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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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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Uncover hidden opportunities with energy data analytics

What springs to mind when you hear the words energy data analytics? To me, I feel like energy data analytics is not my thing. Energy data analytics, however, is of great importance to any organisation or business that wants to run more efficiently, reduce costs, and increase productivity. Energy efficiency is one of the best ways to accomplish these goals.

Energy efficiency is not about investment in expensive equipment and internal reorganization. Enormous energy saving opportunities is hidden in already existing energy data. Given that nowadays, energy data can be recorded from almost any device, a lot of data is captured regularly and therefore a lot of data is readily available.

Organisations can use this data to convert their buildings’ operations from being a cost centre to a revenue centre through reduction of energy-related spending which has a significant impact on the profitability of many businesses. All this is possible through analysis and interpretation of data to predict future events with greater accuracy. Energy data analytics therefore is about using very detailed data for further analysis, and is as a consequence, a crucial aspect of any data-driven energy management plan.

The application of Data and IT could drive significant cost savings in company-owned buildings and vehicle fleets. Virtual energy audits can be performed by combining energy meter data with other basic data about a building e.g. location, to analyse and identify potential energy savings opportunities. Investment in energy dashboards can further enable companies to have an ongoing look at where energy is being consumed in their buildings, and thus predict ways to reduce usage, not to mention that energy data analytics unlock savings opportunities and help companies to understand their everyday practices and operating requirements in a much more comprehensive manner.

Using energy data analytics can enable an organisation to: determine discrepancies between baseline and actual energy data; benchmark and compare previous performance with actual energy usage. Energy data analytics also help businesses and organisations determine whether or not their Building Management System (BMS) is operating efficiently and hitting the targeted energy usage goals. They can then use this data to investigate areas for improvement or energy efficient upgrades. When energy data analytics are closely monitored, companies tend to operate more efficiently and with better control over relevant BMS data.

eCommerce

 

We bet you’ve often read how getting rich through the Internet can be fast and easy. Time for your 5-second reality check: It’s going to entail lots of hard work, dedication, a great deal of information and the ability to use that information to your advantage. Sounds familiar?

Well, it should be. After all, it’s still business. However, while the basic ingredients to achieving success in business are still the basic prerequisites in eCommerce, there are also a lot of technical aspects that have to be factored in. This is where you’ll need us.

Well, actually, we’re going to help you out on those basic ingredients too. That’s because our dedicated specialists will perform most of the hard work until you gain enough know-how to run things on your own.

If you’re starting from scratch, we’ll help you build on your idea and transform it into an actual web-based business.

Then once you’ve got your site online, we’ll redirect traffic to it, attract the right visitors, convert those visitors into buyers and keep them satisfied so that they’ll come back and even spread the word.

Some of our related services include:

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