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In order to produce the best results possible for an organization, every asset at your disposal needs to be utilized effectively. A cohesive approach to internal and external data analysis ensures that you’re given a rounded and accurate picture of what’s best for business.
In order to do this effectively an organization can turn to supply chain analytics. But what exactly is this process? In this guide, we’ll discuss exactly that, while also assessing the key benefits, common types, why an organization should turn to this system, and how to overcome some of the biggest challenges associated with supply chain analytics.
What is supply chain analytics?
Supply chain analytics is the process of combining and analyzing data from several different sources to gain valuable insight into every aspect of a business’ supply chain. The ultimate aim is to enhance optimization at every level, with a focus on providing insights that improve the delivery and management of your services..
Supply chain analytics can be predictive, using analytical tools to try and pre-empt fluctuations to different parts of your SCM systems. Analytics of this nature can also be prescriptive, helping to identify a hidden risk within the supply chain itself and attempt to overcome it.
How does supply chain analytics work?
Supply chain analytics can be visualized in the following way:
- Step 1: Operational sources
Things begin when data is sourced from internal systems. such as procurement, CRM, ERP, Financial systems. It’s also possible to add data from external sources such as Corporate registries, watchlists and even news articles.
- Step 2: Data repository
This data is extracted, transformed and connected so that it can be stored in a repository like a data warehouse or data lake. This allows a business to have an overview and easy access to their data within each area of their supply chain network.
- Step 3: Analytics
The core analysis happens next. This could involve any number of tools, which make it easier to manage and control different elements of your supply chain. It’s at this stage that aspects like predictive analytics, scoring and alerting, data visualization, machine learning, and augmented analytics occur.
- Step 4: Output
With data now analyzed, the end result should be actionable insights which allow an enterprise to make proactive changes to the handling and management of many aspects of their supply chain.
This life cycle ensures that all areas of your supply chain are analyzed to provide the best insights into what you could be doing to better optimize your procedures at every level of the chain.
What are the key components of supply chain analytics?
Supply chain analytics relies on a variety of components in order to provide the best results for an organizaton. However, while each individual system will be slightly different, there are a handful of core features which you’ll find in practically every analytics platform.
Detailed analysis
At its core, supply chain analytics involves the detailed analysis of existing data to provide accurate and useful strategic business decisions.
Digital modeling
This process makes it possible for analysts to effectively clone and then experiment with a digital version of an existing supply chain. This allows for testing to be carried out, without the risk of investing in the wrong areas or making changes to systems that don’t need to be touched.
Internal and external data integration
The pulling and analysis of internal data is vital to any successful supply chain analytics platform. The importance of external data also shouldn’t be overlooked. Public datasets can be integrated into a system to better help pre-empt and predict fluctuations in customer behavior, such as in the case of banking.
Machine learning and artificial intelligence (AI)
These innovative resources are becoming an increasingly powerful weapon in the arsenal of business. This kind of technology is able to learn, forecast, predict, optimize, automate, and quickly analyze large sets of data. AI makes complex analytical decisions in an instant, saving time and reducing the chance of human error.
Collaborative access
Within an organization, supply chain analytics should be widely accessible and collaborative. While data should remain hidden to third parties, that’s not the case for internal users. A collaborative approach reduces barriers within an organization, which in turn heightens efficiency and reduces time lost to procedural navigation. This is particularly useful in an industry like telecoms, where third party intelligence helps to provide a 360 view of customers.
Data visualization
Data is best utilized when provided in a digestible manner. A visual format which allows analysts to quickly process and understand what the numbers are telling them is the optimal way to ensure this is the case.
Enhanced security
All supply chain analytics systems contain sensitive operational data. As such, industry-standard procedures need to be adhered to in order to ensure this information is kept safe at every touchpoint. That includes factors like encryption, centralized data governance, incident alerting and monitoring, firewalls, and disaster recovery systems. Risk and compliance should also be factored in, as well as fraud or error detection during any procurement stages.
Every supply chain analytics system should cover these key features in order to provide the best actionable insights for an organization.
What are the benefits of supply chain analytics?
Organizations are increasingly turning to supply chain analytics in order to ensure they’re getting the most out of their internal processes. There are a series of reasons why this form of analysis is important for businesses, with several benefits to employing this service:
Supply chain planning optimization
By having mastery over every stage of the process, it’s possible for a business to better plan and manage their supply chain as a whole. That extends to predicting the needs and future demands of customers, ensuring compliance procedures are followed at every stage, and using contextual modeling to reduce the chance of false positives.
Supply chain risk management
With supply chain analytics, organizations can detect fraud, corruption, conflicts and other integrity risks within their supplier base. A holistic view makes it possible to monitor and watch for these factors at every stage of the process.
Better clarity over each aspect of a business
Fully comprehending the supply chain provides an organization with a detailed overview of several key areas of their business. Sourcing, logistics, compliance, and aftermarket are facets of a company which can be understood in greater detail thanks to supply chain analytics.
Resilience against market disruptions
If a barrier or stumbling block rears up in your supply chain, it would be significantly harder to resolve this without the use of a strong analytics process. Prescriptive analytics can be used to find the cause of the issue. This can be paired with predictive analytics to pre-empt and predict the likelihood of a similar scenario again in the future, as well as advanced analytics to have AI triage the area which needs support and have it work on that independent of the rest of your supply chain.
Trend comprehension and management
Patterns of consumer behavior or market fluctuation can have a huge impact on a company’s bottom line. Analytics can identify seasonal trends when issues in these departments are more likely to occur, such as quiet periods in finance and telecoms. This helps an organization to predict and pre-empt potential shifts in the market, allowing time to proactively plan for any potential loss of revenue.
Heightened flexibility
Becoming stagnant and unable to adapt to the changing landscape of a rapidly evolving industry can become an issue for some enterprises. Supply chain analytics makes it easier to pivot and shift an approach, making it easier to navigate transitional periods with the help of accurate insight and reliable business intelligence.
Cost reduction
Saving money by streamlining procedures, optimizing efficiency, and lowering overheads costs can be achieved by supply chain analytics. By utilizing this process, and the benefits which it brings, organizations are able to save money at every stage of the pipeline.
Gain control of your data
What are the challenges of supply chain analytics?
Just as with any procedural system, supply chain analytics can sometimes pose challenges to organizations looking to implement them. Here are some of the most common, as well as ways to circumvent them becoming an issue for an enterprise:
Integrating data from multiple sources
Challenge:
Companies have dozens, if not hundreds of different data sources across functional areas, departments and regions. What’s more, data is typically stored or formatted differently, some of it might be coming from legacy systems or be poor quality. This leads to serious challenges in connecting all this data in a meaningful way and often is a barrier to supply chain analytics.
Solution:
Advanced software now exists which is capable of individually processing, standardizing and connecting data. This makes the integration quicker and easier, with all data “speaking the same language”.
Integrating outdated systems
Challenge:
Technology moves on quickly. Tools which were used to store data as recently as 10 years ago could now seem archaic by modern standards. This can make it tougher to integrate with supply chain analytics.
Solution:
Most modern integration tools take this into account. They’re built with the integration of outdated systems into account, allowing data to be processed and converted in a way that makes it easily accessible for all.
Data quality issues
Challenge:
With distributed and disparate data generated by internal and external applications and sources, data quality can be easily compromised. Without the necessary standards, processes, and technologies in place, organizations struggle to ensure quality is maintained over time. This can have a big impact when utilizing this information as part of any analytics campaign.
Solution:
In instances like this, it is very important to select tools that will help match data from different sources, formats regardless of its quality, into a trusted view, forming your data foundation which is then fit for analytics.
Supply chain analytics examples
In order to better understand what kind of an impact supply chain analytics could have on a business, it’s best to visualize them in action. Consider the following examples when thinking about what a supply chain analytics platform could provide:
Creating a strategy is most effectively done when you have already tested it on a simulation. Predictive analytics allows for this, where a company can trial a series of optimized-based scenarios in several different guises to determine the likely outcome of each. This can be used to test the supply chain at every level, determining what actions will or won’t have a positive impact.
Optimizing capacity ensures an organization matches resource management to customer uptake. Prescriptive analytics makes it possible to do this via the help of a detailed mathematical model. This model will assess where and when demand is at its greatest, allowing a business to operate either a lead strategy or lag strategy, or a combination of both.
This system crunches the numbers to determine what scenario would be most profitable. Modeling can again be used here to determine forecasts for factors like sales performance, demand and customer procurement. This allows an organization to plan financial factors in accordance with what works best for their net profitability.
Sales promotions are a great way to drive demand. However, if they don’t correlate with procurement and upscaling capabilities, organizations can find themselves failing to meet demand. Supply chain analytics allows for the testing of sudden changes to the pipeline, helping businesses to understand what a shift in uptake would mean across all levels of the supply chain.
Proactive measuring and monitoring of the supply chain makes it easier to quickly identify breaches in compliance, as well as potential fraud. By analyzing data taken from different parts of the supply chain, analytics can ensure that all products meet the necessary level of adherence to industry standards.
Supply chain analytics FAQs
What is the difference between supply chain analytics and management? 
While both essential components of a modern approach to supply chain operations, these two all-important factors are different. Think of it this way:
Supply chain analytics is the process of modeling, testing, and analyzing different areas of the supply chain to gain valuable insights
Supply chain management is the role of one, or a team of overseers to implement changes discovered or suggested by the analytics process
How can you assess the performance of supply chain analytics?
Judging how well your supply chain analytics is performing relies heavily on defining the parameters by which you want to assess it. Creating an ultimate end-goal should be your first decision, with every step taken thereafter an attempt to reach that end.
Examples of common goals include things like:
Identifying risky suppliers
Reducing reputational and social governance risk
Ensuring compliance to regulatory practices
Protecting from fraud losses including overlap from insiders
Spotting procurement fraud
Accurately forecasting demand
Identifying gaps in the market
Enhancing inventory management
Actively reducing costs
If you want to determine the success of an analytics campaign, it’s important that whatever you choose to pursue has a measurable outcome.
What is the future of supply chain analytics?
While the future is always something of an unknown, two key factors which are highly likely to influence ongoing supply chain analytics are the emerging presence of AI and the need for good, accurate data.
The former has already cemented itself as a key driver of analytic evolution, with machine learning and NLP (natural language processing) models making the assessment, organization, and utilization of data quicker and more streamlined than ever before.
In order for that to be executed as efficiently as possible, the need for highly accurate data is paramount. Organizations are placing continued importance on ensuring that the information these AI systems are processing is trustworthy and reliable.
Useful links
We’ve discussed a lot in this guide, but there might still be more you want to discover about supply chain analytics. Browse these handy sources to learn more.