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.

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Spreadsheet Reporting – No Room in Your Company in an Age of Business Intelligence

It doesn’t take a genius to understand why spreadsheet reporting still pervades the enterprise despite the rise of a complex but highly effective IT solution known to big shot CIOs as Business Intelligence or BI.

If you’re still in the dark as to what BI is, don’t worry because we?ll enlighten you shortly.

Business decisions from disparate data sources

In the meantime, let’s talk about how you make business decisions. If you’re a top executive, then you make decisions based largely on reports submitted to you by your managers, department heads, and so on. They in turn obtain information from different sources, like the company ERP and CRM as well as other external sources (e.g. market surveys).

Now, before their reports ever reach your desk, a lot of data is extracted, shared, filtered, analysed, consolidated, and summarised so that they become actionable information. In all these activities, one software tool gets to take part in most of the action – the spreadsheet.

The problem with spreadsheet reporting

The problem with spreadsheets is that they have very poor built-in controls. Thus, they are susceptible to human errors and are vulnerable to fraud. What’s more, collecting data and manually consolidating them into spreadsheets can be very laborious and time consuming.

If you don’t get accurate, reliable information, your judgement will be fuzzy and your business decisions compromised. In addition, if you don’t receive the information you need on time, your business will constantly be at risk of breaching critical thresholds, which may even force it to spin out of control.

Business Intelligence – actionable information on time

This is mainly the reason why large companies implement Business Intelligence systems. BI systems are equipped with built-in features like reports, dashboards, and alerts.

Reports consolidate data and present them in a consistent format composed of intuitive text, graphs, and charts. The main purpose of having a consistent format is so that you will know what kind of information to expect and how the information is arranged. That way, you don’t waste time searching or making heads or tails out of the data in front of you.

Dashboards, on the other hand, present information through visual representations composed of graphs and gauges that are aimed at tracking your business metrics and goals. The main function of dashboards is to feed you with actionable information at a glance.

Finally, alerts keep you informed when certain conditions are met or critical thresholds are breached. Because their main purpose is to prompt you at the soonest possible time wherever you are, a typical alert can come in the form of an SMS message or an email.

As you can see, all three features are designed to get you making well-informed decisions as quickly as possible.

The problem with Business Intelligence and the alternative solution

The usual problem with full BI systems is that they can be very costly. Hence, if your organisation does end up implementing one, chances are, not everyone under you will be able to access it. As a result, some departments will be forced to go back to using spreadsheets.

If your company cannot afford a full BI system, then that probably means you don’t need one. What you need is a more affordable alternative. There are actually Software as a Service (SaaS) Business Intelligence solutions that may not be as comprehensive as a full BI system, but which may suffice for small and mid-sized businesses.

The disadvantages of spreadsheets are more damaging than you could have ever expected. Be free of it now.

 

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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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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Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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