The supply chain directly influences a company’s capacity to offer a pleasant customer experience while accounting for many expenditures that affect overall profitability. The supply chain is a vital piece of the jigsaw for corporate success. The supply chain is a network that connects suppliers, businesses, and end users, and it includes everything from raw material procurement through delivery to the ultimate consumer. Given the supply chain’s critical relevance to organizations, many have increased their supply chain management (SCM) efforts.
They’re searching for ways to make procedures faster, cheaper, and more accessible throughout the long path from a raw materials supplier to an end user. Supply chains contain a wide range of activities, people, and organizations, resulting in massive data. This is where supply chain analytics come in—they can transform that enormous quantity of data into easily accessible dashboards, reports, and visualizations that affect essential choices and lead to better results. Easy access to these statistics has become necessary in an increasingly competitive world.
Supply Chain Analytics: An Overview
Supply chain analytics is the analysis of data companies get from their supply chain applications, such as supply chain execution systems for purchasing, inventory management, order management, warehouse management and fulfillment, and transportation management (including shipping). A supply chain is like a line of dominoes: each step affects the next, and problems at any point could make it harder to meet customer expectations.
Each of the above-mentioned pieces of software may have its reporting features that give more information about a specific step in the supply chain, such as the estimated lead times for suppliers, the current safety stock levels at the warehouse, or the number of orders that are filled per hour. But supply chain analytics are most valuable when these systems work together, usually through an Enterprise Resource Planning (ERP) system. Dashboards or reports can then show and explain data from all parts of your global supply chain. This can be done by the ERP itself or by a separate application.
This gives employees a complete picture of this logistics network and lets them see how a problem affects both the beginning and end of the chain. Then they can act quickly and solve the problem as much as possible. Some systems, for example, can analyze the data in real-time and send alerts to warn about potential issues before they worsen.
Ways in Which Supply Chain Analytics is Important For Businesses
Let’s look at how vital supply chain analytics will be in 2022 to understand this idea. Here are the ways:
01. Increasing Sales
The main goal of any business is to get people to buy its products and services and make money. Companies need to find out if they have the proper inventory, raw materials, and supply chain network to make more money. This is where supply chain analytics comes in. It helps companies find a slow-moving stock caused by bad estimates and wrong predictions. It can also help find opportunities to sell more products and meet the demand that was already there but wasn’t being met.
02. Getting things moving faster
It’s not hard to guess that improvements will make supply chains move faster so that inventory levels can be cut and cash can be freed up. The key is to figure out how to make them move quickly, which is where supply chain analytics comes in. It helps you figure out the effects of order size, stock-keeping rules, policies, and changes in demand, as well as how much the expansion of the range added to the average inventory level.
03. Data in Abundance
When you put everything together, you can see why and how supply chain analytics can significantly affect how a business runs its supply chain. Today, supply chains send out more digital information than ever before. Information that used to be written down on paper is now routinely captured and stored digitally. Experts say that by 2022, there will be so much data about how well supply chains work that the biggest challenge for companies will be to turn that data into insights they can use.
Enthralling Features of Supply Chain Analytics
Most supply chain analytics software contains the following features:
1. Visualization of data
The capacity to slice and dice facts from many perspectives to gain insight and comprehension.
2. Processing in streams
Getting insights from different data streams supplied by IoT, apps, weather reports, and third-party data, for example.
3. Integration of social media
Improving demand planning by utilizing sentiment data from social feeds.
4. Natural language understanding
Unstructured data is extracted from papers, news sources, and data streams.
5. Location awareness
Using location data to gain insight and enhance distribution.
6. Supply chain’s digital twin
Organizing data into a complete supply chain model shared across many users to better predictive and prescriptive analytics.
7. Databases with graphs
Organizing information into connected pieces to make it simpler to establish connections, spot patterns, and increase product, supplier, and facility traceability.
Different kinds of supply chain analytics
There are four main types of supply chain analytics that companies should think about right now to make their operations more efficient, which could save them time and money. Here’s a summary of each:
01. Descriptive Analysis
Descriptive analytics examines what has occurred in the past. They are capable of detecting trends in historical data. This data might originate from internal supply chain execution tools and external systems that provide insight across suppliers, distributors, different sales channels, and customers. Analytics may uncover trends and postulate probable reasons for change by comparing the same type of data from various periods.
A manufacturer may monitor a descriptive analytics dashboard daily and find that 50% of its deliveries to distributors are late. Leaders at the organization can then look into the issue further and discover that snowfall in the area where that group of distributors is situated has slowed down their vehicles.
02. Predictive Analytics
Predictive analytics, as the name implies, assists businesses in predicting what could happen and the commercial effect of various situations, such as potential supply chain interruptions and other consequences. Leaders can be proactive rather than reactive by forcing them to evaluate these prospective circumstances before they occur. They have time to plan a strategy for a predicted increase or decrease in demand, for example, and may respond accordingly.
Looking at the same manufacturer, it may analyze the most recent Federal Reserve economic estimates and predict that sales will decline by 10-20% in the coming quarter. With this in mind, it orders fewer raw materials from its suppliers and reduces part-time employee hours over the following month.
03. Prescriptive Analytics
Prescriptive analytics uses descriptive and predictive analytics results to recommend what actions a business should take right now to achieve its objectives. This sort of analytics might help companies solve problems and avoid catastrophic supply chain disruptions by analyzing both their data and partners’ data. Because prescriptive analytics is increasingly complicated, they need more powerful software capable of rapidly processing and interpreting large amounts of data.
Prescriptive analytics may alert the company that one of its leading suppliers in Southeast Asia is on the verge of bankruptcy within the following year. Late orders, limited capacity, and the region’s decreasing economic conditions lead to this conclusion. In response, the manufacturer might seek a meeting with the supplier’s management to determine whether or not they are in financial problems and how it might be able to assist. If no clear resolution is reached, the company can assess alternative suppliers to replace this one before it’s too late.
04. Cognitive analytics
Cognitive analytics attempts to emulate human thought and behavior and can assist companies in answering challenging, complex problems. When analyzing data, these analytics can comprehend things like context. Cognitive analytics does this by utilizing artificial intelligence (AI), notably machine learning and deep learning, which allows it to get wiser over time. This reduces the amount of labor required by staff to develop these reports and analyses and will enable individuals outside the data science team to pull and understand the results.
The firm may be able to automate much of the effort involved in demand planning with its AI-enabled software. The technology could evaluate all available data and internal and external factors to generate highly accurate, precise recommendations for the amount of each product needed to fulfill demand in the future quarter. This eliminates the additional costs associated with producing more inventory than necessary or losing revenues due to a failure to satisfy demand.
Are You Looking for Supply Chain Analytics Software from iTechnolabs?
It is recommended that you take an online supply chain management software to stay ahead of the competition in the supply chain curve. Without a supply chain management platform, every supply chain analytics endeavor is likely to fail. Supplier management, procurement, warehousing and storage, picking and fulfillment, shipment/delivery, and reverse logistics are all managed by this program. Because the supply chain management system maintains each component of this network, it offers the data required to benefit from supply chain analytics. Some SCM solutions even include analytics. And at iTechnolabs, we provide the same. Connect with our experts to get the best software.