The logistics industry represents 12% of the global GDP and is growing at a CAGR of 5% per year. Based on these estimates, global logistics spending will surge to more than $15 trillion by 2023. These estimates in conjunction with surging supply chain costs, created by the dynamics of the global pandemic, have increased the need for innovative technology to increase efficiency and lower supply chain costs. Data is now incorporated into every industry, and it is the new oil that’s powering the innovation engine. Data science can disclose hidden insights into a company’s daily operations, allowing it to develop more efficient and productive operating procedures, price risks, and predict market patterns, which is fast making inroads into every supply chain function as well. Aswini Thota, Analytics and AI leader, Bose Corporation, unravels the importance of DATA Analytics in supply chain and how can companies best leverage it to their competitive advantage…
We live in a digital world. From ordering food, and buying groceries, to purchasing cars, we have digital options for everything. But what if I told you that the function responsible for shipping goods and delivering products is still largely paper-based? Yes, according to the Digital Container Shipping Association (DCSA), only 0.1% of bills of lading – just one of many paper-based trade documents – are issued electronically.
There are several obvious reasons businesses should digitize their supply chain operations. Some of them are:
Delays in supply chain processes because of document errors
Vulnerable to security threats
Wastage of valuable natural resources
But the most significant opportunity lost by not digitizing service is the ability to gather data and make data-driven decisions. Most successful companies were always innovative. They constantly try to gauge what their customers want and develop solutions that strike the right chord with them. For a company to innovate using service, all the components of its offering should work like a well-oiled machine, and the supply chain function is at the heart of this innovation.
Organizations realized a long while ago that innovation is key to success, and Covid-19 has created an environment where industry leaders must think on their feet and make fast decisions. The sudden shift in priorities caused by Covid-19 has exposed organizations that didn't have a foundational understanding of using modern technologies.
Perhaps a critical vulnerability that organizations realized after covid-19 was their lack of ability to make data-driven decisions. Enterprises that had a wellestablished data analytics practice were able to run what-if scenarios quickly and pivot to meet customer needs. Organizations that realized the value of timely information made data-driven decisions their top priority to correct these shortcomings.
DATA ALL THE WAY
Data is turning out to be a key enabler for businesses, big and small. This is specifically true for supply chain functions. Below are some ways big data, a byproduct of electronic services, is changing the supply chain landscape.
Transparency: Transparency and accountability have become essential for a good SCM function. Having transparency in SCM means leaders can precisely know the status of each step in the supply chain pipeline. A transparent supply chain function also assumes that the data retrieved is quality tested and is of the gold standard. A transparent and well-governed SCM function can help business leaders understand the pain points at any given period and help them provide timely solutions.
Overseeing the entire SCM function and identifying the problems based on heuristics and observations is a gigantic task. Instead, digital solutions help organizations to store data corresponding to all of the interactions and events. Modern data techniques such as artificial intelligence are very good at analyzing historical data to identify abnormal trends. Anomaly or outlier detection uses AI algorithms to identify rare and unexpected events that are different from standard patterns. Anomalies in SCM function are common. For instance, large volumes of orders coming from the same IP, distribution times that don't follow the normal curve, item prices being significantly below the previous sales price, etc., are some situations that SCM handles regularly.
Modern businesses are built on customer satisfaction. By digitizing services, organizations can instantly understand the details of their supply chain pipeline. Unlike paper-based systems, digital tools allow organizations to collect and store digital touch points. The collected data can then be analyzed to generate critical insights such as traffic patterns, reasons for returns, service delays, etc.
Efficiency: Digital supply chains eliminate the need for manual data entry and customer follow-ups. With digital label scanners and process automation technologies, all parties involved in the supply chain pipeline can be updated about the outcome of each step and status. This process will, in turn, help organizations deploy their workforce more efficiently. Another area where digital supply chains can be very effective is communication. Having efficient and speedy communication is crucial to establish trust between suppliers and businesses. Having digital supply chains will help you swiftly order components and avoid any shortages.
Not only that, but you can also use the historical data captured by your supply chain systems to analyze where the shortcomings are historically and be proactive about it. For instance, you can study patterns in your data and answer key questions such as – are there any suppliers that are consistently slow at responding? What are some of the reasons for not receiving effective responses from the suppliers, etc.?
Forecasting: Forecasting may be the biggest opportunity your supply chain data can open up. Having a good forecast is critical for organizations on multiple levels. Forecasting helps organizations procure the right number of raw materials and parts to build the product, deliver the products to suitable warehouses on time, distribute the products to resellers, and finally meet the customers’ demand. None of this is possible without having good historical data. Forecasting is not just limited to existing products in the market by combining historical sales data with related organizational data such as media spending, promotions, customer segments, etc. There are several proven statistical techniques to generate demand forecasts; some of them are Simple Moving Average (SMA), Exponential Smoothing (SES), Autoregressive Integration Moving Average (ARIMA), etc. In recent years, organizations have been using neural network-based approaches such as Long short-term memory (LSTM), Gated recurrent units (GRUs), and Autoencoders.
Because of the global supply chain shortages and the increased demand for ICs, our ability to accurately demand customer demand had never been this important. If you over manufacture a specific product, it will just be sitting on the shelves, and you wasted ICs that could have been used on other better-selling products. If you under forecast, you lose the opportunity to sell your products. At Bose, we are obsessed with meeting and exceeding our customer’s expectations. We seek to achieve this goal by improving how we forecast customer demand to ensure we can offer our customers the right product at the right time.
Organizations can develop intelligent algorithms to gauge the market share, customer interest, and product demand for new products before they are even launched. When done right, demand forecasting can help supply chain leaders accurately forecast the number of units to manufacture in a specific time period along with the number of units to distribute to each store.
Intelligent solutions: Every organization's supply chain deals with enormous amounts of procedural documentation. This can range from delivery orders, docking receipts, bills of landing, etc. The back-office team is expected to process and store this information. AI algorithms in computer vision and natural language processes can read through the document and translate the images to text, which can then be stored in a database for consumption. Another popular application of AI that's penetrating every enterprise function is the chatbot. Chatbots can provide answers to the most commonly asked questions. Most consumer product centric organizations have to answer questions about delivery requests, tracking, and order-related issues.
Often, the questions your customers ask are repetitive and can be avoided by taking them on a guided search through your knowledge base that contains answers to frequently asked questions. We will continue to see technological innovations that can deliver a different level of personalization. Metaverse, digital twin, etc., are examples of taking personalization to the next level. AI technologies such as chatbots and voice assistants will be embedded into mainstream applications to promote the personalization of services.
Re-deploy talent: Using archaic non digital practices not just waste your financial resources, they also hurt your employees. The services of your intelligent and enthusiastic workforce are being decimated by manually entering the data or by scanning and filing hundreds and thousands of bills. By digitizing your supply chain end-to-end, you can divert your workforce from mundane and trivial tasks to more value added tasks.