- What is Supply Chain Analytics?
- Challenges of Supply Chain Industry
- Best Examples of Supply Chain Management
- How Big Data is transforming Supply Chain Management
- Advanced Analytics in Supply Chain
- Analytics Courses for Supply Chain
What is Supply Chain Analytics?
Supply Chain is a tricky business. One missing entity or a lack of synchronisation can break the entire chain and mean millions in losses for a company. However, the use of analytics in this domain is resolving several pain points in supply chain management at the strategic, operational, and tactical levels. According to Capgemini Analytics, “Supply Chain Analytics brings data-driven intelligence to your business, reducing the overall cost to serve and improving service levels.” For supply chain professionals, it can only mean one thing – to upskill to be able to use advanced analytics to improve operational efficiency and make data-driven decisions.
Analytics uses data to make predictions to help businesses take advantage of the widely available data in the supply chain industry. Data Visualization techniques ranging from charts, graphs and more help decision making, by uncovering patterns and generating insights.
The different types of data analytics are:
- Predictive analytics: While predictive analytics doesn’t actually tell you what will happen in the future, it gets pretty close to that. This process unveils all the patterns and motifs from existing data to present a set of trends that are likely to occur in the future.
- Descriptive Analytics: Descriptive analytics breaks down the insights gathered from any data sets to help businesses understand them better. In a way it summarises all the information gathered.
- Prescriptive Analytics: Prescriptive analytics helps businesses take decisions based on analytical findings. Since it is based on data it is scalable and can provide businesses with direction.
- Cognitive Analytics: Cognitive analytics tries to mimic the human brain by studying data and understanding patterns and interpreting them to draw conclusions. This knowledge thus gained is further used for future reference.
With modern businesses becoming increasingly global, the demand for an efficient supply chain logistics which can perform at scale is on the rise. Operations and logistics teams have become more complex, and issues pertaining to inconsistent suppliers, delayed shipment and more need a robust solution. Following are a few of the most common challenges that the industry of supply chain is facing today:
Challenges of Supply Chain Industry
- Managing Demand Volatility
- Supply chain and logistics process transparency
- Tackling cost fluctuations in Supply Chain
Business analytics and intelligence have improved significantly in the last decade, resulting in a dependable system that can help solve the issues of this domain with meaningful insights, analysis and predictions. Here are 4 case studies about the use of analytics in the supply chain that will motivate professionals in this domain to upskill.
4 Best Examples of Supply Chain Management
- Identify the most efficient shipping carriers through advanced analytics
One of the issues that a Fortune 100 CPG company faced was assessing each carrier and choosing the right carrier for shipment across the globe. Since there were various metrics available, the challenge was ranking carrier performance and choosing the right one for shipping. Understanding the carrier selection framework, the solution by Fractal Analysis followed a step-wise approach by identifying correlated attributes, ranking of carriers, and assessing alternate carriers via what-if analysis.
- Pierian Digital delivered interface, forecasting, and visibility solution to oil & gas major
A Global EPC Service Provider was facing issues without a common user interface across applications to provide end-to-end visibility, visibility of global supply chain and logistics processes, and forecasting for cost and schedule. Pierian Digital’s solution provided Proactive Supply Chain Performance Management Analytics insights positively impacting the top and bottom-line business growth specifically through improvements across the whole supply chain.
- Gartner Analyzes Market for Supply Chain Management Solution
A Fortune 500 company knew its innovative solution for supply chain management had huge potential, however, management needed the validation of the market, the competitive landscape, and opportunities to secure funding. A Gartner engagement answered all their questions by conducting a full product assessment, sizing the market, and analysing competitive alternatives.
- Improved forecasting and inventory planning for a large retailer
Mu Sigma helped one of UK’s leading fashion retailer to build a customised demand forecasting and inventory planning solution for its online channel involving apparel and home furnishing products. They developed an analytical process to forecast demand and estimate launch quantities for new products during seasonal sales leading to an increase in product availability by 8%.
Use predictive modelling to control critical process parameters – A Fortune 100 fertiliser manufacturing company produces fertilisers that must meet quality criteria for key natural elements like potassium, nitrogen, and phosphorus to be within a defined range of specification. The results of thorough data understanding and brainstorming determined that for water soluble fertilisers, in 98.4% of instances the prediction accuracy was greater than 95%. For other nutrient products, in 99.9% of instances, the prediction accuracy was greater than 95%. The client saw multi-million-dollar savings through these tools in fine-tuning the manufacturing process.
Advanced Analytics in Supply Chain
Data is crucial for managing all kinds of supply and storage systems. Predictive analysis, in particular, has emerged as a successful way of ensuring an effective management of supply chain units. Predictive analysis uses copious amounts of data to gain insights on any kind of future scenarios to avert plausible disruptions and prepare for the inconvenience.
Predictive analysis can be used for demand forecasting to understand consumer behavior and anticipate product and consumer demand. Historical data can be used to perform time series analysis and come up with insights that can predict seasonal fluctuations, consumer trends, weather-date purchase correlations. It is a proven method of predicting and managing supply chain logistics.
Data-powered decisions can help supply chain businesses set the standard for operational success. Fast digitisation and ready availability of data has paved the way for big data analytics in the supply chain industry. The use of big data analytics has immense potential to curtail costs, manage demand volatility, and make the process more visible for stakeholders.
Analytics Courses for Supply Chain
Great Learning’s PG Program in Data Science and Business Analytics has been helping professionals to upskill in analytics and transition to a data-driven business environment.
Analytics provides opportunities for people from a diverse set of professional backgrounds, and Parag Janrao is a shining example. As a management trainee, Parag was handling supply chain and finance projects. Here’s how this course, helped him advance his journey in this domain.
Why did you choose an online program?
I wasn’t looking for a full-time classroom or blended program because I know that it will be very hectic to go and attend the classes. Initially when the videos were shared, basics were understood and then during the live mentoring sessions, our doubts were clarified. It depends on personal interest. I was very much interested, so I’ve learned a lot.
What did you think about the fee structure?
I felt that it is a bit highly-priced when I compared with Jigsaw and others. That is when the inputs from alumni convinced me that this has more weight and value than other programs available in the market. When I had updated in my LinkedIn profile that I am doing this program, recruiters reached out to me asking about the program and they started reaching out me looking at my e-portfolio.
How did you transition to Hasbro?
I have moved into my new role after 6-7 months of completing the course. I had no prior experience in analytics because I was working for the functional domain of planning, supply chain and logistics. That was a major issue for me because most of the recruiters look for candidates with hands-on experience in analytics. I had used forecasting and time-series in my previous role then. Hasbro selected me based on my background knowledge of supply chain and logistics because the work I am doing now is on data analytics reporting with a focus on logistics & supply chain. We mainly work on Excel and other tools and PowerBI for visualization.
Any advice or tips to the Aspirants?
There are 6 modules and candidates don’t have to be perfect in all the 6 modules. They should be perfect and thorough in at least one module that they like, and they can get a job in that. In my case, I was really interested in forecasting and time series and so I started working more on those skills. If you really work hard on getting the concepts clear, you should be able to make the transition easily.1