Sunday, 2 July 2017
Technology is changing rapidly for wireless, signiﬁcantly changing the power requirements of the 6000+ base stations within our Kenyan Telcos infrastructure. These improvements increase the viability of using eco-friendly power and our Telcos have already seen this trend of IoT and are engaged in efforts to stop the trend of rising telecom energy demands. With so many options for reducing their eco-footprint, and considering the challenge of implementing changes while remaining proﬁtable, planning a sensible, ecologically friendly path forward is often a formidable task. It is for this reason that I chose to take an opportunity to write to the power departments in our communication institutions which I have gracefully worked with for close to 3 years indirectly as an engineer assigned to do electrical and computational works for them.
The4G+ as an already laid out plan by one of the major Telcos within our country serves as an example which is a really good move that comes with growth of bandwidth demand which can easily cause Safaricom network energy consumption to rise in step with the growth. The resulting increase in electricity costs leads to reduced margins at a time when competition is also driving prices down-the relauch of Telkom Kenya a few days ago marks a threat in the same regard. Having worked closely with a number of power departments amongst the Telcos we have, I have seen and learnt two options used when planning to reduce power consumption. First, there are new network architectures that are inherently more energy-efﬁcient and which can simultaneously provide the ﬂexibility to support continued increases in demand. Second, choices in network equipment, options, and support equipment for new or existing infrastructure have also had a tremendous impact on the amount of power consumed. Both options are quite viable and should be part of any power reduction plan even as we leap into the digital disruptive era in the coming years.
Am grateful to have worked indirectly with the engineers at both power and optimization departments and have been able to tap a lot of skills in my area of expertise and personal growth as well. I look forward for an opportunity to present my ideas (a combo mixture of Artificial intelligence, big data analytics and IoT) as well as deliberate further on how best can power can be planned and supported to attain the ultimate goal in energy efficiency. Am also grateful to Parastatals that deal directly and indirectly with power and energy distribution for the nifty work they are putting across to solve the trilemma of cost, reliability and quality of power being used in our republic.
Below is a recap article of the latest bell lab power technical journal 2017 edition that I saw it prudent to share as well with other engineers and stakeholders in power & energy sector alike whom I revere and hold atmost respect for the training and lessons I have gained from them.
Methodology for Planning Energy-Reducing
The methodology for planning network changes to reduce energy usage consists of three cascading steps:
• Energy consumption hierarchy. Identiﬁcation of the network elements that consume power and their location in the network.
• Energy-saving chain. Identiﬁcation of network element dependencies upon each other’s power dissipation (e.g., larger air conditioning units having higher energy consumption are necessary if inefficient power rectifiers are installed because of the energy they waste through heat radiation). This allows network operators to target the most effective points for energy reduction by applying energy-saving initiatives.
• Energy-saving initiatives or options. Determination of speciﬁc choices or actions that can be taken to reduce energy consumption for one or more net- work elements (e.g., replacing low-efﬁciency rectifiers with high-efficiency rectifiers, which requires capital and installation expense, but these expenses may be offset in 12 to 15 months based on today’s high energy costs). Sets of initiatives are often deployed simultaneously due to typically lower installation costs as compared to deploying the initiatives one at a time
As we continue improving our communication systems across the country and beyond, lets research and read widely for the upcoming 4th industrial revolution which in my own view will be sparked and born in China and fully utilized here in Africa and hopefully in our dear motherland Kenya.
“A powered nation is a growing nation”~Samwel Kariuki
Saturday, 1 April 2017
Monday, 13 February 2017
There has been recent progress in the analysis of call-center data. Call-by-call data from a small number of sites have been obtained and analyzed, and these limited results have proven to be fascinating. In some cases, such as the characterization of the arrival process and of the delay of arriving calls to the system, conventional assumptions and models of system performance have been upheld. In others, such as the characterization of the service-time distribution and of customer patience, the data have revealed fundamental, new views of the nature of the service process. Of course, these limited studies are only the beginning, and the effort to collect and analyze call-center data can and should be expanded in every dimension in Kenya and Africa at large.
Perhaps the most pressing practical need is for improvements in the forecasting of arrival rates. For highly utilized call centers, more accurate, distributional forecasts are essential. While there exists some research that develops methods for estimating and predicting arrival rates, I strongly believe there is surely room for additional improvement to be made both here at home and the entire continent. However, further development of models for estimation and prediction will depend, in part, on access to richer data sets. Some of us believe that much of the randomness of Poisson arrival rates may be explained by covariates that are not captured in currently available data.
Procedures for predicting waiting-times are also worth pursuing. Field-based studies that characterize the performance of different statistics and methods would also be of value. More broadly, there is need for the development of a wider range of descriptive models. While a characterization of arrival rates, abandonment from queue, and service times are essential for the management of call centers, they constitute only a part of the complete picture of what goes on. For example, there exist (self ) service times and abandonment (commonly called “opt-out”) behavior that arise from customer use of IVRs. Neither of these phenomena is likely to be the same as its CSR analogue. Similarly, sojourn times and abandonment from web-based services have not been examined in multi-media centers.
Parallel, descriptive studies are also needed to validate or refute the robustness of initial findings. For example, lognormal service times have been reported in two call centers, both of which are part of retail financial services companies. Perhaps the service-time distributions of catalogue retailers or help-desk operations have different characteristics.
Similarly, one would like to test some finding that the waiting-time messages customers hear while tele-queueing promote, rather than discourage, abandonment.
It would also be interesting to put work on abandonment (Palm, Roberts, Kort, Mandelbaum with Sakov and Zeltyn) in perspective. These studies provide empirical and exploratory models for (im)patience on the phone in Sweden in the 40’s, France in the late 70’s, the U.S. in the early 80’s, Israel in the late 90’s and now Africa(Kenya in particular under this research) in the early millennium. A systematic comparison of patience across countries, for current phone services, should be a worthy, interesting undertaking.
There is the opportunity to further develop and extend the scope of explanatory models. Indeed, given the high levels of system utilization in the QED Quality and Efficiency Driven (operational) regime, a small percentage error in the forecast of the offered load can lead to significant, unanticipated changes in system performance. In particular, the state of the art in forecasting call volumes is still rudimentary. Similarly, the fact that service times are lognormally distributed enables the use of standard parametric techniques to understand the effect of covariates on the (normally distributed) natural log of service times.
In well-run QED call centers, only a small fraction of the customers abandon (around 1-3%), hence about 97% of the (millions of ) observations are censored. Based on such figures, one can hardly expect any reasonable estimate of the whole patience distribution, non-parametrically at least. Fortunately, however, theoretical analysis suggests that only the behavior of impatience near the origin is of relevance, and this is observable and analyzable.
Indeed, call-center data are challenging the state-of-the-art of statistics, and new statistical techniques seem to be needed to support their analysis. Two examples are the accurate non- parametric estimation of hazard rates, with corresponding confidence intervals, and the survival analysis of tens of thousands, or even millions, of observations, possibly correlated and highly censored.
Last but certainly not least, a broader goal should be, in fact, the analysis of integrated operational, marketing, human resources, and psychological data. That is, the analysis of these integrated data is essential if one is to understand and quantify the role of operational service quality as a driver for business success.
A prerequisite for understanding the financial effects of operational decisions is the ability to analyze an integrated data set that includes operational (ACD) automatic call distributor and marketing / business (customer information systems) data. With this information, one can attempt to tease out the longer-term, financial effects of operational policies.
My experience has been that both types of data are very difficult to access, however. One reason for this is technical. Only recently have the manufacturers of telephone equipment given customers something of an “off the shelf ” ability to capture, store, and retrieve detailed, call-by- call data. Similarly, the integration of these operational data with the business data captured in customer information systems is only now becoming widely available. Another reason stems from confidentiality concerns; most of our Kenyan companies are rightly wary of releasing customer information. Once managers recognize the great untapped value of these data, i believe they will employ mechanisms for preserving confidentiality in order to reap the benefit.
Ultimately, i envision a data-repository that is continuously fed by many call centers of varying types. The collected data would be continuously and automatically analyzed, from both operations and marketing perspectives. Then the data would be both archived and fed back to the originating call centers, who would use it (through visualization tools) to support ongoing operations, as well as tactical and strategic goals.
Little imagination is required for appreciating the value of such a data-base. As a start, its developer could become a benchmark that sets industry standards, as far as customer-service quality and call-center efficiency are concerned. As already mentioned, such a data-base would enable the identification of success-drivers of call-center business transaction.
Researched & Compiled: Samwel Kariuki
Date: 12th Feb 2017