When it comes to reading your supply chain analytics report, do you feel it would be nice to learn just a bit more about the meaning behind all those numbers? Perhaps you have a solid understanding of your historical progress and current operational state, but you cannot precisely gauge what opprortunities or risks are waiting around the corner.
The truth is that every link and participant in a supply chain generates a wealth of data about the past, present, and future of supply chain operations. However, not all organizations can see, capture, and transform that data into intelligence for decision-making.
In other words, they lack end-to-end supply chain analytics solutions.
What is supply chain analytics?
Supply chain analytics refers to a cohort of methods and technological means companies employ to draw data from connected applications like inventory management, fleet management, shipping, and fulfilment software to obtain summarized intelligence on current and projected supply chain performance.
Any supply chain is a composite entity with many moving parts, producing and requiring data to ensure effective operations at every leg. A mishap within one link in a supply chain can shake the entire chain, resulting in operational disruptions and unmet customer expectations.
Traditional and new supply chain analytics solutions stave off chaos by providing a consolidated view of all operations as well as granular insights into individual segment performance.
Featuring supply chain business intelligence (BI) tools, self-service analytics reports, visual dashboards, and data science models, supply chain analytics software comes in different shapes and sizes.
But all supply chain analytics solutions can be divided into one of three groups according to the type of analytics provided:
- Descriptive supply chain analysis provides a summary of the present supply chain. Descriptive analytics solutions act as a single source of truth for all connected systems.
- Diagnostic supply chain data analytics aims to identify the root cause of specific issues within the chain to migrate disruptions and streamline collaboration.
- Predictive supply chain analytics forecasts supply chain performance under specific conditions. It’s helpful for modelling business scenarios and identifying risks.
- Prescriptive supply chain analytics is the pinnacle of technological evolution. Such AI- and ML-driven systems can not only predict outcomes but also prescribe recommended actions to ensure the best response.
Each of these types of supply chain analysis tools has its merits and place in modern supply chain management. Yet predictive analytics in supply chain management is undeniably gaining momentum as global leaders continue with supply chain digitization.
Supply chain: Predictive analytics vs supply chain BI tools
Traditional BI tools are good for processing data that is pre-cleansed, well-structured, and stored in the required format. Typically, such tools are bound to one specific function (e.g. SCM analytics for finance) and limited by a set of standardized reports. For example, you may be able to easily generate a financial compliance report for a local regulator but may not have an option to generate all insights needed for, say, GDPR reporting.
The scope of traditional business intelligence supply chain solutions ends with descriptive analytics. Most BI software can provide answers to the following questions:
- What happened in the past?
- What is happening now (operationally)?
- And occasionally — Why is this happening?
A newer generation of predictive supply chain solutions has a more extensive reach.
Connected to data warehouses or data lakes, such solutions leverage big data analytics and machine learning (ML) to model advanced scenarios spanning the what, why, how, and what’s next. This allows business leaders not only to learn about past and current happenings but also to understand the underlying reasons for them and get a peek into the near future.
Why the need for better supply chain management analytics is dire
In 2018, 78% of organizations surveyed by Ventana Research relied on spreadsheets for supply chain planning.
Three years later, supply chain managers rank advanced supply chain analytics as a crucial technology investment, due for short-term adoption:
Source: Gartner — How to build a strong supply chain analytics strategy
Why such an increase in interest?
Because modern supply chains have grown increasingly complex and prone to multiple headwinds and tailwinds coming from every direction.
Consumers expect a stellar omnichannel shopping experience, whereas the business climate encourages businesses to develop multilateral relationships with partners to grow and scale jointly. Not to mention the overall shift to globalized sourcing strategies and mounting regulatory pressure around reducing environmental footprints and improving supply chain sustainability.
87% of supply chain executives agree that multiparty systems are poised to become the center of commerce, supply chain, and transactions among partners and customers.
Supply chain analytics solutions, as well as supply chain management (SCM) digitization at large, are vital to connecting all partners into a tightly enmeshed, well-oiled ecosystem that can be analyzed and then optimized for better performance.
Overall, supply chains and data analytics are a strong match, as supply chains produce plenty of raw data for successful operationalization.
However, supply chains also offer some barriers to implementing SCM analytics:
- Lack of interoperability of disparate legacy systems
- Fragmented infrastructure obtained during a host of mergers and acquisitions
- Low-to-no data management capabilities to support analytics implementation
- Missing talents and skills to create and use new solutions
- Lack of a strong business case for adoption
Many business leaders are not fazed by these challenges. On the contrary, as many as 53% of CEOs have already allocated investment towards accelerating supply chain transformation.
Supply chain analytics: Use cases and benefits
In what areas are SCMs seeing the biggest benefits and returns on investment from supply chain data science and analytics solutions? We have identified five function-specific use cases.
Supply chain demand planning and forecasting
Big data generated by supply chains can be used for sales planning, forecasting, inventory management, and procurement.
Instead of operating on hunches and best judgment, you can precisely determine how many sales you can generate in a given timeframe, region, and product category. Or you can develop new data-backed scenarios for collaborating with upstream and downstream partners during peak seasons.
Such knowledge is priceless. Especially given that 75% of SCMs faced problems with production and distribution in 2020.
Supply chain logistics analytics
Transportation accounted for 70% of the total global logistics bill of $10.6 trillion in 2020. This figure makes sense given the rapid adoption of ecommerce and omnichannel shopping. But it also indicates the growing pressure to excel at logistics.
Between last-mile deliveries and cold chain logistics, companies (and their delivery partners) have little room for mistakes. For instance, 56% of consumers will not buy from a retailer after one unsatisfactory delivery experience.
Integrating analytics solutions for logistics and transportation into your supply chain can help you gain clarity into:
- Asset movements across locations and partners
- Costs associated with different transportation routes
- Fuel consumption and management
- Emissions produced at different segments
- On-site personnel performance
- Warehouse operations and local asset use
- Fleet visibility and remote management
The current state of technology advancements allows for producing granular, real-time insights on asset movement and transportation conditions — from geolocations to humidity levels inside trucks.
Supply chain process optimization
Supply chains are complex organisms, with many flows extending in different directions. However, as they evolve, some of these flows become redundant or obsolete. Beyond that, supply chains are inherently prone to an array of disruptions, both one-off and recurring.
Magnitude of, frequency of, and ability to anticipate disruptions
Source: McKinsey — Risk, resilience, and rebalancing in global value chains
Diagnostic and predictive analytics solutions in supply chain management can significantly facilitate the discovery of low-value processes and their subsequent optimization or automation as well as help companies plan for emerging risks and disruptions. For instance, after adopting predictive analytics, Lenovo managed to reduce the time for responding to supply chain disruptions by 90% — from days to minutes.
Cumulatively, targeted investment in supply chain visibility and transparency can improve operational effectiveness, resilience, and the customer experience (CX).
Procurement analytics
Gaining extra intelligence for procurement can enhance the quantity of collaborative relationships with suppliers, from optimizing spending on raw material delivery to predicting maintenance and ordering spare parts.
With more systems connected to the analytics core, procurement officers can gain accurate, timely, and actionable insights into a company’s overall procurement spending as well as granular views into individual supplier contributions to the bottom line. What’s more, the “predictive” component can help mitigate risks and disruptions occurring in this segment.
As per the Deloitte 2021 Global Chief Procurement Officer Survey, supply assurance was the biggest risk supply chain leaders faced in 2020:
- 56% mentioned suppliers going bankrupt or being severely hampered
- 41% had to spend extra on expedited shipping to keep critical supply lines flowing
- 36% admitted that suppliers failed to meet obligations
- 32% lost revenue due to supply shortages
Advanced supply chain management analytics solutions can help identify such risks at the onset so your teams can put contingency plans in place.
In fact, the same survey suggests that leaders in procurement are four to five times more likely to have deployed advanced analytics solutions and data visualizations and 18 times more likely to use AI and cognitive technologies for procurement compared to other supply chain actors.
If you’re wondering how such setups function technologically, check out our case study detailing how we developed a turnkey system to orchestrate product management processes for a Fortune 500 company.
Inventory management
Inventory management is another complex process prone to costly disruptions if left unattended.
From deadstock to delayed shipments, supply chain managers are often tasked with monitoring asset movements across warehouses, vehicles, and production and sales locations. Yet many still lack fit-for-purpose enterprise resource planning (ERP) analytics tools.
Gaps in inventory management
Source: Deloitte — Making the case for inventory optimization
These lacunas in knowledge have a tangible impact on operations. According to various sources, in 2020:
- In the fashion industry, the cost of unused fabric tallied $120 billion
- While 71% of businesses felt that indirect materials management and managing associated data was an important optimization strategy, few rated their abilities as “excellent”
Overall, modern inventory management analytics solutions can be packed with an array of models and formulas from maximizing turnover, managing costs, and ensuring timely delivery to end customers.
Popular methods include:
- Just-in-time (JIT) inventory management
- Predictive materials requirement planning (MRP)
- Days sales of inventory (DSI) models
- Segment, stock, and plan (SSP) approach
- And more!
For example, a targeted investigation into the causes of inventory accumulation and identification of slow-moving parts helped one manufacturer reduce the volume of slow-moving inventory by one-third, increase spare parts revenue by 3%, and improve overall gross margins by over 60%.
At any rate, the goal is to determine which type of issue you’d like to investigate and optimize, then work out the optimal supply chain data science method for creating accurate, real-time analytics.
To conclude: The pillars of a strong supply chain analytics strategy
Adopting supply chain analytics is a big step for companies early in their data management journeys. While many current off-the-shelf solutions are non-invasive and can be implemented in a matter of moments, most assume a certain degree of data management infrastructure maturity.
Conducting an inventory of available data is the first step we always recommend prior to adoption. Learn where the best intelligence is trapped and how you can gain access to it. In some cases, accessing analytics-ready data can be easy due to the availability of APIs.
Legacy systems, however, may require more complex transformations and re-platforming. That’s why you should choose your battles and go after the most accessible use cases first.
Use sources that are easy to tap into first. Test different supply chain analytics techniques to understand which help you obtain the best intelligence. Measure the impact of analytics on your performance. Then select new targets and revise your methods! Progress at a measured pace, one supply chain segment at a time.
Contact Intellias specialists for a personalized consultation on supply chain analytics solutions and optimal approaches to adoption.
