“Watch the product life cycle; but more important, watch the market life cycle” – Philip Kotler
The product life cycle describes the period over which an item is developed, brought to market and eventually removed from the market.
This paper describes a simple method to estimate Life Cycle stages – Growth, Maturity, and Decline (as seen in the traditional definitions) of products that have historical data of at least one complete life cycle.
Here, two different calculations have been done which helps the business to identify the number of weeks after which a product moves to a different stage and apply the PLC for improving demand forecasting.
A log-growth model is fit using Cumulative Sell through and Product Age which helps to identify the various stages of the product. A Log-Linear model is fit to determine the rate of change of product sales due to a shift in its stage, cet. par.
The life span of a product and how fast it goes through the entire cycle depends on market demand and how marketing instruments are used and vary for different products. Products of fashion, by definition, have a shorter life cycle, and they thus have a much shorter time in which to reap their reward.
Across industries, an increasing number of organizations are moving to data-driven decision making. Some of these decisions are contingent on historical data, and trends, whereas some are made on the fly, usually the more critical ones, based on the point in time data. Some of these judgments must be made with the ever-metamorphosing nature of data, whereas some are repetitive, based on data, that does not change all that often.
Our focus for this article are those decisions that are made on a recurring basis, based on processes that can be repetitive in nature. Specifically, processes that can be automated, and re-run with minimal manual intervention. Typically, data-driven automated decision making or insight generating engines, are referred to as ‘models’, and they can be statistical or heuristic in nature. We will talk about the differences in later sections.
Internet of Things (IoT) is a network of connected physical objects embedded with sensors. IoT allows these devices to communicate, analyze and share data about the physical world around us via networks and cloud-based software platforms.
In the current scenario, IoT is one of the most important phenomena revolutionizing the technological and business spheres. Several industries such as Agriculture, Healthcare, Retail, Transportation, Energy, and manufacturing are leveraging IoT to solve long-standing industry-wide challenges and thus transforming the way they function. For example, in Manufacturing, sensors placed in the various equipment collecting data about their performance are enabling pre-emptive maintenance and providing insights to improve overall efficiency. In Retail, “things” such as RFID inventory tracking chips, in-store infrared foot-traffic counters, digital signage, a kiosk, or even a customer’s mobile device is enabling retailers to provide location-specific customer engagement, in-store energy optimization, real-time inventory replenishment, etc.
The Insurance industry, on the other hand, has been rather sluggish by virtue of its size and inherent traditional nature. They cannot, however, afford to continue a wait-and-watch attitude towards IoT. Insurance is, interestingly, one of the industries that are bound to be most impacted by various technological leaps that are being made. IoT, blockchain, Big Data are all expected to push Insurance to evolve into a different beast altogether, including a shift from restitution to prevention.
Organizations have realized quantum jumps in business outcomes through the institutionalization of data-driven decision making. Predictive Analytics, powered by the robustness of statistical techniques, is one of the key tools leveraged by data scientists to gain insight into probabilistic future trends. Various mathematical models form the DNA of Predictive Analytics.
A typical model development process includes identifying factors/drivers, data hunting, cleaning and transformation, development, validation – business & statistical and finally productionalization. In the production phase, as actual data is included in the model environment, true accuracy of the model is measured. Quite often there are gaps(error) between predicted and actual numbers. Business teams have their own heuristic definitions and benchmark for this gap and any deviation leads to forage for additional features/variables, data sources and finally resulting in rebuilding the model.
Analytics have been growing at a rapid pace across the world. The well-established companies have realized the importance of analytics in their business where crucial decisions are taken that drives their revenue. But why do just the well-established corporates need to leverage this statistical and computational modus operandi when it can be implemented in a much-needed arena also?
The idea is to get to use the analytics for non-profit social organizations and provide a breakthrough. These are the organizations which strive to look for the upliftment of society by identifying the social responsibilities. The organizations cover a wide variety of aspects that helps to promote education, health, food, shelter etc
Affine recently completed 6 years, I have been a part of it for about 3 of those years. As an analytics firm, the most common business problem that we have come across is that of forecasting consumer demand. This is particularly true for Retail and CPG clients.
Over the last few years have dealt with simple forecasting problems for which we can use very simple time-series forecasting techniques like ARIMA and ARIMAX or even linear regression these are forecasts which are more at an organization or for specific business divisions. But over the years we have seen a distinct shift in focus of all our clients to get forecasts at a more granular level, sometimes for even specific items. These forecasts are difficult to attain using simple techniques. This is where more sophisticated techniques come into play. These techniques are the more complex machine learning techniques which include RF, XG Boost etc.
The loose definition of data science is to analyze data of a business, to be able to produce actionable insights and recommendations for the business. The simplicity or the complexity of the analysis, aka the level of “Data Science Sophistication” also impacts the quality and accuracy of results. The sophistication is essentially a function of 3 main data science components – technological skills, math/stats skills and the necessary business acumen to define and deliver a relevant business solution. These 3 pillars have very much been the mainstay of data science ever since it started getting embraced by the businesses over the past two decades and should continue to be even in the future. What, however, has changed or will change in the future is the underlying R&D in the areas of technology and statistical techniques. I have not witnessed many other industries where these skills are becoming obsolete at such fast rate. Data Science is unique in its requirement of the data scientist and the consulting firms to constantly update their skills and be very futuristic in adopting new and upcoming skills. This article is an attempt to look at how the tool/tech aspects of data science have evolved over the past few decades, and more importantly what the future holds for this fascinating tech and innovation driven field.
In a world of extreme competition, expense reduction being the mantra for most organizations, primarily in the retail and CPG industries, they try to focus on cost cutting and maintaining optimum levels of inventory to gain the competitive edge. To accomplish this, forecasting demand is of utmost importance. It is also not enough to have a macro level sales forecast for the entire organization.
Efficient and accurate demand forecast enables organizations, to anticipate demand and consequently allocate the optimal amount of resources to minimize stagnant inventory. This will result in negligible wastage of resources as well as reduction of costs such as storage cost, transportation cost etc. Another side effect of accurate demand prediction is the prevention of shrinkage so that firms don’t have to give huge discounts to clear stock.
This excerpt will touch upon the steps in demand forecasting and briefly, talk about the different demand forecasting methods. The article ends with some challenges of demand forecasting.
The above photo is not created by a specialized app or photoshop. It was generated by a Deep learning algorithm which uses convolutional networks to learn artistic features from various paintings and changes any photo depicting how an artist would have painted it.
Convolutional Neural Networks has become part of every state of the art solutions in areas like
Self-driving cars in identifying pedestrians, objects.
Natural Language Processing.
A few days back Google surprised me with a video called Smiles 2016 where all the photos of 2016 where I was partying with family, friends, colleagues are put together. It was a collection of photos where everyone in the photo was smiling. Emotion recognition. We will discuss a couple of Deep learning architectures that powers these applications in this blog.
Before we dive into CNN lets try to understand why not Feed Forward Neural network. According to universality theorem which we discussed in the previous blog, any network will be able to approximate a function just by adding Neurons(Functions), but there are no guarantees in time when will it reach the optimal solution. Feed Forward neural networks tend to flatten images to a flat vector thus losing all the spatial information that comes with an Image. So for problems where spatial feature importance is high CNN tend to achieve higher accuracy in a very shorter time compared to Feed-Forward Neural Networks.
Before we dive into what a Convolutional Neural Network is letting get comfortable with nuts and bolts which form it.
Before we dive into CNN lets take a look at how a computer looks at an image.
What we see
What a computer sees
Wow, it’s great to know that computer sees images, videos as a matrix of numbers. A common way of looking at an image in computer vision is a matrix of dimensions Width * Height * Channels. Where Channels are Red, Green, Blue and sometimes alpha is also part of channels.
Filters are a small matrix of numbers usually of size 3*3*3 (width, height, channel) or 7*7*3. Filters perform various operations like blur, sharpen, outline on a given image. Historically these filters are carefully hand picked to gain various features of an image. In our case, CNN creates these filters automatically using a combination of techniques like Gradient descent and Backpropagation. Filters are moved across an image starting from top left to the bottom right to capture all the essential features. They are also called as kernels in Neural networks.
In a convolutional layer, we convolve the filter with patches across an image. For example on the left-hand side of the below image is a matrix representation of a dummy image and the middle layer is the filter or kernel. The right side of the image has the output of convolution layer. Look at the formula in the image to understand how the kernel and a part of the image are combined together to form a new pixel.
Data science helps us to extract knowledge or insights from data- either structured or unstructured- by using scientific methods like mathematical or statistical models. In the last two decades, it has been one of the most popular fields with the rise of all big data technologies. A lot of companies have been using recommendation engines to promote their products/suggestions in accordance with users’ interests such as Amazon, Netflix, Google Play. A lot of other applications like image recognition, gaming, or Airline route planning also involves the usage of big data and data science.
Sports is another field which is using data science extensively to improve strategies and predicting match outcomes. Cricket is a sport where machine learning has scope to dive into quite a large outfield. It can go a long way towards suggesting optimal strategies for a team to win a match or a franchise to bid a valuable player.