Spreadsheet Woes – Burden in SOX Compliance and Other Regulations

End User Computing (EUC) or end User Developed Application (UDA) systems like spreadsheets used to be ideal ad-hoc solutions for data processing and financial reporting. But those days are long gone.

Today, due to regulations like the:

  • Sarbanes-Oxley (SOX) Act,
  • Dodd-Frank Act,
  • IFRS (International Financial Reporting Standards),
  • E.U. Data Protection Directive,
  • Basel II,
  • NAIC Model Audit Rules,
  • FAS 157,
  • yes, there?s more ? and counting

a company can be bogged down when it tries to comply with such regulations while maintaining spreadsheet-reliant financial and information systems.

In an age where regulatory compliance have become part of the norm, companies need to enforce more stringent control measures like version control, access control, testing, reconciliation, and many others, in order to pass audits and to ensure that their spreadsheets are giving them only accurate and reliable information.

Now, the problem is, these control measures aren’t exactly tailor-made for a spreadsheet environment. While yes, it is possible to set up a spreadsheet and EUC control environment that utilises best practices, this is a potentially expensive, laborious, and time-consuming exercise, and even then, the system will still not be as foolproof or efficient as the regulations call for.

Testing and reconciliation alone can cost a significant amount of time and money to be effective:

  1. It requires multiple testers who need to test spreadsheets down to the cell level.
  2. Testers will have to deal with terribly disorganized and complicated spreadsheet systems that typically involve single cells being fed information by other cells in other sheets, which in turn may be found in other workbooks, or in another folder.
  3. Each month, an organisation may have new spreadsheets with new links, new macros, new formulas, new locations, and hence new objects to test.
  4. Spreadsheets rarely come with any kind of supporting documentation and version control, further hampering the verification process.
  5. Because Windows won’t allow you to open two Excel files with the same name simultaneously and because a succession of monthly-revised spreadsheets separated by mere folders but still bearing the same name is common in spreadsheet systems, it would be difficult to compare one spreadsheet with any of its older versions.

But testing and reconciliation are just two of the many activities that make regulatory compliance terribly tedious for a spreadsheet-reliant organisation. Therefore, the sheer intricacy of spreadsheet systems make examining and maintaining them next to impossible.

On the other hand, you can’t afford not to take these regulations seriously. Non-compliance with regulatory mandates can have dire consequences, not the least of which is the loss of investor confidence. And when investors start to doubt the management’s capability, customers will start to walk away too. Now that is a loss your competitors will only be too happy to gain.

Learn more about our server application solutions and discover a better way to comply with regulations.

More Spreadsheet Blogs


Spreadsheet Risks in Banks


Top 10 Disadvantages of Spreadsheets


Disadvantages of Spreadsheets – obstacles to compliance in the Healthcare Industry


How Internal Auditors can win the War against Spreadsheet Fraud


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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Failure Mode and Effects Analysis

 

Any business in the manufacturing industry would know that anything can happen in the development stages of the product. And while you can certainly learn from each of these failures and improve the process the next time around, doing so would entail a lot of time and money.
A widely-used procedure in operations management utilised to identify and analyse potential reliability problems while still in the early stages of production is the Failure Mode and Effects Analysis (FMEA).

FMEAs help us focus on and understand the impact of possible process or product risks.

The FMEA method for quality is based largely on the traditional practice of achieving product reliability through comprehensive testing and using techniques such as probabilistic reliability modelling. To give us a better understanding of the process, let’s break it down to its two basic components ? the failure mode and the effects analysis.

Failure mode is defined as the means by which something may fail. It essentially answers the question “What could go wrong?” Failure modes are the potential flaws in a process or product that could have an impact on the end user – the customer.

Effects analysis, on the other hand, is the process by which the consequences of these failures are studied.

With the two aspects taken together, the FMEA can help:

  • Discover the possible risks that can come with a product or process;
  • Plan out courses of action to counter these risks, particularly, those with the highest potential impact; and
  • Monitor the action plan results, with emphasis on how risk was reduced.

Find out more about our Quality Assurance services in the following pages:

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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What Heijunka is & How it Smooths Call Centre Production

The Japanese word Heijunka, pronounced hi-JUNE-kuh means ?levelling? in the sense of balancing workflows. It helps lean organizations shift priorities in the face of fluctuating customer demand. The goal is to have the entire operation working at the same pace throughout, by continuously adjusting the balance between predictability, flexibility, and stability to level out demand.

Henry Ford turned the American motor manufacturing industry upside down by mass-producing his iconic black motor cars on two separate production lines. In this photograph, body shells manufactured upstairs come down a ramp and drop onto a procession of cars almost ready to roll in 1913.

Smoothing Production in the Call Centre Industry

Call Centres work best in small teams, each with a supervisor to take over complex conversations. In the past, these tended to operate in silos with each group in semi-isolation representing a different set of clients. Calls came through to operators the instant the previous ones concluded. By the law of averages, inevitably one had more workload than the rest at a particular point in time as per this example.

Modern telecoms technology makes it possible to switch incoming lines to different call centre teams, provided these are multi-skilled. A central operator controls this manually by observing imbalanced workflows on a visual system called a Heijunka Box. The following example comes from a different industry, and highlights how eight teams share uneven demand for six products.

This departure from building handmade automobiles allowed Henry to move his workforce around to eliminate bottlenecks. For example, if rolls of seat leather arrived late he could send extra hands upstairs to speed up the work there, while simultaneously slowing chassis production. Ford had the further advantage of a virtual monopoly in the affordable car market. He made his cars at the rate that suited him best, with waiting lists extending for months.

A Modern, More Flexible Approach

Forces of open competition and the Six Sigma drive for as-close-to-zero defects dictates a more flexible approach, as embodied in this image published by the Six Sigma organisation. This represents an ideal state. In reality, one force usually has greater influence, for example decreasing stability enforces a more flexible approach.

Years ago, Japanese car manufacturer Toyota moved away from batching in favour of a more customer-centric approach, whereby buyers could customise orders from options held in stock for different variations of the same basic model. The most effective approach lies somewhere between Henry Ford?s inflexibility and Toyota?s openness, subject to the circumstances at the moment.

A Worked Factory Example

The following diagram suggests a practical Heijunka application in a factory producing three colours of identical hats. There are two machines for each option, one or both of which may be running. In the event of a large order for say blue hats, the company has the option of shifting some blue raw material to the red and green lines so to have the entire operation working at a similar rate.

Predictability, Flexibility, and Stability at Call Centre Service

The rate of incoming calls is a moving average characterised by spikes in demand. Since the caller has no knowledge whether high activity advisories are genuine, it is important to service them as quickly as possible. Lean process engineering provides technology to facilitate flexibility. Depending on individual circumstances, each call centre may have its own definition of what constitutes an acceptably stable situation.

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