Data Science



The Complete Guide to Supply Chain Analytics: Skills, Benefits, and Challenges

As with different analytics domains, supply chain analytics delves into the data drawn from multiple sources and subsequent analysis to draw actionable insights. This, in turn, fuels customer-centric decision-making in the long run. 

What sets it apart is that supply chain data analytics plays around with information on inventory, order management, procurement, etc.

If supply chains as a whole represent dominoes, analytics is the glue that coordinates the moving parts and streamlines the overall process. 

When operations like estimating safety stock levels for the warehouse and predicting lead time for suppliers are integrated with Enterprise Resource Planning (ERP) systems, business operations tend to run more smoothly.

Moreover, the ability of dedicated systems to analyze and shoot alerts in real-time is a major advantage in any logistics-heavy network. On that note, let’s dive deeper and understand the significance, benefits, and industry challenges surrounding Supply Chain Analytics.

What does supply chain analytics do?

In practice, supply chain analytics pulls data from third-party sources, applications, and cutting-edge IoT platforms to enhance supply chain management’s strategic and operational processes. However, a tactical approach is needed to manage large amounts of data being generated at different touchpoints.

One of the key factors is the improvement in real-time visibility of the processes involved in supply chain planning. This increased visibility helps key stakeholders assess cost and customer service tradeoffs.

The initial stages include data scientists understanding what factor (inventory, cash flow, waste, etc.) to focus on and building a predictive model that optimizes the output of the entire supply chain. The robust model is then deployed by data engineers, keeping in mind the scalability and performance concerns. 

From an industrial standpoint, data analytics are operationalized in practice, as the supply chain models are refined over time. This is majorly done by correlating the performance of these models in production with the actual business value offered.

Current challenges in the supply chain industry

The pandemic was an eye-opener for businesses, exposing supply chains’ different complexities and challenges. Despite supply chain data analytics’s boon, let’s understand some of the palpable roadblocks that supply chains can face.

Lack of data granularity

With the pandemic forcing businesses to become increasingly global, supply chains have become bulkier. This has only meant more touchpoints with an added hint of complexity.

It is high time that companies understand the need for more agile predictive and prescriptive analysis throughout the supply chain. Be it trade wars, strikes, market fluctuations, civil unrest, or natural mishaps, such analysis dampens the blow and makes the entity future-ready.

Slow digital evolution

Despite the need for data and connectivity ushered in by the rage of the pandemic, some companies are still struggling to catch up. Since they go back and forth with hybrid or manual plus digital data collection models, information gets stuck in functional silos.

For smoother decision-making aided by analytics, data should flow seamlessly across the supply chain. Businesses are expected to adopt newer, cutting-edge technologies to make this a palpable reality.

Volatility of demand

Since demand volatility is usually a result of ever-changing technological advancements, supply fluctuations, increased customer choices, and global competition, businesses are expected to leverage the digital twin tech stack, artificial intelligence, and automation to overcome it.

Even though managing volatile demand is a tall task, it can be worth the effort to create competitive differentiation for the company.

Insufficient actionable data and insights

One of the functional challenges arises when the ERP systems housing tons of data cannot render adequate actionable insights into future trends and oncoming roadblocks.

Drawing parallels, it has also been observed that supply chain analytics software analyzes past data but seems rather ineffective in peeking into future supply chain hurdles, which is where modern prescriptive analytics comes into the picture.

How can supply chain analytics help with the above challenges?

Before diving deeper, it is imperative to understand that the fundamental parts of supply chain management are planning, sourcing, manufacturing, delivery, and returns. Understandably, it is only after the first strategic planning phase that the process focuses on execution to ensure that products and services reach consumers as soon as possible.

This is where firms can leverage analytics to accelerate delivery time and make well-informed decisions. Furthermore, it is common knowledge that reporting dashboards help estimate risks and optimize inventory management, all of which satisfy consumer expectations. 

Analytics software can detect and identify anomaly patterns to quantify their impact on deliveries, chargebacks, and returns.

When it comes to supply chain managers, they can meticulously integrate all moving parts to fulfill dynamic volumes with precise projections. Supply chain management is further augmented and enhanced by analytics in the inventory management wing. Data-backed systems establish a balance and control costs.

These smart-systems send out triggered alerts for SKUs and lead time for specific providers and determine storage planning on the sales trends. All of the abovementioned measures add to the supply chain functioning smoothly, directly impacting the enterprises’ bottom line. 

In short, deploying the right analytics tools enables tracking order accuracy rates, identifying ongoing trends, and meeting customer need.

Types of supply chain analytics

From a company’s perspective, there are four broad supply chain analytics classes worth investing in for more efficient operations. 

Descriptive analytics

In essence, descriptive analytics identifies patterns from past data. This can compare similar data from varying periods in time to correlate patterns and pull out hypotheses for the cause of change. Such data can be derived from internal execution software or external pipelines, as long they offer a bird’s eye view of distributors, sales channels, suppliers, and end users.

Predictive analytics

As the name suggests, predictive analytics aids in the forecasting of potential supply chain disruptions and other outcomes that might distort the flow of products/services. Such proactive analysis helps companies prepare and react according to a pre-planned strategy.

Stakeholders are offered a sneak peek into the future based on data-driven insights, and they can then assess the business impact of all possible scenarios.

Prescriptive analytics

Combining the best of both descriptive and predictive analytics, Prescriptive analytics comes up with current actions for business leaders to take that can put the company in a position to achieve its goals. 

By evaluating internal information as well as that of partners, prescriptive analytics helps companies handle major supply chain disruptions. 

The increased complexity means that this kind of analysis requires robust software that cans quickly process and interpret a lot of data. 

For instance, prescriptive analytics can notice the consistency in late orders, plummeting economic health of the region, as well as the diminished capacity of the supplier and suggest the manufacturer look for alternatives in case the supplier goes under.

Cognitive analytics

As the name suggests, Cognitive analytics aim to leverage human-like thinking, behavior, and factor in context to help companies answer twisted business problems. Therefore, it heavily relies on machine learning and deep learning for self-corrective mechanisms over time. 

This intuitive approach reduces the manual labor in producing routine reports and analyses. Such an end-to-end AI-enabled system can process large amounts of data and spit out highly precise suggestions. 

Advantages of supply chain analytics 

Supply chain analytics helps an organization make smarter, leaner, and more business-friendly decisions. Let’s understand its significance and advantages.

Planned approach and risk mitigation

The use of varied datasets results in a continuous integrated planning approach. Moreover, real-time visibility enhances operational efficiency and churns clearer actionable insights. 

The data analytics deployed in supply chains can readily identify potential risks via thorough identification of patterns/trends in the supply chain.

Increased accuracy & leaner supply chain

Online supply chain analyst courses can help you better estimate future demand by studying client data. You can then assist an organization in determining which items can be reduced based on their decreasing profitability or ascertaining consumer wants after the initial order.

Moreover, analytics in the supply chain are credible decision drivers as they monitor warehouses, partner reactions, and consumer needs.

Future-ready operations

With the influx of AI-related software, the constraints of evaluating natural language data that could not previously be processed can now be studied in real-time. Smart systems can easily read, interpret, correlate and derive patterns from mutually exclusive resources, silos, systems, and platforms.

Advanced Analytics is another exciting avenue in the supply chain, with the capability to deal with structured and unstructured data. They can also draw patterns among different sources to shoot triggered alerts to minimize risks at little cost and less sustainability impact.

Impact of supply chain analytics and a look to the future

There is no doubt that analytics has an even bigger role in the supply chain domain as the future arrives. The evolution of digital infrastructure, analytics models, and the ability to integrate data from disparate application silos will all be key factors.

The advent of AI and IoT will equip enterprises to draw real-time insights from dynamically changing data. Machine learning and Deep learning will imbibe a more intuitive approach to offering predictive insights that can be easily embedded into workflows. 

Some other tech stacks that are liable to play a significant role are:

  • Blockchain: is the key to increased traceability throughout the supply chain. Businesses will look to deploy smart contracts to automate, control, and execute transactions.
  • Graph analytics: will be the driving factor behind a wide majority of corporate applications and will aid the analysis of the links between various organizations in the supply chain.
  • Hyper-Automation: is expected to leverage process mining analytics to identify candidates for automation, generate the automation procedures and manage them.

Skills needed for a supply chain analytics job

Some preliminary skills needed for a supply chain analytics position include inventory management, mathematical skills, IT development, Interpersonal skills, data analysis, and forecasting.

Consumer Product experience and Project Management are skills that are expected from decently experienced professionals.

When it comes to the technical stack, tools like Structured Query Language (SQL), Microsoft Excel, analytical models, and mathematical analysis come in handy. They are a major plus in any analyst’s arsenal.

Moreover, some of the high-level skills can be the ability to implement supply chain design for firms, accomplish effective logistical operations via streamlined strategizing, execution, & management, and, most importantly, leverage competitive advantage by adopting a strategic approach to integrated supply chains.

There is no stopping the speed at which businesses are growing in the modern era. And with the supply chains becoming longer and more complex by the day, companies are turning toward supply chain analytics to draw quicker insights to fuel well-informed decisions. 

Final thoughts

You can easily realize your supply chain analytics dream with Hero Vired PG Certificate Program in Business Analytics and Data Science, offered in partnership with edX and from Georgia Tech.

The 70+ live sessions with global and Indian faculty will thoroughly expose you to the applications and benefits of predictive modeling and exploratory data analysis in operations and supply chain.

Such supply chain analytics courses hosting an industry-validated curriculum and industry projects & case studies will integrate skills seamlessly and yield in-depth insights into analytical problems.

This supply chain analytics certification is helpful for early & mid-career professionals who are willing to master the art of deriving insights from data and conveying it to key stakeholders. 

The program uses Python and R to explore detailed data modeling and analytics concepts and equips you with the ability to make data-driven business decisions.

Learn in-demand skills and get guaranteed job oportunities

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