6 ways Businesses can Optimize Big Data in the Supply Chain
Updated: Feb 7
There is virtually no sector in the economy that has remained unaffected with the advent of big data. Insight mining has become the core of any decision ever taken and continues to drive the market based on consumer behavior patterns and habits. Amidst such transient scenarios, retailers often wonder how they can use big data to make each and every step of their supply chain process more efficient and productive.
The rise of Big Data in Supply chain
Big data refers to the huge volumes and amounts of data which may be structured or unstructured. While data analytics refers to the qualitative and quantitative technique of analyzing this data that which gives insights which can then be used to enhance productivity. As corporations face an immense amount of pressure to increase profits margins and shorten delivery cycles, big data insights can come in real handy in achieving those.
Big data has 3 basic V’s that define it-
It basically refers to the magnitude or depth of data available for analysis. One might normally think that data is numbers of text picked out of a report somewhere, but it is everything that is ever generated on the internet and can help in interpreting how the market might function. It can be a tweet, an image, a meme, audio, videos etc.
Data today is expanding at a rate that doubles every two years. IT World Canada Projected that by 2020 the sheer volume of World’s digital data would fill a stack of iPad Air Tablets that would extend from earth to the moon.
It refers to the frequency at which the data changes.
Real-time data is projected to grow ten folds by 2025. Very close to real-time data is near time data, which includes a time delay between the occurrence of an event and its publication. If you ever access a website that publishes stock prices on a 5-minute delay, you have access to the near time data.
It refers to how the data can be defined as structured, semi-structured or unstructured.
Structured data is the one which has been organized into a formatted repository, more often than not a database so that it’s accessible for processing and analysis. Semi-structured data aren’t parts of a relational database, but it has been organized in a way that it can be easily uploaded into processing tools like excel for analysis. Un-structured data is not contained in any form of database or structure, but it consists of numbers, texts, videos, images etc.
Zara’s secret to success, Big Data, and an agile Supply Chain
It takes about 20 days for Zara to design and stock an outfit in all its international stores.
How may you wonder?
A typical day at Zara starts with the entire staff and manager huddled together, discussing what trends are in, what is selling fast and what needs to be restocked. This immaculate information system isn’t just limited to the internal workings of the company. The brand uses big data to determine what kind of trends are selling and what is slowly fading away.
This info then is transferred to a team of 300 designers who quickly work on these trend preferences to create stylish clothing that flies off the shelves within minutes.
And the time it takes for a design to be conceptualized and reach the stores? 21 days or less.
To stay abreast of the market and maybe a few steps ahead, Zara doesn’t bank of showing off its clothes. Instead, it focuses on working on the consumer feedback to capitalize on possible opportunity and improvements.
Zara’s pain-point in Asia is the logistics, where the stores aren’t that well connected to the distribution hubs as in Europe. But with the opening of factories in Vietnam and China, this problem has been foreseen to be solved. And also the geological differences are likely to be in variety in global trends which Zara can again bank upon.
Why is it important to optimize big data in a supply chain?
With the magnitude at which the data had been growing, it has been predicted that roughly 1.7 megabytes of new info will be created for every human for every second at that point, the digital universe will be 44 zettabytes strong (up from a current 4.4 zettabytes).
With data growing at such a rapid pace, it has become paramount for the supply chain managers to wrap their hands around this data and use it for their benefit. The growing number of software that analyzes and manages this data has made the task extremely easy.
To put it simply, simply having data at your disposal isn’t enough. You need to be able to put the insights into meaningful decisions making that helps drives operational efficiency and productivity.
6 ways business can optimize Big Data in Supply Chain
Not using big data and its insights mean money left on the table which can weaken a company’s profits and its position in the market. Here are a few in which big data will be of immense help-
1. Establish a business and technical owner for all big data initiatives.
Today the corporate culture is moving at a neck-breaking speed and the heard of the company can’t afford to not pay close attention to every minute detail. Running a global supply chain operation requires a deep knowledge of planning, sourcing, delivery, measurement and a
well-informed viewpoint. This requires that the person overseeing all these should be
technically sound and as efficient as possible.
2. Break down the communication silos between teams.
Businesses that segregate operation and don’t collaborate much can never fully harness the potential of cross-functional platforms. Simply by aggregating data into single data multidepartment unit systems, a company’s data capabilities increase immensely. Since supply chain is made up of multiple branches, a centralized data should connect each branch for a well-rounded understanding.
3. Normalize data and terminology across all platforms and departments.
An analysis is a very hectic process. Department specific Jargons make it very troublesome for organizations to leverage data which requires accurate analysis. By keeping the data terminology similar across all businesses, info processing can be faster and understood much more easily, resulting in more efficient decisions.
4. Establish goals which are doable and within reach.
Big data is the oracle of business forecasting. However, like all businesses processes, implementation can take time. Executives need to outline clear goals for revenue, sourcing, and P2Pdevelopment that makes sense for each individual team as well as make goals that aim to achieve a result collectively.
5. Organize data necessary for business growth.
Not all data is equal. Some info is more valuable than the others. Executives must identify top business priorities to pull the data necessary.
6. Use business intelligence to spend data for better sourcing strategies.
Finding out the source uses is much easier and done more efficiently with organized data. Big data can give a clear picture of expenses and ROI, but it is paramount for a company to identify the sourcing inefficiencies and ensure maximum profits.
Wondering how you can use big data to make your supply chain more efficient? Contact us at Datahut, your big data experts.