Supply Chain Analytics: What It Is, Why It Matters, and More
The ability of a firm to deliver a pleasant customer experience is directly impacted by the supply chain, which also accounts for many of the costs that affect overall profitability.
The supply chain is a network of suppliers, businesses, and end users that handles everything from locating raw materials to delivering goods to customers.
Many companies have intensified their supply chain management (SCM) efforts due to the supply chain's immense importance to their operations.
In the lengthy process from a supplier of raw materials to the end user, they are searching for any opportunity to make procedures quicker, less expensive, and more straightforward. This is particularly important because supply chains have only gotten more complicated over time.
Businesses now collaborate with an increasing number of foreign partners and are under increasing pressure to ship their goods as soon as feasible.
The numerous actions, individuals, and organisations that makeup supply chains generate vast information.
Supply chain analytics can transform this overwhelming volume of data into easily understandable dashboards, reports, and visualisations that impact essential choices and improve outcomes.
Easy access to these analytics has become essential in a market that keeps getting more competitive.
What is Supply Chain Analytics?
Supply chain analytics is the study of data that businesses extract from various applications related to their supply chains, such as supply chain execution systems for order management, inventory management, warehouse management, and fulfilment (including shipping).
Each step in a supply chain impacts the one behind it, and any problems at any level could ultimately undermine the company's ability to satisfy customer expectations.
Each of the aforementioned software programs may have its reporting features that provide information on that particular stage of the supply chain, such as anticipated lead times for suppliers, current safety stock levels in the warehouse, or orders filled per hour, as examples.
But when all of these systems are connected, typically through an Enterprise Resource Planning (ERP) system, supply chain analytics are at their most effective.
Then, the ERP or a different application can show and illustrate data from your global supply chain through dashboards or reports.
Employees are given a thorough understanding of this logistical network and the ability to comprehend the upstream and downstream repercussions of a particular interruption.
They can then act swiftly to minimise the problem to the greatest extent possible. Some systems, for instance, can instantly evaluate the data and inform users of potential issues before they escalate.
Roles of Supply Chain Analytics
Companies can collect, evaluate, and take action on the data created by their supply chains thanks to supply chain analytics. It enables them to make long-term strategic changes that will provide the company with a competitive advantage and immediate modifications.
Managing this information manually or through spreadsheets is nearly impossible because supply networks frequently span the world and can contain hundreds of distinct businesses. It is, at the very least, incredibly ineffective.
Demand planning (predicting client orders using historical data and other criteria), sales and operations planning (producing or purchasing the commodities a business needs to meet expected demand), and inventory management are a few examples of supply chain analytics (tracking sell-through of items and which SKUs it needs to replenish).
These actions can improve corporate operations' overall efficiency, resulting in significant cost savings. For example, more precise demand planning allows you to avoid high procurement costs, stockouts, and surplus inventory (which can turn into obsolete inventory).
In this way, your company manages costs while providing a top-notch customer experience that will set you apart from the competitors.
The most successful companies operating today continue to emphasise supply chain analytics. More than ever, businesses are paying close attention to these figures and optimising each connection in this network using a variety of analytics tools.
Types of Supply Chain Analytics
To create more effective operations that could save time and money, firms should immediately consider four main types of supply chain analytics. Here is a quick summary of each:
Analyses that are descriptive and focus on the past. They can spot patterns in past data. Both internal supply chain execution software and external systems that provide insight across suppliers, distributors, different sales channels, and customers may provide this data. Analytics can examine the same data from many periods to find patterns and predict possible reasons for change.
Daily monitoring of a descriptive analytics dashboard by a manufacturer may reveal that half of the distributor deliveries are behind schedule. The company's executives can look into the issue further and discover that the truck delay was caused by a snowstorm that hit the area where that particular distributor group is situated.
Predictive analytics does precisely what it says on the tin: they assist businesses in foreseeing future outcomes, including supply chain disruptions and other events, and the effects of such results on their operations.
Leaders can be proactive rather than reactive by making them think about these potential outcomes before they occur.
They can plan and respond appropriately, for as, by developing a strategy for an anticipated rise or reduction in demand.
The same manufacturer may analyse the most recent Federal Reserve economic forecasts and predict that sales will decline by 10–20% in the upcoming quarter.
In light of this, it places smaller orders with its suppliers for raw materials and reduces the number of part-time employees' hours for the following month.
Prescriptive analytics combines descriptive and predictive analytics findings to recommend a business's actions to achieve its targeted goals.
By analysing both their data and that of partners, this analytics may assist businesses in solving issues and preventing significant supply chain interruptions.
Prescriptive analytics demand more robust software that can quickly process and analyse large amounts of data since they are more sophisticated.
Prescriptive analytics may inform the manufacturer that one of its essential Southeast Asian suppliers faces a one-year risk of going out of business.
This conclusion is supported by consistently late orders, limited capacity, and deteriorating regional economic conditions.
The manufacturer might respond by asking to meet with the supplier's executives to learn whether they are having financial difficulties and how it might be able to assist. If a solution cannot be found, the company should start looking for a new supplier before it is too late.
Cognitive analytics, which mimics human thought and behaviour, can assist corporations in resolving challenging, complex issues.
When analysing data, these analytics can take context into account. To accomplish this, cognitive analytics depends on artificial intelligence (AI), particularly machine learning and deep learning. These techniques enable cognitive analytics to become wiser over time.
This gives people outside the data science team the ability to extract and comprehend results, which can significantly minimise the labour required of staff to develop these reports and analyses.
The firm can automate much of the effort involved in demand planning with its AI-enabled software.
All accessible data, along with internal and external variables, could be processed by the system to generate detailed, thorough recommendations for how much of each product should be produced in the future quarter to meet demand.
This lowers the extra costs associated with producing more inventory than necessary or losing sales due to the inability to fulfil orders.
Importance of Supply Chain Analytics
- Organisations in various sectors use supply chain analytics to make more intelligent, quicker, and more informed decisions about their daily operations. In this sense, it offers the businesses who employ it genuine and long-lasting value.
- These reports and dashboards assist businesses in better planning, risk identification, inventory management, and meeting the high expectations of their consumers. By observing, for instance, that a particular transportation provider has consistently delivered products late over the past month, analytics tools can identify dangers. It can detect this trend and predict the possibility of more delays. The technology may also calculate the cost of chargebacks and refunds and the quantity of probable late deliveries.
- With more precise estimates made possible by analytics, planning may be enhanced, allowing you to put all the operational components in place to handle the anticipated volume. A store may put larger purchase orders with suppliers and hire additional workers at its warehouse if sales increase steadily. The holidays are drawing near to prepare for an increase in orders during the key holiday period. The retailer can identify alternate solutions while it still has time if any suppliers cannot fulfil these higher orders.
- Both are having too much or too little inventory is problematic for many firms. Running out of stock results in lost sales, while having excessive inventory results in higher than necessary inventory-carrying expenses. To maintain prices as low as possible without experiencing stockouts, analytics help achieve the ideal inventory balance. By the average lead time for that supplier, the system may send out an alert for SKUs that are running low. The operations staff can utilise sales patterns to determine which products need more warehouse space and which can be phased out or kept in small amounts.
- All of these indicators and figures work together to assist firms in exceeding client expectations. Any flaw in the supply chain has the potential to degrade the consumer experience and tempt them to shop at a rival. Companies can watch statistics like on-time delivery or order accuracy rates directly tied to the customer experience to spot and rectify any troubling tendencies.
Supply Chain Analytics: Advantages
Accurate supply chain analytics has significant and long-lasting advantages.
- By identifying patterns and providing other important insights, they can assist at every stage of the supply chain. They can identify areas for process improvement and draw attention to issues that operations directors might have yet to anticipate. Given that supply, chain interruptions can significantly impact the bottom line, the capacity to identify current supply chain risks and anticipate impending ones may be the most advantageous feature of analytics.
- Additionally, having access to real-time information enables businesses to understand their profitability better, prevent stockouts, cut down on late shipments, and adjust to changing client preferences. With the aid of this information, organisations may better deploy their resources, which reduces costs. Many decisions are made based on primary historical facts and speculation in the absence of this information.
- Supply chain analytics is a crucial step toward the "data-driven" strategy many businesses aspire to. Simply put, firm executives can make better choices with access to comprehensive supply chain data and analyses.
Supply Chain Analytics: Challenges
- High entrance barriers are one of the main problems with supply chain analytics. Purchasing the technology could be a sizable—yet worthwhile—investment for people who do not currently have the systems to obtain these insights. For gathering and reviewing this crucial data, relying on spreadsheets, emails, and point solutions isn't sufficient. Systems for supply chain management are required by businesses so that products may be tracked from raw ingredients to final delivery. They might also need an analytics system that can transform reams of data into helpful reports and visuals to capitalise on this data fully.
- A company must also have reliable procedures to gather all the required data. A central database should house data from all points throughout the supply chain, which necessitates trustworthy integrations. An organisation can only comprehend its supply chain's status and outlook when data from all pertinent systems flows smoothly.
- The potential need for specialised personnel to develop and comprehend specific analytics is another difficulty. Although software can significantly simplify analytics for supply chain workers, most of whom lack a background in data science, it is still important to consider whether you have the necessary personnel to support this endeavour. The only thing that might be required is training on the analytics solution. Larger businesses that wish to take advantage of the newest developing technology to acquire more profound, more sophisticated insights into their supply chains tend to be more concerned about this.
Supply Chain Analytics: Features
- Connected: Data is the foundation of every supply chain analytics project, so the solution must have access to all relevant data sources. The ERP and any other business systems are the starting point for these data linkages, which also cover any other information-gathering tools your company may employ, such as Internet of Things (IoT) devices.
- Collaboration: Companies should keep sight of the importance of their supply chain partners to their overall performance. To improve products or processes, they should work together with their suppliers and, if possible, customers. Cloud technology allows these parties to exchange information and ideas more efficiently than ever.
- Cyber aware: As companies add more software and connected devices, the risk of cyberattacks has grown, as have the chances that an attack will be successful. Companies need to be aware of this and rely on internal cybersecurity resources or outside specialists that can help them provide the appropriate protections for any systems that plug into their analytics.
- Cognitively enabled: In the years to come, supply chain analytics will undoubtedly play a more significant part in cognitive analytics, which, as said earlier, use AI to form its judgments. Companies can immediately comprehend the effects of disruption and prioritise their responses with the aid of cognitively aided analytics. A solution like this will only improve over time, allowing more automation.
- Comprehensive: One-off insights or reports are narrower. To fully utilise the capabilities of these instruments, analytics software must offer deep and extended observations. The solution must be functionally robust and scalable to deliver quick results even as data volumes grow.