Using Pull Systems to Optimise Work Flows in Call Centres

When call centres emerged towards the end of the 20th century, they deserved their name ?the sweatshops of the nineties?. A new brand of low-paid workers crammed into tiny cubicles to interact with consumers who were still trying to understand the system. Supervisors followed ?scientific management? principles aimed at maximising call-agent activity. When there was sudden surge in incoming calls, systems and customer care fell over.

The flow is nowadays in the opposite direction. Systems borrowed from manufacturing like Kanban, Pull, and Levelling are in place enabling a more customer-oriented approach. In this short article, our focus is on Pull Systems. We discuss what are they, and how they can make modern call centres even better for both sets of stakeholders.

Pull Systems from a Manufacturing Perspective

Manufacturing has traditionally been push-based. Sums are done, demand predicted, raw materials ordered and the machines turned on. Manufacturers send out representatives to obtain orders and push out stock. If the sums turn out wrong inventories rise, and stock holding costs increase. The consumer is on the receiving end again and the accountant is irritable all day long.

Just-in-time thinking has evolved a pull-based approach to manufacturing. This limits inventories to anticipated demand in the time it takes to manufacture more, plus a cushion as a trigger. When the cushion is gone, demand-pull spurs the factory into action. This approach brings us closer to only making what we can sell. The consumer benefits from a lower price and the accountant smiles again.

Are Pull Systems Possible in Dual Call Centres

There are many comments in the public domain regarding the practicality of using lean pull systems to regulate call centre workflow. Critics point to the practical impossibility of limiting the number of incoming callers. They believe a call centre must answer all inbound calls within a target period, or lose its clients to the competition.

In this world-view customers are often the losers. At peak times, operators can seem keen to shrug them off with canned answers. When things are quiet, they languidly explain things to keep their occupancy levels high. But this is not the end of the discussion, because modern call centres do more than just take inbound calls.

Using the Pull System Approach in Dual Call Centres

Most call centre support-desks originally focused are handling technical queries on behalf of a number of clients. When these clients? customers called in, their staff used operator?s guides to help them answer specific queries. Financial models?determined staffing levels and the number of ?man-hours? available daily. Using a manufacturing analogy, they used a push-approach to decide the amount of effort they were going to put out, and that is where they planted their standard.

Since these early 1990 days, advanced telephony on the internet has empowered call centres to provide additional remote services in any country with these networks. They have added sales and marketing to their business models, and increased their revenue through commissions. They have control over activity levels in this part of their business. They have the power to decide how many calls they are going to make, and within reason when they are going to make them.

This dichotomy of being passive regarding incoming traffic on the one hand, and having active control over outgoing calls on the other, opens up the possibility of a partly pull-based lean approach to call centre operation. In this model, a switching mechanism moves dual trained operators between call centre duties and marketing activities, as required by the volume of call centre traffic, thus making a pull system viable in dual call centres.

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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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Finding the Best Structure for Your Enterprise Development Team

An enterprise development team is a small group of dedicated specialists. They may focus on a new business project such as an IoT solution. Members of microteams cooperate with ideas while functioning semi-independently. These self-managing specialists are scarce in the job market. Thus, they are a relatively expensive resource and we must optimise their role.

Organisation?Size and Enterprise Development Team Structure

Organisation structure depends on the size of the business and the industry in which it functions. An enterprise development team for a micro business may be a few freelancers burning candles at both ends. While a large corporate may have a herd of full-timers with their own building. Most IoT solutions are born out of the efforts of microteams.

In this regard, Bill Gates and Mark Zuckerberg blazed the trail with Microsoft and Facebook. They were both college students at the time, and both abandoned their business studies to follow their dreams. There is a strong case for liberating developers from top-down structures, and keeping management and initiative at arm?s length.

The Case for Separating Microteams from the?Organisation

Microsoft Corporation went on to become a massive corporate, with 114,000 employees, and its founder Bill Gates arguably one of the richest people in the world. Yet even it admits there are limitations to size. In Chapter 2 of its Visual Studio 6.0 program it says,

‘today’s component-based enterprise applications are different from traditional business applications in many ways. To build them successfully, you need not only new programming tools and architectures, but also new development and project management strategies.?

Microsoft goes on to confirm that traditional, top-down structures are inappropriate for component-based systems such as IoT solutions. We have moved on from ?monolithic, self-contained, standalone systems,? it says, ?where these worked relatively well.?

Microsoft’s model for enterprise development teams envisages individual members dedicated to one or more specific roles as follows:

  • Product Manager ? owns the vision statement and communicates progress
  • Program Manager ? owns the application specification and coordinates
  • Developer ? delivers a functional, fully-complying solution to specification
  • Quality Assurer ? verifies that the design complies with the specification
  • User Educator ? develops and publishes online and printed documentation
  • Logistics Planner ? ensures smooth rollout and deployment of the solution

Three Broad Structures for Microteams working on IoT Solutions

The organisation structure of an enterprise development team should also mirror the size of the business, and the industry in which it functions. While a large one may manage small microteams of employee specialists successfully, it will have to ring-fence them to preserve them from bureaucratic influence. A medium-size organisation may call in a ?big six? consultancy on a project basis. However, an independently sourced micro-team is the solution for a small business with say up to 100 employees.

The Case for Freelancing Individuals versus Functional Microteams

While it may be doable to source a virtual enterprise development team on a contracting portal, a fair amount of management input may be necessary before they weld into a well-oiled team. Remember, members of a micro-team must cooperate with ideas while functioning semi-independently. The spirit of cooperation takes time to incubate, and then grow.

This is the argument, briefly, for outsourcing your IoT project, and bringing in a professional, fully integrated micro-team to do the job quickly, and effectively. We can lay on whatever combination you require of project managers, program managers, developers, quality assurers, user educators, and logistic planners. We will manage the micro-team, the process, and the success of the project on your behalf while you get on running your business, which is what you do best.

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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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