The traditional supply chain execution model is becoming more cumbersome with a mix of operating systems, excessive pressure from the pricing system, and increasing customer expectations. Additionally, global economic impacts like global recession, increasing fuel costs, and competition from low-cost business outsourcers. Unless analyzed, all these factors create potential waste in the supply chain business. There comes supply chain analytics or data analytics solution in the supply chain.

As we all know, data analytics examines raw data to conclude, collected, and analyzed data. The supply chain and many industries use it to make better business decisions using existing theories like every business supply chain too supposed to add values in business. However, to dig deeper into the supply chain and increasing savings and efficiencies, there is no other choice than advanced analytics tools and mechanisms.

The supply chain industry itself is a complex area as it plays a prominent role in a company's profitability and cost structure. Apparently looks simple; this area is not a simple part of the business, which demands more of a predictive approach than a reactive view of the data. Thus, the use of analytics tools definitely gives a competitive advantage.

Implementation of supply chain analytics, along with tools, can capture and analyze the massive amount of accumulated data resulting from a product moving from one point to another. With supply chain analytics, we can focus more on the future instead of studying the past.

Related post - How does Transaction data Relate to Big Data analytics?

Definition of Supply Chain Analytics

Supply chain analytics is a form of representation of all data generated from various segments of the supply chain. All these data are collected, processed, and displayed in a visual representing tool in a meaningful way, for example, in the form of charts and graphs. Based on these graphs and charts, a business can make planning and future decisions.

However, the supply chain is a complex domain that requires various domains and people for its functioning. "Treasure hunt," which is a common term used in the supply chain, often refers to digging through a huge amount of generated data due to a single supply chain operation. This "treasure hunt" needs proper plans of operations and the application of the right tools. Alongside this, it needs a proper strategy road map to represent relevant data instead of presenting too much information.

Why is supply chain analytics important?

According to a market study, nearly 21% of supply chains stated that data visibility is one of the biggest challenges in supply chain management. Of course, we can solve that by implementing an analytics solution combined with AI and machine learning. But that could be a complex as well as costly method. On the other hand, supply chain analytics addresses this issue quicker, smarter, and more efficiently. It enables the following things:

Gain a significant ROI. When a business adopts a standardized supply chain analytics system, they can integrate data from different ERP systems and consolidate it in one central location. This significantly reduces the time spent navigating between these systems.    

Better risks analysis.  Supply chain analytics is using modern predictive analytics mechanism to recognize patterns, key trends, patterns, and potential disruptions. This helps to protect the most valuable assets of enterprises. Besides, it creates sophisticated risk, mitigation models. The supply chain analytics risk management strategy prioritizes vital business components, analyzes the full supply chain interdependencies, and uncovers areas where disruption can lead to failure.

Increase accuracy in business planning. Utilizing supply chain analytics, you can analyze customer data, thereby helping businesses better predict future demand. This also leverages profit analysis so that an organization can decide what products should be minimized depending on profitability. This also gives a better understanding of customer needs.

Paved the way for a lean supply chain. When customized properly, supply chain analytics solutions can help business leaders to improve the demand planning process supply chain planning, reduce wastage and excess spending, reduce excess inventory costs, improve the demand forecast, and enhance the capacity planning process.

How is data analysis used in Supply chain management?

In a nutshell, Data analysis is used extensively in Supply Chain for following

- Planning, Replenishment planning to Product Launches

- Transportation Analysis and Landed Costing

- Scheduling of assets and resources

- Fulfillment Process Analysis

- Demand Planning

- Purchase Order Analysis

- Vendor Analysis

- Supply Chain Network Design

- SKU Rationalization

- Facility Design, Layout Planning, and Simulation

Identification of Process and Problem: Utilizing supply chain analytics, you can identify the process for improvement or get insights from it. To analyze the process, you should have a clear question on the problem in your mind beforehand. For example, you are thinking of improving the packaging process through analysis. So, if you believe the average packaging time of a batch is too long, then in the analysis, the packaging time must not be a straight line, and after improvement, it should be downward.

What are the different analysis strategies used in supply chain analytics?

Now that we got an overview of supply chain analytics, it is also necessary to know what methods to use and where to look for such analytics? Here are some of the methods:

Descriptive Analytics

As the term suggests, descriptive analytics gives users clarity on what’s happening within the supply chain at a certain moment.  These analytical data require corporate databases and are dependent on everyday operations. This type of analytics focuses on unique truth across the supply chain, including entire systems, i.e., internal and external systems. Few things that can be uncovered by this type of analytics are:

• Customer-Product Matrix – Purchasing of one product opens the opportunity for another product.
• Customers per SKU – How many customers purchase a product. If many customers purchase a product that is unlikely to be purchased, it asks for sales strategy modification.
• Coefficient of Variation – The average demand for a product is calculated by the last 12 months of shipments
• Alert Reporting – Alert tools based on logic can help create alerts for situations like out of stock.

These are traditional BI findings and can be displayed in a dashboard. This type of analytics reveals past incidents and its relation to the future.

Predictive Analytics

As the term suggests predictive analytics forecasts the future of business and prepares changes accordingly. This analytics closely adds AI and machine learning with it along with manual effort. Businesses use predictive analytics for the fine-tuning of their supply chains.

Image source

Prescriptive Analytics

This is the name of the “final frontier of analytical capabilities” in supply chain analytics. This shows the final result of a collaboration between predictive analytics and descriptive analytics. Prescriptive analytics applies to the past, present, and future. It gives business potential routes using data from the past and present—this collaboration between data from the past and present highlights future opportunities to prevent future risks.

A constant flow of structured numerical data and unstructured video and image information acts as a pillar in this analytical method. Furthermore, without advanced technology like machine learning, this type of analytics hardly becomes available. However, this method is useless unless you know what questions should be asked and how to react to them. But with proper utilization of this analytical method, organizations can gain an advantage over operations that do not use analytics.

Cognitive analytics

Supply chain analytics works as the foundation for applied cognitive technologies to the supply chain process. One of these technologies is artificial intelligence (AI). Such cognitive technologies behave like a human. It understands, provides reasoning, learns, and has the power to interact like a human and with enormous speed and capacity.

The application of cognitive technologies in supply chain analytics is ushering a new dimension of supply chain optimization. This has enormously improved the forecasting, innovation in the process along with generating breakthrough ideas.

What are some drawbacks of supply chain analytics?

As per the market study, setting up an efficient analytics flow with AI and machine learning can be difficult and costly. The cost of entry is one of the major challenges of supply chain analytics because to operate at the highest analytical level requires some cutting edge technology, which is an additional burden on an already costly system. Additionally, the cost increases the supply chain segment and which area you are looking to optimize. For example, one of the vital areas of the supply chain is warehouse management.

To achieve the highest data visibility level and then materialize it with a proper workforce needs huge funding, which is a tedious job for a small business to achieve. There are budgetary constraints in this type of in-depth analytics. However, with cloud technology intervention, smaller organizations are getting their own way for hands on for affordable prices.

What is the future of Supply chain analytics?

The global supply chain analytics market size is expected to reach USD 9,875.2 million by 2025, registering a CAGR of 16.4% from 2019 to 2025, according to a new study by Radiant Insights, Inc. Thus, the growing need to manage these enormous business data and deriving insights from it brings the demand for supply chain analytics in the market. The enterprises are well aware of the benefits of using supply chain analytics, which accelerates the demand for analytic solutions.

Supply chain analytics help organizations achieve growth, increase market share, enhance profitability by deriving insights, and make strategic decisions. This solution provides a holistic view of the supply chain and enhances sustainability by reducing inventory costs while accelerating time to market for products. Why is the optimization of the supply chain necessary? Because unless you do it, there is a high chance of below factors which become an obstacle to driving the growth of the market –

- shortening product life cycles

- low supply chain visibility

- elevated warehousing costs

- ineffective supplier networks

- redundant forecasts

- fluctuating customer demands

The benefits of supply chain analytics are expected to encourage its adoption in other end-use applications such as consumer goods, retail, healthcare, and manufacturing.

What are some of the key trends in Supply chain analytics space

As technologies such as AI and Machine learning are becoming more commonplace in supply chain analytics, organizations may see an explosion of further benefits. Previously the scope of processing natural language data was limited. With the proliferation of AI and machine learning, those data are now being analyzed in real-time.

Data from disparate sources are now being analyzed rapidly and comprehensively by AI. It then provides real-time analysis based on data interpretation. Hence, it provides companies with a broader supply chain intelligence. These efficient technologies also support new business models.

Similarly, Machine learning is being utilized when businesses need to deal with large dynamic data sets gained from supply chain analytics.

Final thoughts:

In today’s scenario, effective supply chain analytics requires to be more customer-centric. Maintaining integrity and accuracy, it must respond quickly. The need for the hour is supply chain analytics solutions that can quickly analyze massive amounts of data from disparate data sources. This data could be unstructured and natural language-based data. Finally, supply chain analytics must predict a wide number of supply chain variables, including external forces such as workers, weather, regulations, and many other factors.