The market driven journey III – the market driven supply chain

Intuitively being demand driven indicates having a pull-based manufacturing system customer orders trigger sourcing and production activities. However – it is rarely the case, that this is possible – due to the long lead time this would cause. This article will point out the requirements to a market driven supply chain.

From the previous article in series “The market driven journey” on market driven forecasting, two things stood out regarding the role of the supply chain

  • The supply chain should be responsive to true demand keeping a high customer service level.
  • Possible demand scenarios must be evaluated and consequences to the supply chain identified – and the time to do so is very short.

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Are your big data skills disruption-ready?

Disruptive innovation—the big changes in an industry or sector that occur when someone or something turns the whole business model on its head—have huge implications. Big data is likely to be one of those disruptors. So are your big data skills disruption-ready? These following aspects will guide you to verify your readiness.

Are your big data skills disruption-ready

Digital security expertise is the biggest issue for businesses right now. This is, however, expected to be overtaken by analytics and big data skills within the next three years, according to an analysis by the Economist Intelligence Unit. In total, 43% of executives in the US and Europe said that these skills would be the most important within three years, compared with the current level of 38%.

 

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Machine learning and the evolving intelligence landscape

Machine learning has been around for a while, with the earliest techniques developed in the 1950s. It is currently enjoying a particularly high profile, thanks to a whole range of possible applications from self-driving cars through to Go-playing computers. But what exactly is it?

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I’ve just finished diving into Josefin Rosen’s blog post, a description of how machine learning makes for a smarter life, and asked her to put ML in context. Welcome to join my discussion with Josefin.

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Six ways cognitive computing will impact banks

Cognitive computing evokes images of science fiction and endless possibilities that are in the distant future. However, a recent survey of banking executives suggests that 89% of those familiar with the concept thought that it would be very disruptive to the banking industry, and 79% thought it would be critical to the future of the industry. So what are the possible applications for cognitive computing in the banking sector?

Product and service development

Cognitive computing has the potential to manage large numbers of banking transactions in a much more sophisticated way, allowing banks to fulfil the promise of competing on analytics. It can use context- and evidence-based information to provide tailored products and services to customers. This will improve self-service for bank customers through enhanced automation of banking and investment products.

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Trends in Telecom: The wind of change is still blowing hard on the Telecom Operators

The traditional business of a Telecom Operator used to be an extremely profitable one with steady growth rates. But now it has been exposed to a wind of change for some time and looking at the graphics below of predictions for services (based on data from STL Partners analysis) taken from Dirk Rohweder blog, and the wind is not likely to stop blowing anytime soon.

telco-revenue-change

Different strategies are currently being explored by the operators to address this constant change in the market landscape. Depending on whether you decide to love and embrace that change, or view it as a threat you need to stop, this is handled in separate ways. Some strategies are more innovative and positively looking at new opportunities, some are mere packages of measures to survive and some are of course addressing a mix of both.

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Why Sports is the ultimate arena for Analytics

If you are involved in Analytics, you already know that there are some key areas to be aware of in order to achieve results. Over the last year and a half I’ve found that some of those things make Sports a really intriguing and exciting, not to say the ultimate arena for Analytics. At least if you believe that working against difficult conditions improves your skills, understanding and capability.

Sample size

The more data the better. It’s as simple as that.

Those of you who noticed that the Swedish National Hockey team “Tre Kronor” brought in a data scientist, Jon Blomqvist, to prepare for the World Cup you may recall that the analysis of all players going in to the tournament was based on 1230 games, 1,000,000 shifts and 400,000 events. That’s a reasonable amount in order to get an overall understanding but actually not very much. In reality sports data is really limited, and rather basic by nature. To work with this and still bring out relevant and statistically significant results is a challenge.

hockey-analytics-rink

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How IoT is propelling insurance to the next frontier

The auto insurance industry is facing an unprecedented number of challenges brought on by heavy price competition and increased claims costs. To counter this, the industry is now turning to new clever ways of using advanced analytics in order to be more competitive, and be able to better assess risk and pricing. One of the key technologies is internet of things (IoT), or more specifically telematics. With the power to understand in detail how insured items such as cars and boats are being used, a whole new arena is opening up for the technically savvy insurer.

 Insurance adjuster examining damage to car exterior

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3 reasons why International Fraud Week matters to Nordic Government authorities

International Fraud Week is here!

We at SAS have marked the International Fraud Week by going on a Nordic road show throughout this month discussing Risk Based Controls in Government. And it is really a pleasure to be involved with these seminars. A living debate between speakers and participants on various aspects of the subject has been the general picture.

Having addressed the issue specifically in the Nordic countries I cannot help noticing a couple of specific challenges that our customers seem to have in common in our region.

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Data to perform analytics

First and foremost the challenge of creating an analytical data layer to perform an efficient and automatized compliance and regulation environment. Typically, data is optimized to support production tasks as opposed to analytics. Data needs to be organized so that there can be performed time series analyses. Also the silos that are typically found in the organization needs to be broken down, so it is possible to get an enterprise view of how a citizen or a company – let’s call them a customer – behaves across the authority. It is compelling to have one common view on data entities, in order to risk assess the customer in question. If it is possible, subject to legal constraints, this even applies to sharing data between authorities. This represents one important challenge we have in our region – the data environment is often subject to change so the solution needs to be very dynamic and easy to adjust.

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The market driven journey II – demand driven forecasting

This is the second post of a series about the market driven journey to demand driven forecasting. You can read my first post Supply chain transparency: The market driven journey here. If you are interested to know more about demand driven forecasting and planning taking demand responsiveness to the next level, you can download a White Paper about taking demand responsiveness to the next level.

 

Being market driven boils down to three things:

  1. Knowing the true market demand and the demand drivers
  2. Having a supply chain that can respond to the true demand
  3. Having processes in place to align demand and supply on day-to-day AND long term basis.

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To become market driven, companies often follow the journey depicted below with development on both organizational and technical areas. I explored the early maturity levels in my previous post.

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Will excitement about IoT help telematics shine?

Telematics is a term originally coined to describe the coming together of telecommunication and informatics. In its widest sense, it is about the use of telecommunications technology to receive information and then affect other ‘things’ remotely. More often, however, it is used in a narrower sense. It represents the combination of telecoms and informatics in vehicles, a use that purists would call vehicle telematics.

Telematics and IoT

You may be thinking that this sounds like the Internet of Things (IoT). And yes, the two are closely related. Telematics has been around since the late 1970s, so you could see it as the grandfather of the IoT, with machine-to-machine (M2M) as the generation in between.  This may sound like splitting hairs, and perhaps it is, but the three are slightly different.

Telematics also has similarities with radio-frequency identification (RFID) technology. My colleague Brad Hathaway noted recently that RFID technology is eminently compatible with the IoT, and there is no reason at all why telematics and the IoT should not co-exist for some time either. One does not need to replace the other. Instead, the two may be more useful together. And just like RFID, perhaps the IoT will provide new ways for telematics to shine.

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