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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Are Master Data Management and Hadoop a Good Match?

Master Data is the critical electronic information about the company we cannot afford to lose. Accordingly, we should sanitise it, look after it, and store it safely in several separate places that are independent of each other. The advent of Big Data introduced the current era of huge repositories ?in the clouds?. They are not, of course but at least they are remote. This short article includes a discussion about Hadoop, and whether this is a good platform to back up your Master Data.

About Hadoop

Hadoop is an open-source Apache software framework built on the assumption that hardware failure is so common that backups are unavoidable. It comprises a storage area and a management part that distributes the data to smaller nodes where it processes faster and more efficiently. Prominent users include Yahoo! and Facebook. In fact more than half Fortune 50 companies were using Hadoop in 2013.

Hadoop – initially launched in December 2011 ? has survived its baptism of fire and became a respected, reliable option. But is this something the average business owner can tackle on their own? Bear in mind that open source software generally comes with little implementation support from the vendor.

The Hadoop Strong Suite

Free to download, use and contribute to

Everything you need ?in the box? to get started

Distributed across multiple fire-walled computers

Fast processing of data held in efficient cluster nodes

Massive scaleable storage you are unlikely to run out of

Practical Constraints

There is more to Hadoop than writing to WordPress. The most straightforward solutions are uploading using Java commands, obtaining an interface mechanism, or using third party vendor connectors such as ACCESS or SAS. The system does not replace the need for IT support, although it is cheap and exceptionally powerful.

The Not-Free Safer Option

Smaller companies without in-depth in-house support are wise to engage with a technical intermediary. There are companies providing commercial implementations followed by support. Microsoft, Amazon and Google among others all have commercial versions in their catalogues, and support teams at the end of the line.

A Definitive List of the Business Benefits of Cloud Computing – Part 2

Improves cash flow

The capital investment you put into an on-premise IT infrastructure is normally based on a long-range forecast of what your highest computing demands will be. But what if, as they often do, the estimates turn out to be too high? Then you’ll have to bear with the huge depreciation cost or monthly amortisation of a grossly underutilised asset for the next couple of years. (more…)

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:

Continuous improvement is essential for success

Business processes can be measured and improved

Top down commitment is fundamental to sustained improvement

Claims of progress must be quantifiable and yield financial benefits

Management must lead with enthusiasm and passion

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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