Quoting Stephen Hawking “Intelligence is the ability to adapt to change.” Technology is undoubtedly fast evolving. It reflects in almost every other domain. Abiding by technological changes is one thing we all do, given a scenario where leading tech like data science rules!
Machine learning, artificial intelligence and data science have revolutionized different domains of work. Supply chain is one such area where machine learning algorithms have had and continue to have a great impact. Here in this blog, let us get into popular data science applications in data science answering the question of why upgrade into it
Overview of Topics Covered
Data science use cases in Supply chain:
Before getting into popular applications of data science in supply chain, we should first address the umbrella domain of Supply Chain Management or SCM. From procurement to outbound logistics, managing each and every step under the whole supply chain process comes under this title. Data science has a key role to play when it comes to Supply Chain Management. The role of data science can be segregated into the following:
1. Data science for procurement analysis
Getting the right and best supplies is integral when it comes to Supply Chain Management. It is also one of the first steps in the process. Cost optimization without compromising on quality comes important while following the procurement protocols. Predictive analysis which is a major application of data science is significant in this domain. Predictive analysis can also help optimize logistics and transportation in the supply chain.
2. Data science for inventory optimization
Planning and positioning of inventory come under inventory optimization. Simply put, inventory optimization deals with identification of wants and meeting those requirements to avoid stockouts. With the help of analytics, and tracking warehouse and manufacturing data, we will get to have a clear picture on how much to store and where. Data science also helps in replenishment planning.
3. Data science for network planning
Network planning basically deals with keeping in channel and in loop different inventory and manufacturing units. Based on a properly channelized network, customer demands can be properly met without compromising on quality output. The market demand can be analyzed using data analytics, which plays key when it comes to planning the network.
4. Data science for analyzing demand
There are different factors that can influence sales and altogether the market demand. Some of this include the average income, market trends etc. Having a sound data set on different key parameters that influence supply chain business helps profit making. Moreover, it helps satiate the demands of different consumer segments taking into consideration the perspectives of distributors and stores.
Various leading firms in India like Mahindra Logistics, TVS Supply Chain Solutions, EIM Solutions make use of data science in supply chain. Mahindra Logistics uses data analytics for freight management. TVS Supply Chain Solutions uses NLP and ML for automation. At the same time, EIM Solutions focus on delivering smart manufacturing solutions.
Even in the future, data science will have significant applications in the supply chain domain. Especially, when it comes to the following:
1. Dynamic pricing
2. Cognitive sourcing
3. Demand forecasting
4. Shipment optimization
There is definite possibility that in the future, data science in supply chain will revolutionize businesses by working on:
- Virtual reality integrated with maintenance
- Autonomous operation of complex machineries
- Demand forecasting with predictive big data analytics
- Retail Sales Forecasting with Machine learning
By now, you must be aware of the possible prospects of data science in supply chain. Let us now plunge into the important question of how to make a career switch into data science.
Why is data science important?
Learning data science is significant because of several reasons. Coming to the supply chain sector, data science applications can make tremendous difference by increasing product portfolio and meeting demands. Moreover, it paves way for a greener supply chain with route optimization. Besides, it helps in easily identifying demographic changes for responses. Also, AI applications help in unbiased & error-free decision making.
Coming to the job prospects in the data science field, you can don the roles of a Demand planner, Digital supply chain manager, Supply chain data scientist, Supply chain analyst etc. with a supply chain background and data science skills.
Therefore, skilling up in data science is a big and the best decision you can make if you are aiming for a lucrative career. MNC’s hire for data science professional roles since they extensively apply data science in their supply chain business strategies.
For instance, IBM uses it for Demand Analytics. Deloitte uses it for Finished Inventory Optimization and McKinsey applies data science for Procurement Analytics. So, opportunities are going to be plenty and upgrading with data science can definitely help!
Career switch from supply chain to data science
If you are aiming for a career leap into data science with a background in supply chain, you might need to skill up in the following:
1. Python programming fundamentals
2. Supply chain statistical analysis
3. Data cleaning, manipulation, forecasting
In addition, you can get Python, Matplotlib, Seaborn training. While these are the fundamentals to learn, it is not limited to just this. You must be learning about data analytics in depth along with core concepts like machine learning algorithms.
To be a data science professional or to excel in data science roles like Big data analysts, you need to get the right training clubbed with proper industry exposure and project experience.
You can start off with online learning materials and free YouTube Tutorials. But, to advance your learning experience, you can enroll in data science online courses. So, make maximum use of the opportunity that you have and upgrade your skills in data science. Because the applications are plenty and job prospects are high!
In this article, we have discussed the important use cases of Data Science in supply chain, followed by the importance of learning it and career prospects in the field. Procurement analysis, inventory optimization, network planning and demand forecasting are some important fields in Supply Chain Management where Data Science can potentially help. Having an academic/work background in Supply chain management plus Data Science skills can undoubtedly give a leading edge to your career and make your portfolio stand out. There are numerous job opportunities when it comes to the field of Supply Chain analytics. Having the right kind of professional training can help you land in your dream Data Science job roles in Supply Chain Management.
Analyzing and interpreting data, helps understand data behavior in depth. Honing your Data analytics skills can help identify customer trends, which in turn can contribute in length to managing the supply chain. Predictive analytics and demand analytics have huge scope in this context. Further, having Data Science skills can help with well managed production, pattern recognition, improved accuracy and management, enhancement in supply chain, lesser costs and better performances.
Data Science training combined with a background in supply chain can open the doors to a plethora of job opportunities. One such is a supply chain analyst. First of all, an academic background in logistics, business management or supply chain can help. You should be having a sound knowledge of inventory management along with analytical and mathematical skills. Inorder to interpret meaningful insights from a huge amount of data, you should be having skills in data analytics and visualization. Soft skills such as communication and interpersonal skills are also vital.
Digitization is playing a huge role in supply chain management these days. Hence, having a knack for it is an essential skill if you want to start off with a career in supply chain. Further, you should have knowledge in demand forecasting which is data driven, cloud computing, system automation, end to end understanding of how supply chain works. The skills should not be limited to this, but these are some fundamental know-how’s you should have. You can learn these core skills by signing up on a professional online training platform. With live training, mastering these key concepts will be easy. Especially, if you can get 1:1 mentorship and personalized training.