An Overview of Supply Chain Analytics
Using traditional supply chain execution strategies is getting increasingly onerous due to a jumble of various software systems, price challenges, and ever-increasing consumer demands.
Recent economic implications include the outbreak of a pandemic, increasing fuel costs, the global financial crisis, supplier networks that have collapsed or relocated offshore, and greater competition from low-cost outsourcers.
All of these difficulties have the potential to squander your supply chain. This is where data analytics can be your savior.
The science of evaluating raw data to develop insights about information is known as data analytics. It is used in numerous sectors to help businesses and organizations make better business decisions and research to endorse (or negate) current models or ideas.
Advances in analytic tools and techniques make it feasible to analyze supply chain data in quest of savings and efficiency. Due to its significance and scope, a supply chain is a good place where companies can leverage data analytics.
This brings us to the question of what exactly is supply chain analytics?
What is Supply Chain Analytics?
While most supply chain networks produce vast volumes of raw data utilized in decision-making and forecasting, it is mainly fragmented. It is challenging to synthesize and analyze all of the information since several data sources and formats are available.
Supply chain analytics provides the ability to find trends, develop insights, and integrate data from multiple sources. Decision-making in all work processes gets easier, faster, and adds greater value due to the availability of consolidated, transparent data.
Supply Chain Ecosystem
Supply chain analytics enables the analysis of data drawn from various supply chain programs, such as supply chain planning systems, inventory management systems, warehouse management systems, and transportation management systems.
A supply chain has a domino effect: each node in the network impacts the one after it. Any faults at any point might influence the potential to satisfy consumer expectations.
Each of the above-mentioned pieces of software has its reporting capabilities that give insight on that particular phase in the supply chain, such as expected lead times, current inventory levels in the warehouse, or orders completed per hour.
However, supply chain analytics are most effective when all of these systems are linked together and generate consolidated information.
This provides personnel with a complete perspective of the logistics network and allows them to comprehend the upstream and downstream consequences of a single interruption. Businesses can then take measures in a manner that minimizes the problem to the greatest extent feasible.
Types of Supply Chain Analytics
There are four main categories of supply chain analytics that companies can leverage to develop more effective processes that are efficient and effective. The short descriptions of each are given below:
Descriptive analytics examines what has happened over the years. They are capable of detecting trends in historical data. This data might originate from internal supply chain management systems and external systems that provide insight across suppliers, distributors, multiple sales channels, and consumers.
Analytics can uncover trends and postulate probable reasons for change by comparing the same data from various periods. On a regular basis, a producer may monitor a descriptive analytics dashboard and find an increase in the supplier’s lead time. Managers may then look into the issue further and discover the reasons.
As the name implies, it assists businesses in predicting what might happen and the impact of various situations, such as possible supply chain bottlenecks. Managers can be proactive rather than responsive by pushing them to evaluate these prospective circumstances before they occur. For instance, they can plan a strategy for a predicted increase or decrease in demand and respond appropriately.
Prescriptive analytics uses the findings of descriptive and predictive analytics to recommend what measures a business should take to achieve its objectives. This sort of analytics might help businesses to solve issues and avoid supply chain disruptions by analyzing both their own data and that of their supply chain partners.
Because prescriptive analytics is increasingly complicated, they need more powerful software capable of rapidly processing and interpreting large amounts of data.
Cognitive analytics attempts to mimic human thought and behavior, and they can assist companies in answering challenging, complex problems. When drawing conclusions, these analytics can comprehend things like context. Cognitive analytics does this by utilizing artificial intelligence (AI), which allows it to be better over time.
This can significantly help to cut down the time and effort required by personnel to generate these reports and analyses while also empowering employees outside of the data analytics team to pull and interpret the results.
For instance, a company may automate much of the effort that goes into the demand forecast and planning by employing artificial intelligence. The AI system can process the data from all the sources and analytics to generate accurate, comprehensive guidelines for the stock needed to meet demand requirements.
This eliminates the extra costs associated with producing more inventory than is required or losing revenues due to a failure to satisfy demand.
The Value and Importance of Supply Chain Analytics in the Supply Chain Ecosystem
Supply chain analytics enables businesses to better organize their business operations and streamline their supply chains. In this way, it has the capacity to provide real and long-term value for those companies that implement it.
The reports and analysis generated by supply chain analytics can help businesses to effectively satisfy customer's requirements, identify and comprehend the risks associated with their business, as well as by improving their business planning, demand planning, and inventory management.
One of the primary uses of analytics is to monitor risks, for instance, flagging instances in which a particular shipping company is delivering shipments late over several months. It can not only detect this pattern but also predict the possibility of more delays.
The solution also provides the information necessary to quantify the business implications of such delays, such as the cost of returns.
Analytics provides an efficient way to improve planning because it gives you better forecasts. This enables businesses to schedule the supplies and manufacturing processes to meet the projected customer's demands.
Placing larger inventory levels and adding more contractors to the warehouse is common practice during the holiday season when sales begin to rise steadily. Analytics enable manufacturers to sign up more suppliers to fulfill the increased demand.
Businesses use these statistics and metrics to fulfill customer expectations. Even a little interruption in the supply chain may lead to consumer dissatisfaction and may result in losing prospective customers.
Businesses might also use analytics to analyze metrics such as safety stock level and order accuracy rate, as well as analytics to assess general customer experience, to discover and deal with any problematic patterns.
Supply chains operate in a very dynamic and diverse environment, each link in the supply chain with its own set of challenges. Most supply chains are complicated since the “chain” is made up of various functions inside a business. A slight change in the market may have far-reaching consequences across this intricate ecosystem.
• Supply chain analytics can improve decision-making across all the functions in an organization. Let’s takes a quick look at how supply chain analytics can make improvements across all the functional units.
• Finance teams may use supply chain analytics to precisely project demand, evaluate and manage across transfers, comply with tax rules, and evaluate suppliers for the ESG (Economic, Social, and Growth) sustainability.
• Supply chain analytics help sales and operations planning teams to monitor real-time supply chain variations, identify potential suppliers, anticipate risk management, and forecast and analyze the trends in the cost of supplies. Based on prior on-site user activity, brick and mortar sites may estimate supply, improve product placement, calculate the price, and even forecast future purchase patterns.
• Finally, marketing departments that employ predictive analytics and supplement it with other data sources such as purchasing behaviors and search analysis may effectively target people across channels to drive purchases while ensuring higher ROI.
• HR teams that utilize advanced analytics can optimize human resources and make better decisions about the recruitment process, as well as examine possible upskilling possibilities within the supply chain and retool existing staff for new applications.
• Manufacturing teams powered with supply chain analytics can enhance production processes using real-time quality control and error reporting, and with the introduction of AI, may even prompt the automated production of components to replace damaged components soon after faults are spotted.
• Teams that utilize supply chain analytics with data acquired by smart technologies may be able to improve their decision-making across the supply chain. These advancements include streamlining repair and replacement requests, skill development of operators, and building a safer overall design for all operations and processes in the supply chain ecosystem.
• Supply chain analytics allow logistics teams to swiftly discover bottlenecks and enhance performance throughout their warehouse, transportation, and distribution networks. These improvements may result in shorter lead time, faster delivery, and cost savings by lowering fuel usage through targeted shipping options.
In a recent survey conducted by Gartner, supply chain executives highlighted the importance of advanced supply chain analytics in accelerating business performance. They have also acknowledged that advanced analytics is the second most important emerging technology.
Advantages of Supply Chain Analytics
While we have discussed above that how supply chain analytics help across all the business divisions by enabling them to gain insights into data to make informed decisions. Now we’ll look deeper into these benefits that how they help in a larger context by activating supply chain level outcomes.
Supply chain analytics improves transparency across the supply chain
A transparent supply chain highly depends on information about materials and goods as they progress through the supply chain network. Supply chain transparency is described as a company’s capacity to communicate with stakeholders effectively in order to get visibility and traceability across supply chain processes.
The information gathered from such collaboration must be precise, timely, reliable, and comprehensive, as well as arranged in a way that supply chain participants can understand. Firms have traditionally depended on a variety of data sources and analyses to optimize their supply chain processes.
Supply chain analytics has lately been used to improve transparency across the supply chain.
One of the main challenges in the upstream supply chain is how to deal with suppliers when there are problems regarding the sourcing processes. Disruptions on the supply side of materials will cause the firm’s manufacturing schedules to be delayed or even halted, lost opportunities sales, reduced revenues, profit decline, and a bad reputation.
Predictive analytics (e.g., demand forecasts) may be used to identify patterns and trends as well as anticipate breakdowns that may impact suppliers and, consequently, production processes.
Supply chain managers can acquire transparency into all critical areas of possible disruptions and devise strategies to respond to these disruptions.
Another advantage of supply chain analytics is that it provides transparency into supplier performance, allowing organizations to make informed decisions when it comes to select a supplier.
For example, a spend analysis examines a target firm’s purchase spending, evaluates supplier performance, and selects the supplier with the highest performance.
In addition, analytics may combine supplier performance information with other essential data (such as current and upcoming market trends) to give insights about supplier responsiveness to increased demands and the opportunity for ongoing improvement.
Past and present supplier performance, together with future performance, creates a comprehensive view of each supplier, allowing focused firms to make better supplier selection decisions.
As a result, analytics capabilities in the sourcing process provide insight across the available supplier pool to make informed decisions and increase supply chain transparency.
Managers are concerned with the delivery process, i.e., end node of a supply chain, because it is crucial to reach targeted levels of customer satisfaction, revenue, and business performance.
As businesses expand globally, the product distribution procedure becomes more complicated and difficult than ever before. Shipping companies, warehouse operators, and carriers generate a large quantity of data that may reveal the real-time location, milage, delivery sites, on-time performance, and other information.
This data will be integrated, processed, examined, and displayed using Supply chain analytic tools to give information about fuel usage, fleet efficiency, route accuracy, staff, and environmental factors.
As a result, businesses gain transparency into their transportation and delivery activities, allowing them to make real-time decisions about route planning, maintenance schedule, driver selections, etc.
The supply chain analytic example of Nestle provides a roadmap of how to effectively use data analytics for increased visibility and transparency across all tiers of supply.
Nestle leverage AI to enable data-driven decisions in its operations which consequently provides increased transparency to meet the customer's demands in an effective and efficient manner.
Supply chain analytics as an enabler of supply chain agility
Supply chain agility is described as a business’s capacity to successfully produce and deliver a diverse variety of high-quality goods and services with shorter lead times and fluctuating volumes, all while offering increased customer value.
Supply chain analytics can improve supply chain agility by improving diagnostic information, detecting external variables, anticipating demand, reducing fluctuation in demand, and cycle durations.
Analytics is a critical enabler of informative decision-making powered by data and supply chain resilience, with the goal of reducing a ripple effect in supply networks or recovering quickly from supply chain disruptions.
Supply chain analytics can give your business fast and reliable information about your spending patterns to help you make strategic sourcing choices. It enables end-to-end real-time information exchange and surveillance of supply chain operations, which might lead to better supply chain choices and, as a result, increased supply chain agility.
Advanced analytics can provide your business with tools like probabilistic and predictive modeling, which may be used for supply chain risk management methodologies, including scenario creation, and performance analysis.
IBM is a great example of a company that is employing these sophisticated capabilities to enhance risk management and supply chain agility. Watson, IBM’s cognitive robotic system, tracks and assesses IBM’s supply chain to discover potential disruptions, assess potential repercussions, and suggest risk mitigation strategies.
It then sends notifications to computers and handheld devices, having already observed risk/reward trade-offs, risk management guidelines, and other factors.
Supply chain analytics enable supply chain innovation
Supply chain analytics is primarily intended to improve operational capabilities and reduce risk, but it can also act as a catalyst for innovation. In supply chain management, innovation is a complicated process encompassing the identification of new ways and techniques as well as the transformation of opportunities into innovative ideas.
Supply chain analytics is a comprehensive process that uses quantitative tools and methods to analyze past and current data. Advanced analytics gathers data, transforms it, and retains it. Further data is translated into useful information that promotes evidence-based decision-making.
This approach may reveal previously undisclosed facts, patterns, and knowledge, which may aid managers in planning, analyzing, and projecting, as well as making comparisons.
Such data may be used to identify problem areas and devise methods to minimize delivery time, mistake rates, and costs, as well as increase operational capabilities and efficacy.
The revealed problem areas boost brainstorming among supply chain professionals to come up with their solutions, which results in innovative ideas. Therefore, it would be justified to say that analytics boost innovative thinking.
Supply chain analytics includes the analysis of supply chain-related data in order to get relevant and valuable insights that may be used for innovation or as inputs for innovations.
The coffeehouse chain Starbucks offers an outstanding illustration of how analytics fuel innovation. Starbucks isn't only employing data analytics to enhance its shop operations, it also has innovated the way to secure all its equipment in stores.
Thanks to the analytical capabilities of IoT, the coffee company has started using an IoT-enabled guardian module to securely link all of its coffee machines to the cloud. The best way to separate a device from a network is to use a guardian module.
This keeps devices safe from attack, blocks data transfer except with approved cloud and device partners, and helps preserve the module itself and the equipment. This technology can be used in new equipment at Starbucks, as well as retrofitted into existing equipment.