{"id":5213,"date":"2015-05-24T15:10:28","date_gmt":"2015-05-24T09:40:28","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=5213"},"modified":"2016-09-04T11:45:32","modified_gmt":"2016-09-04T06:15:32","slug":"time-series-decomposition-manufacturing-case-study-example-part-2","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/time-series-decomposition-manufacturing-case-study-example-part-2\/","title":{"rendered":"Time Series Decomposition – Manufacturing Case Study Example (Part 2)"},"content":{"rendered":"
\"Painter\"<\/a>

Painter and Time Series Decomposition – by Roopam<\/p><\/div>\n

\n

In the previous\u00a0article, we started a new case study on sales forecasting for a tractor and farm\u00a0equipment manufacturing company called PowerHorse. Our final\u00a0goal is\u00a0to forecast tractor sales in the next 36 months. In this article, we will delve deeper into time series decomposition. As discussed earlier<\/a>, the idea behind time series decomposition is to extract different regular patters embedded in the observed time series. But in order to understand why this is easier said than done we need to understand some fundamental properties of mathematics and nature as an\u00a0answer to the question:<\/p>\n

Why is Your Bank Password Safe?<\/span><\/h2>\n

Mix one part of blue and one part of yellow to make 2 parts of green<\/em><\/span>: a primary school art teacher writes this on the blackboard during a\u00a0painting class. The students in the class then curiously try this trick and Voil\u00e0<\/em>! they see green colour emerging from nowhere out of blue and yellow. One of the students after exhausting all her\u00a0supplies of blue and yellow curiously\u00a0asks the teacher: how can I extract the original yellow and blue from my\u00a0two parts of green? This is where things get interesting, it is easy to mix things however it is really difficult (sometimes impossible) to\u00a0reverse the process of mixing. The underlining principle at work over here is entropy (read the article on decision trees and entropy<\/a>); reducing entropy (read randomness) requires a lot of work. This is essentially the reason why time series are difficult to decipher, and also the reason why your bank password is safe.<\/p>\n

Cryptography, the science of hiding communication, is used\u00a0to hide secrets such as bank passwords or credit card numbers and relies heavily on the above property of mixing being easier than “un-mixing”. When you share your credit card information on the internet it is available on the public domain for anybody to access. \"\"However, what makes it difficult for anyone without the key to use this information is the hard to decipher encryption. These encryptions at the fundamental level are created by multiplying\u00a02 really large prime numbers. By the way, a prime number (aka prime) is a natural number greater than 1 that has no positive divisors other than 1 and itself. Now multiplication of two numbers, no matter how large, is a fairly straight forward process like mixing colours. On the other hand, reversing this process i.e. factorizing a product of two large primes could take hundreds of years for the fastest computer available on the planet. This is similar to “un-mixing” blue and yellow from green. You could learn more about cryptography and encryption by reading a fascinating book by Simon Singh called ‘The Code Book’.<\/p>\n

Time Series Decomposition – Manufacturing Case Study Example<\/span><\/h2>\n

Back to our case study example, you are helping PowerHorse Tractors with sales forecasting (read part 1<\/a>). As a part of this project, one of the\u00a0production units\u00a0you are analysing is based in South East Asia. This unit is completely independent\u00a0and caters to neighbouring geographies. This unit\u00a0is just a decade and a half old. In 2014 , they captured 11% of the market share, a 14% increase from the previous year. However, being a new unit they have very little bargaining power with their suppliers to implement Just-in-Time (JiT) manufacturing principles that have worked really well in PowerHorse’s base location. Hence, they want to be on top of their production planning to maintain healthy business margins. Monthly sales forecast is the\u00a0first step you have suggested to this unit towards effective inventory management.<\/p>\n

In the same effort, you asked the MIS team to share month on month (MoM) sales figures (number of tractors sold) for the last 12 years. The following is the time series plot for the same:<\/p>\n

\"Tractor<\/a><\/p>\n

Now you will start with time series decomposition of this data to understand underlying patterns for tractor sales. As discussed in the previous article, usually business time series are divided into the following four components:<\/p>\n