5 Key Applications of Business Analytics in Supply Chain Success

Innovation is the trend of our generation. Amidst all the technological advancements we have made, one that has stuck out is business analytics. Its widespread applications range to industries like human resources, finance, marketing, supply chain, etc. And while there is a growing trend towards the adoption of business analytics in the supply chain industry, there are many businesses that also face inefficient inventory management, poor demand forecasting, and transportation delays due to not incorporating business analytics in their operations.

Sowing the seeds of business analytics has proved quite beneficial for supply chain organisations. While it increases organisational efficiency and drives growth, business analytics is mostly highlighted for its data-driven decision making. With data and better informed decisions, businesses have observed a huge positive impact in their performance and business standing.

In this blog, we’ll cover the long relationship between business analytics and supply chain, its applications, challenges, upcoming trends, and much more!

Key Applications of Business Analytics in Supply Chain

Business analytics is particularly popular for its heavy dependence on data, which makes strategic planning and solution implementation a lot more practical. By leveraging data-driven insights and suggestions, companies can improve efficiency, enhance customer service, reduce costs, and build more resilient supply chains. In totality, businesses improve supply chains from the start of the funnel till the end. Here are the key applications of business analytics in the supply chain domain:

       1. Demand Forecasting

With the help of predictive analytics, business analytics helps predict future demand for products based on market sales, historical sales data, seasonality, and other relevant factors. With the help of techniques like exponential smoothing and machine learning algorithms, demands are forecasted with much more accuracy. Such advanced prediction enables better production planning and resource allocation, in a way that no time or energy is wasted on any front.

      2. Inventory Optimisation

Minimising costs is a great challenge in supply chain management. By classifying inventory based on demand variability and value, inventory management efforts are prioritised and executed accordingly, The order quantity is also optimised to minimise ordering costs and inventory holding. Slow-moving and obsolete stock can also be identified to track inventory turnover rates. Since inventory costs are generally quite heavy, optimising inventory efficiently reduces many costs.

      3. Risk Management

Like any other domain, there are bound to be several risks in the supply chain too, which can be avoided well with risk preparation and management. Analytics helps by evaluating supplier’s performance data like on-time delivery, quantity, quality, etc.) to identify any potential risks. With the help of disruption modelling, the impact of potential disasters (natural and man-made) are studied to develop contingency plans for the same. Advanced planning helps to mitigate the impact of potential disruptions and keep any risks at bay.

      4. Supply Chain Optimisation

The supply chain can be quite long and intricate, depending on the number of parties involved. It is important to identify and eliminate any inefficiencies in the process to make things smooth for everyone in the chain. With analytics, transportation routes and warehouse locations are optimised for cost-effective alternatives and improved delivery times. The entire process is also improved by identifying inefficiencies and bottlenecks throughout, and working to make them better.

      5. Improved Customer Service/Experience

Customer experiences have gained more and more attention in the past few years, and rightly so. Analytics helps provide real-time delivery updates to update customers about their order status. It also predicts customer demand effectively, which enables meeting customer demand and reducing delays, as well as resolving any issues proactively by addressing their concerns quickly. All these factors collectively improve the end user’s experience which shapes their brand loyalty and perspective too!

Also Read –Benefits Of Using Business Analytics In Supply Chain Management

3 Interesting Case Studies Of Business Analytics in Supply Chain

The incorporation of business analytics has proven quite successful in the supply chain domain. Many businesses have adopted the practice of business analytics, and have observed the advantages and impact of data-driven decision making. These success stories also set an example for other businesses, and establish their trust in the process of business analytics. Following are some popular brands that have incorporated business analytics in supply chain:

• Walmart

Challenge: Walmart, a multinational retail corporation, found it challenging to manage a complex global chain with millions of SKUs, suppliers, and the fluctuating demand.

Solution: By utilising predictive analytics, Walmart forecasts demands for products across different seasons and regions, considering holidays, weather, local events, and other factors. This helps minimise markdowns, prevent stockouts, and optimise inventory levels. They also optimise transportation routes and schedules to reduce transportation costs and improve delivery times. And finally, Walmart’s proprietary system collects data from various sources including supplier data, point-of-sales systems, market trends, etc., to optimise pricing, improve inventory management and find other areas of improvement.

• Nike

Challenge: World’s largest supplier of athletic footwear and apparel, Nike manages a global chain with complex manufacturing processes in a fashion landscape that is constantly evolving.

Solution: Nike makes use of data analytics to forecast demand for different products in different markets while considering factors like athlete performance, fashion trends, cultural events, regions, etc. They gain real-time visibility into its global supply chain, enabling faster responses to disruptions and improved operational efficiency. In an effort to make the brand more planet-friendly, Nike also uses data analytics to track environmental impact and identify opportunities to improve the sustainability of its supply chain.

• Unilever

Challenge: Yet another MNC with fast-moving consumer goods, Unilever handles a diverse portfolio with a complex global supply chain and numerous suppliers.

Solution: Unilever leverages data analytics to optimise its supply chain planning, including production scheduling, raw material procurement, and distribution. They also manage and mitigate potential risks such as natural disasters, currency fluctuations, political instability, etc., by analysing data efficiently. In order to improve the sustainability of its supply chain, Unilever also tracks its environmental and social impact to identify opportunities for improvement.

Also Read –Business Analytics: 5 Practical Applications

Emerging Trends in Business Analytics for Supply Chain Management

As we just discussed, the applications of business analytics in the supply chain are many. It only goes a step further with the upcoming trends that will be embraced by business analytics in optimising supply chain management. These trends are predicted to make a significant difference in supply chain optimisation, which will in turn boost operational efficiency and organisational growth for many businesses. Here are some upcoming trends in business analytics for better supply chain management:

     1. Digital Supply Chain Twins

A popular trend in the digital world, a digital supply chain twin will be a virtual replica of the physical supply chain, encompassing all its components like warehouse, suppliers, manufacturers, transportation, etc. Digital twins can help by simulating and testing different scenarios such as demand fluctuations and disruptions to optimise operations before implementing them in the real world. It can also help predict equipment failures and schedule maintenance accordingly.

     2. Blockchain Technology

While it has been around for a while, the use of blockchain technology in business analytics will be even more profound. It can help improve transparency and traceability across the supply chain to improve visibility and reduce fraud. This, in turn, can facilitate trust and collaboration among the supply chain partners and foster an encouraging work environment. With blockchain technology, many supply chain processes can be automated and streamlined, such as contract management and payments.

     3. Artificial Intelligence and Machine Learning

The implementation of artificial intelligence (AI) and machine learning (ML) algorithms will help predict demand, identify potential disruptions, and optimise inventory levels. By analysing historical data, market trends, and other external factors, it will also improve the accuracy of demand forecasting. Advanced AI and ML algorithms will optimise transportation routes for greater efficiency and cost savings, while also detecting anomalies and issues such as quality defects, security breaches, etc. in real time.

     4. Internet of Things

As grand as the internet of things (IoT) sounds, its impact serves as testimony to the hype. IoT sensors can provide real-time data on equipment status, inventory levels, and transportation logistics. It can also be used to monitor equipment health, product potential failures and plan maintenance accordingly. From raw materials to finished goods, IoT can help enhance visibility across the supply chain for better management.

     5. Sustainability Analytics

Like Nike and Unilever, many brands hold sustainability at the centre of their focus. It is an upcoming trend for businesses to track and monitor the environmental impact of their supply chains in terms of carbon emissions and resource consumption. Supply chain processes can be improved by identifying and sourcing sustainable materials and suppliers, and optimising supply chains for reducing waste, circularity, and maximising resource allocation.

Also Read– Roadmap to Master Business Analytics

Challenges in Implementing Business Analytics in Supply Chain

Despite all the applications and benefits of business analytics in the supply chain, there are several obstacles in the way which make it a tricky decision. Certain challenges in implementation of business analytics in supply chain prevents businesses from making complete use of this practice and savouring its benefits. Here are some common challenges faced in implementing business analytics in supply chain:

       1.Data Quality Issues

The one thing that needs to be a hundred percent correct and untainted in business analytics is data. Inconsistent, inaccurate, and incorrect data can lead to improper analysis and flawed decisions. Missing values can not only hinder the quality of the data, but also limit its scope of analysis and accuracy of insights. The absence or inaccuracy of data makes the data quite unreliable and incapable of analysis.

       2.Data Integration Challenges

Data is generated on various platforms today. Gathering data from multiple sources, internal as well as external, can prove quite tedious and time-consuming. Data requires a comprehensive analysis, which is difficult when the data is not integrated but trapped in different systems.

       3.Lack of Skilled Resources

While data collection and cleaning is one part of the process, it’s a whole another task to analyse the data and interpret it efficiently. Finding good business analysts who understand the supply chain as well as the data collected, with strong analytical skills, can prove to be quite difficult. A shortage of skilled talent in this area hinders the effective implementation and utilisation of analytical tools as well as efficient data analysis and interpretation.

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Frequently Asked Questions

       1. What is the role of business analytics in supply chain management?

Business analytics plays a significant role in supply chain management by enabling data-driven decision making, improved efficiencies in processes, risk mitigation, enhanced customer experience, and a competitive edge over other businesses.

       2. What is the main aim of supply chain analytics?

The primary goal of supply chain analytics is to optimise supply chain management by increasing agility and responsiveness, improving efficiency and reducing costs, and above all, improving customer satisfaction.

      3. What are the benefits of supply chain analytics?

Some benefits of supply chain analytics include increased revenue, lower inventory costs, reduced transportation costs, minimised waste, and better resilience in times of disruptions and changing market conditions.

       4. What are the other applications of business analytics?

Business analytics has many other applications in the fields of finance, marketing, human resources, manufacturing, retail, healthcare, etc., where it enables businesses to achieve their strategic goals with data-driven insights and informed decision making.

Conclusion

The aim of business analytics is to drive organisational growth by smoothening processes, one such process being the supply chain. Most businesses, marketplaces and e-commerce alike, have a unique supply chain of their own. With the help of business analytics, organisations can improve supply chain management and optimise their operations to reduce costs, minimise extra effort and focus on strategising and growing in the long run. It is, of course, an elaborate process, but the end results make every effort worth the try!