In most companies, supply chains have become more intricate than ever, generating vast amounts of data that need to be analyzed. For example, in 2018, 78% of companies surveyed used spreadsheets (and were limited only to) for supply chain planning. In contrast, in 2021, supply chain specialists rank advanced supply chain analytics as a crucial technology investment, understanding its importance in the modern world for creating a well-oiled ecosystem that can be scrutinized and then optimized for better performance.
Enterprises must deal with various suppliers, partners, and other third parties to properly run supply chain management system (SCM). And since there is a wealth of data produced across the entire logistics network, business leaders also need to derive intelligence out of it. Today it is possible thanks to supplying chain analytics software solutions, allowing for receiving essential insights and transforming information into a powerful business tool.
In this article, we’ll discuss the peculiarities of data analytics in the supply chain, its elements, and future trends. You’ll also know why the need for better supply chain analytics is dire and how to build your own analytics platform to succeed.
What is Supply Chain Analytics?
Supply chain analytics implies a group of methods logistics companies apply to extract data from interconnected systems to get summarized data on current and forecasted supply chain performance. The information can be derived from fleet management, shipping, warehouse and inventory system, fulfillment software, and more.
Any supply chain is a consolidated entity with many moving parts involved, generating and demanding data to ensure effective operations at every leg of transportation. Therefore, a failure within one link in a supply chain affects the entire chain, translating into operational disruptions and unmet client expectations. As a result, enterprises look for practical solutions to improve the visibility of supply chain processes and bring added value to essential services.
Specialized software prevents chaos by submitting a merged view of all logistics chain operations as well as detailed 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 solutions can be presented in different shapes and sizes. That’s why it’s better to appeal to an experienced IT vendor that will help you build a solution fitting your supply chain needs.
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Before we talk about the opportunities that analytics grants, let's outline the process of managing a supply chain. It encompasses a wide range of interconnected activities that can be categorized into several groups:
- Planning covers demand forecasting and resource planning.
- Procurement is an act involved in selecting vendors, negotiating the terms of collaboration, and purchasing supplies needed for your business.
- Manufacturing concerns production and capacity management.
- Inventory management aims at controlling the optimal stock balance, sales, and warehousing procedures.
- Logistics management addresses order fulfillment and all delivery-related activities.
As we mentioned earlier, all supply chain digital transformation software solutions differ in terms of complexity and integrated features. For instance, some platforms may be primarily utilized for simple dashboards and visualizations. At the same time, advanced solutions address more complex data analysis such as self-service data preparation, interactive visual exploration, etc. However, there are four common areas of supply chain analytics depending on the data provided:
Descriptive supply chain analytics employs dashboards and reports to interpret what has happened. It implies using different statistical methods to look through, summarize and structure data about supply chain operations. This type of data processing is proper when answering questions like:
- How have stock levels altered over the last month?
- What is the return on invested funds?
But it is almost useless in forecasting: it doesn’t tell us why the change happened or predict the customers’ expectations.
Diagnostic supply chain data analytics regulates cause-and-effect relationships. It answers the following questions:
- Why are cargoes being delayed or lost?
- Why is our enterprise not fulfilling the same number of inventory turns as a competitor?
So firstly, you strive to understand what's going on in your logistics business, and then you are carrying out some diagnostic and data science stuff. Finally, it assists in identifying factors that affect your revenue.
Predictive supply chain analytics goes deeper into the data and allows forecasting supply chain performance under certain conditions. For instance, "How will a pandemic lockdown impact the availability and price of raw materials or finished goods?" It helps model business scenarios and detects possible risks. So, this type of data processing is a more specific niche, allowing business leaders not only to analyze information but also to make predictions driven by the data collected.
Prescriptive supply chain analytics is the peak of technological advancement. With the help of AI (Artificial intelligence) and ML (Machine Learning) powered systems, businesses can predict outcomes and prescribe the best course of actions employing optimization or incorporated decision logic. This can be helpful to adjust decisions about when to launch a product, whether or not to build a factory or define the best shipping strategy for each retail location.
Four common areas of supply chain analytics
Trends in Supply Chain Analytics
To get ready for the future of supply chain management, take a look at these supply chain analytics trends to boost your business performance.
Internet of Things (IoT)
Adding IoT functionality to the supply chain will allow logistics companies to collect different kinds of data from various supply chain stages. It will enable monitoring individual units of items and finished-goods inventory in the most effective way. Using sensors, supply chain leaders can automatically gather data such as pressure, temperature, GPS location, etc., and autonomously upload this information to a cloud server via the internet.
The visibility issues in supply chain operations increase the need for cloud technology that enables the immediate accessing and sharing of data across platforms and projects. In addition, maintaining visibility allows transportation firms to save time, free up resources, avoid issues related to inventory shortages and order backlogs, as well as enhance a company’s overall customer experience.
As big data grows, there is a need to reduce applicable analytics to make more informed decisions. This is where AI comes into play. Along with automating things like warehouse and inventory management, AI systems that comprise predictive analytics and machine learning algorithms are becoming critical for smooth improvements across planning and decision maintenance systems.
Transportation companies should record information from IoT devices on a blockchain, which can collect detailed decentralized data and can be applied to check that transaction records are authentic. Organizations can employ this data to guarantee sustainable sourcing, enhance the execution of supply chain contracts, and gain better funding.
Check our "Blockchain and IoT in Supply Chain" guide to know how modern technology can shape the logistics business
How to Use Analytics in Supply Chain
Today’s global supply chains imply not only physical streams of goods transportation but also massive information flow. Supply chain analytics helps apply all verified and relevant data collected by analysts, giving meaningful insights. Let’s see how data analysis in the supply chain works and how companies can make use of analytics platforms.
How Supply Chain Analytics Operates
Supply chain analytics consolidate data across multiple apps, infrastructure, third-party services, and advanced technologies such as Blockchain to enhance decision-making across the strategic, tactical, and operational processes that comprise supply chain management.
The process of making supply chain analytics usually starts with data scientists who comprehend a specific aspect of the business, such as the factors that involve cash flow, inventory, waste, and service levels. Next, these specialists search for potential relations between various data components to create a predictive model that streamlines the output of the supply chain. Finally, they test out variations until they get a robust model.
After, the analytics models that met the test are deployed into production by data engineers. These models are polished over time by correlating the performance of data analysis models in manufacture with the business value they deliver.
However, it’s pretty challenging to identify, structure, and interpret lots of data manually. That’s where automated solutions come in handy. With supply chain analytics software, you'll be able to get accurate results in a highly visualized manner in a few clicks. This type of software helps coordinate supply chain planning and performance by improving real-time visibility into logistics processes and their impact on consumers and profit.
Now, let’s see what supply chain analytics software can be used for:
- Data visualization. The ability to scrutinize data from different sides to enhance insight and understanding.
- Natural language processing. Deriving and systemizing unstructured data stored in various documents, news sources, and data feeds.
- Stream processing. Extracting insight from different data streams created by, for example, the IoT, apps, weather reports, and other third-party systems.
- Location intelligence. Extracting insight from location data for distribution optimization.
- Graph databases. Arranging data into linked elements to simplify searching for relations, identifying patterns, and improving traceability of goods, suppliers, and facilities.
- Digital twin of the supply chain. Arranging information into a comprehensive supply chain model that is distributed across all types of users to improve predictive and prescriptive analytics.
Why is Supply Chain Analytics Important
Supply chain data analytics tools bring significant benefits to logistics companies. Revenue growth, margins improvements, and better capital management are among the key reasons to integrate business intelligence into supply chain management.
Increased Planning Accuracy
For a logistics company, predicting consumer demand is critical since it affects all the other supply chain processes and is the basis for planning procurement, production capacities, logistics, sales, etc. By scrutinizing consumer data, supply chain analytics will assist businesses in better future demand prediction. For example, it will allow enterprises to determine what goods can be minimized when they become less profitable or decide what products will be in demand after the initial order.
Poor inventory management results in product shortages and overstocking, and neither is good. Excess inventory implies higher stock carrying costs while running out of items leads to lost sales. Analytics allows companies to strike the right stock balance to keep costs as low as possible without stock-outs. For example, the system might initiate an alert for SKUs (Stock Keeping Units) that are running out based on the average lead time for that supplier. Sales trends can also assist the operations team in determining which items require additional warehouse space and which could be stored in lower numbers or phased out.
Improved Risk Management
Emerging supply chain management technologies are often overvalued, so decision-makers must fully understand each new technology's risks and opportunities. Analytics solutions allow organizations to identify and predict their potential/future risks, enhance planning, streamline inventory management, and meet their clients’ high expectations. For instance, an analytics platform could point to the risks by noting that a specific carrier has repeatedly been late, delivering cargoes late over the last month. It can not only capture this pattern but signify the likelihood of further delays. In addition, the software can calculate the impact of such a delay, involving the number of potential late deliveries and the cost of chargebacks and returns.
Most logistics services providers focus on increasing revenue, neglecting increasing profitability. However, if you intend to grow your logistics business profitably, it’s not sufficient to adjust a few cost levers or hope to increase revenue by way of acquisitions. Instead, you must be ready to engage fundamental changes and challenge how things have been performed in the past.
Analytics systems allow identifying and analyzing costs across the entire chain, from network costs to last mile delivery. An essential product in this area is supply chain network optimization, where this network is analyzed in terms of cost to serve and service levels. Then, you can access and compare scenarios covering all predictable market dynamics to determine the best available option.
Why to use analytics software in supply chain
Supply Chain Analytics Examples
The examples of supply chain analytics include demand planning, sales and operations planning, inventory management, and logistics management. Each of these activities can enhance the overall efficiency of business operations, which can lead to significant cost savings. In this section, we’ll discuss the specific opportunities analytics provide in each logistics chain element.
Analytics in Planning
According to the BI-Survey, 57% of companies invest in BI to improve their sales planning and forecasting to get a competitive edge. Predictive analytics and machine learning techniques can be applied to support the planning process. Thus, companies will be able to understand the current performance, past trends, existing risks, and possible future scenarios. Forecasting consumer demand is figuring who will purchase your product/service, for what price, where, and in what quantities.
There are two basic approaches to demand forecasting:
Conventional statistical methods make predictions rely on historical data and presume the continuation of existing trends. However, the forecast's accuracy is unreliable since it involves general stability that is rare for the fast-changing environment of most markets.
Machine learning techniques scrutinize the big data from multiple sources, recognize hidden patterns and unobvious correlations between variables, and generate complex models that can be retrained to adjust to evolving conditions automatically. Even though this approach requires significant software, equipment, and personnel investments, the resulting accuracy is worth the price.
Besides planning, performing deep research of consumer tastes and sales trends would also help you cut expenses on reverse logistics. Distribution management also relies on market research as selecting the right distribution channels is essential for better customer reach.
Analytics in Procurement
After you understand how much you need to purchase, you have to define where or from whom to buy. For this purpose, you should scrutinize the suppliers and their performance.
Supplier assessment is essential if you want to ensure the reliability and credibility of your future partner. The information that your procurement management software creates will allow you to scrutinize potential suppliers’ performance by comparing different variables, such as KPIs, prices, compliance, etc.
Supplier performance review means scrutinizing current suppliers' metrics throughout your partnership lifecycle. It's vital to track your vendors' accountability, customer services, and possible additional fees. You can remove the poor-performing suppliers by assessing the timing of delivery and quality of delivered goods. Moreover, supply chain analytics techniques enable the prediction of possible supply disruptions (e.g., delivery delays) so that you can take preventive actions.
Analytics in Manufacturing
Manufacturing directly depends on accurate planning but at the same time has to be flexible enough to adjust to demand changes. The never-lasting need for optimization and maximizing performance should also be considered. How to decrease production time and cost? How to enhance quality? How to utilize the workforce and equipment more efficiently? These are some of the issues that can be addressed with the help of scrutinizing the following production-related data.
Monitoring production performance
Analytics and BI tools can merge and visualize all the critical data (e.g., machine performance, shift performance, quality, and scrap rates) that would allow you to track your production process more effectively.
Cost analysis techniques such as BOM intelligence provide a clear understanding of the product’s cost structure and disclose opportunities for lessening your expenses. For example, using cost-benefit analysis (CBA), you can evaluate the benefits of decisions made, while marginal analysis enables defining optimal production capacity.
Data mining techniques allow finding correlations between apparently disconnected factors. For instance, root cause analysis would help determine the cause and effect between machine breakdown and low-quality spare parts or fuel. More advanced analytics allow performing preventive maintenance based on information from IoT devices and multiple sensors connected to equipment.
Scrutinizing both quantitative and qualitative information from manufacturing operations and consumer feedback, you get valuable insights into enhancing your product's quality and design to meet clients' expectations. Besides, you can reveal the reasons behind item defects, even if they arise years later.
Analytics in Inventory Management
Inventory management engages in stock operating, including raw materials, finished/for-sale goods, MRO (maintenance, repair, and operations) goods, etc. The main task here is to figure out the right quantity of needed goods in the right location at the right time.
Stock management. The statistics claim that US retailers lost $82 billion in 2021 due to being out-of-shelf items. The supply chain analytics monitor sales trends and inventory left, submit suggestions on replenishments, recognize top-selling and underperforming goods, and so on. That enables preventing costly overstocking and stockouts.
Optimizing warehousing processes. Analytics software can scrutinize the goods flow to provide recommendations on the best possible resources allocation and the most effective route to pick them.
Measuring channel performance. Analytics and BI tools allow generating different reports to visualize your dealers' performance, products, sales channels, etc., and assist in improving inventory control and further decision-making. For instance, you can determine which channels can handle more stock or watch that some channels bring losses because logistics costs overrun, resulting in incomes.
Pricing optimization implies adopting your prices under different internal and external factors, such as market conditions, competitor actions, input costs, etc. Tracking sales information along with these variables would allow you to select the optimal pricing strategy and increase revenue.
Analytics in Logistics
There are a lot of advantages that software in general and particularly analytics tools can give to streamline logistics and transportation management. Here are a couple of examples of areas that can be analyzed.
Fuel management. By scrutinizing driver behavior using telematics devices, you’ll be able to reduce fuel costs (which can be a significant saving since fuel costs take one-fourth of total operational expenses).
Route optimization. Using weather and traffic data allows determining more fuel-cost-efficient routes between each stop. It helps logistics companies maximize completed deliveries, considering many criteria such as driver schedules, available hours, total stops, etc.
Shipment tracking and vehicle maintenance. Gathering information from sensors, cameras, and other IoT devices allows cargo monitoring (so that you and your clients can be aware of the location and condition of the goods) and helps carry out preventive/predictive maintenance.
Managing returns. Recognizing the reasons that cause returns would allow you to find ways to eliminate their number. What are the reasons? For instance, your product description doesn’t correspond to authenticity, or there is some item defect. Whatever the reason is, it should be removed.
Examples of supply chain analytics
How to Integrate Analytics into Supply Chain Management Process
After you’ve figured out how your company can make out of analytics solutions, the next question arises: “How to implement it into logistics operations?”. We’ve prepared a step-by-step guide to supply chain analytics software implementation success.
Define Business Problem
Firstly, you need to determine your specific objectives: what business challenges you want to solve. It’s not limited to goals like “get more profit” and “reduce costs.” You may want to address other pain points, such as preventing equipment breakdown, finding more reliable suppliers, improving product quality, etc. Understanding what business issues have to be solved would set up the direction of your further improvement.
Evaluating Current Capacities
At this stage, you need to identify the criteria that would let you monitor progress and measure success. Even though you're tracking different metrics to get an accurate understanding of your enterprise’s performance, KRIs are required to show how you reach your specific business objectives. For instance, if you want to improve product quality, you can track scrap rate and the number of returns. On the other hand, if you have concerns with your suppliers, it's worth watching out for order accuracy, number of on-time deliveries, number of damaged products, etc.
Determine Data Resources
Once you outline the KPIs you want to center on, you have to seek information you will need for analysis. It’s vital to define which data and from which sources are already being employed and which data is missing leading to inaccurate KPIs monitoring. You may discover that you lack some critical data or exhaust some sources. In other cases, you may find out that you have the information, but it must be digitized (like qualitative data from social networks). Data can be received from both inside your companies across multiple departments and outside it.
Internal data sources include:
- ERP system
- CRM system
- Logistics management systems
- Warehouse management system, and so on.
External data sources include:
- Social media
- B2B integration platforms
- Competitors’ performance information
- Customer feedback, etc.
Choose Trustworthy IT Partner
After understanding business needs and capacities, as well as the data sources, you need to find an experienced IT partner to cooperate with. This issue needs a thoughtful approach. But how to find a reliable vendor? As a professional software development company, we’ve gathered several practical tips for finding the right development team to bypass costly mistakes.
- Consider industry expertise. Pay close attention to the company's expertise to ensure that you get the desired product. In addition, the industry expertise accelerates the creation of analytics software for the supply chain since the team is familiar with processes, required technologies, and challenges they may encounter.
- Check the portfolio. Look through the company’s portfolio to learn what solutions they delivered before and see if they align with your vision for your own analytics platform.
- Reviews. Check the clients’ reviews to decide whether it’s worth dealing with the company. It will allow you to manage expectations and be ready for possible challenges.
- Communication. It’s vital to establish a smooth communication flow to stay in touch with your team to work effectively and solve issues faster (especially, if you deal with an offshore software development company). For instance, the Cleveroad team uses Jira, Confluence, Slack, Google Meet, or Zoom for these purposes. Besides, we create a communication plan with clients to hold meetings on a convenient schedule for them.
The next question is “Where to look for a development partner?”. You can find the required expertise using different hiring platforms, thoroughly analyzing the feedback left by previous clients, and getting acquainted with their company websites or related resources, such as Clutch.
Development and Integration
Develop a customized supply chain analytics software meeting your unique logistics business needs. Like any other software solution, analytics platforms are typically built during sprints, two-week intervals for certain functionality completion. Each sprint starts with the planning session and ends with delivering working software or features. After the analytics system is built, it should be implemented into existing business processes. It’s vital to optimize the data flow to prevent its duplication or failure.
You should also think of functionality to integrate and results to be obtained. For example, if you only need to visualize and track your supply chain performance through the major KRIs, including the standard reporting capabilities makes sense. However, if you want to receive actionable insights from available information and make accurate predictions, it's worth designing a full-fledged data architecture that would contribute to your business growth.
Learn how business intelligence in supply chain can contribute to better analysis of business-related indicators
Even though supply chain analytics software implementation is a complex process, it gives logistics businesses the opportunities to reach a new level of profitability and efficiency. One of the main things you need is to connect a reliable technical partner with experience in this domain. And Cleveroad can help you with that.
Cleveroad is a professional web and mobile software development company with 10 years of experience in the market. We have extensive experience in the Logistics software development services and provide a wide range of services from, developing custom solutions to supporting and upgrading legacy software.
We have an individual approach for every project and provide our customers with a range of benefits:
- Thorough research conducted by our business analysts and free project estimate
- Hands-on software development experience across different domains
- Guaranteed high-level protection data by following best security practices and regulations
- A variety of flexible partnership models suitable for every case: dedicated team, fixed price, time & materials
- Full-cycle software development and ensuring top quality of your solution
- Utter team structure comprising all the essential high-qualified specialists
- Innovative development approach and cutting-edge tech stack
Whether you need to create a separate supply chain analytics software or integrate it into existing logistics solutions, Cleveroad professionals are ready to help you with your project. You’ll be provided with a thorough consultation from our domain experts and a custom analytics solution for your supply chain needs.
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Big data analytics consolidate data across multiple apps, infrastructure, third-party services, and advanced technologies such as Blockchain to enhance decision-making across the strategic, tactical, and operational processes that comprise supply chain management.
Supply chain analytics helps use all verified and relevant data collected by analysts, giving meaningful insights. It contributes to increased planning accuracy, margin improvements, inventory optimization, revenue growth, etc.
Predictive supply chain analytics allows forecasting supply chain performance under certain conditions. For instance, "How will new trade legislation or a pandemic lockdown impact the availability and price of raw materials or finished goods?".
Any supply chain is a consolidated entity with many moving parts involved, generating and demanding data to ensure effective operations at every leg of transportation. Therefore, a failure within one link in a supply chain affects the entire chain, translating into operational disruptions and unmet client expectations. As a result, companies need analytics software solutions to improve the visibility of supply chain processes.
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