the forefront of data processing, more people are given access rights to the locked cells. Ironically, it is during these crunch times, when errors are most likely to happen.

Because the built-in features of a spreadsheet support none of the controls mentioned above, some companies are tempted to purchase control-enabling programs for spreadsheets just to continue using them for financial reporting. But although these programs can integrate the required controls, you?d still be interacting with the same complex and outdated interface: the spreadsheets.

Thus, these band-aid solutions may not suffice because the root cause of these problems are the spreadsheets themselves.

Learn more about our server application solutions and discover a better way to implement controls.

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Spreadsheet Reporting – No Room in your company in an age of Business Intelligence


Still looking for a Way to Consolidate Excel Spreadsheets?


Disadvantages of Spreadsheets


Spreadsheet woes – ill equipped for an Agile Business Environment


Spreadsheet Fraud


Spreadsheet Woes – Limited features for easy adoption of a control framework


Spreadsheet woes – Burden in SOX Compliance and other Regulations


Spreadsheet Risk Issues


Server Application Solutions – Don’t let Spreadsheets hold your Business back


Why Spreadsheets can send the pillars of Solvency II crashing down

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FUJIFILM Cracks the Energy Code

FUJIFILM was in trouble at its Dayton, Tennessee plant in 2008 where it produced a variety of speciality chemicals for industrial use. Compressed-air breakdowns were having knock-on effects. The company decided it was time to measure what was happening and solve the problem. It hoped to improve reliability, cut down maintenance, and eliminate relying on nitrogen for back-up (unless the materials were flammable).

The company tentatively identified three root causes. These were (a) insufficient system knowledge within maintenance, (b) weak spare part supply chain, and (c) generic imbalances including overstated demand and underutilised supply. The maintenance manager asked the U.S. Department of Energy to assist with a comprehensive audit of the compressed air system.

The team began on the demand side by attaching flow meters to each of several compressors for five days. They noticed that – while the equipment was set to deliver 120 psi actual delivery was 75% of this or less. They found that demand was cyclical depending on the production phase. Most importantly, they determined that only one compressor would be necessary once they eliminated the leaks in the system and upgraded short-term storage capacity.

The project team formulated a three-stage plan. Their first step would be to increase storage capacity to accommodate peak demand; the second would be to fix the leaks, and the third to source a larger compressor and associated gear from a sister plant the parent company was phasing out. Viewed overall, this provided four specific goals.

  • Improve reliability with greater redundancy
  • Bring down system maintenance costs
  • Cut down plant energy consumption
  • Eliminate nitrogen as a fall-back resource

They reconfigured the equipment in terms of lowest practical maintenance cost, and moved the redundant compressors to stations where they could easily couple as back-ups. Then they implemented an online leak detection and repair program. Finally, they set the replacement compressor to 98 psi, after they determined this delivered the optimum balance between productivity and operating cost.

Since 2008, FUJIFILM has saved 1.2 million kilowatt hours of energy while virtually eliminating compressor system breakdowns. The single compressor is operating at relatively low pressure with attendant benefits to other equipment. It is worth noting that the key to the door was measuring compressed air flow at various points in the system.

ecoVaro specialises in analysing data like this on any energy type.?

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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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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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