Inefficiencies in a supply chain are expensive; in the UK alone, it’s reported to incur an annual cost of $2 billion. Supply chain managers aim to increase productivity, cost savings, and speed to market but struggle against a complex ecosystem, driven by multiple participants (manufacturers, retailers, vendors, etc.), channels (online, offline, omni), and variables.
Moreover, most data generated in a supply chain falls outside the scope of just one enterprise or entity, and this layering makes analysis even more challenging.
As the supply chain management (SCM) landscape is becoming increasingly complex and traditional systems are proving to be inadequate, supply chain managers are turning to big data analytics.
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Big data analytics is a combination of tools, processing systems, and algorithms that can interpret insights from data. Traditionally, SCM has relied on ERP and other disparate storage systems for data. But with supply chain analytics, the needle has shifted from just automation to forward-thinking data integration and better decision making.
Using real-time data — which is a mix of both structured and unstructured formats — and the power of the 3Vs (volume, velocity, and variety), supply chain analytics has enabled collaboration of supplier networks and end-to-end integration in the truest sense.
Let’s take a look at how big data can make a difference in each stage of the supply chain:
Forecast product demand more accurately
Sourcing and Development
Evaluate contractor performance in real-time and identify hidden costs
Maximize resources and production output
Improve performance significantly in terms of efficiency, accuracy, and speed
Reduce return costs and provide greater visibility to the process
Big data for planning
At the planning stage, integrated data across the entire supply chain network along with the use of statistical models help forecast demand more accurately (e.g. sales numbers, inventory levels).
For instance, by getting inventory and replenishment systems to communicate, we can ensure that there are no out-of-stock scenarios in retail. These models not only factor in past and real-time data, but also consider macroeconomic factors, industry trends, and competitor data.
Big data for sourcing and development
Procurement costs on average around 43% of the total costs incurred by an organization. Given the huge potential for savings in this area, firms are leveraging supply chain analytics to evaluate contractor performance and compliance in real-time rather than in quarterly or annual cycles when it may be too late to intervene and lower costs.
Even during contractor evaluation, quantitative methods can make the cost structure more transparent by helping decision makers identify hidden costs.
Big data for execution
During execution, big data can help reconfigure the many moving parts to optimize the available resources (space, tools, materials, people, etc.) and maximize output. In the manufacturing industry, for instance, IoT sensors can provide real-time equipment data that can be optimized on the fly to improve asset performance and production capacity.
Analytics is also used in predictive scenarios such as fault estimation or scheduling maintenance. Intel, for instance, is saving $656 million per year using predictive analytics.
Big data for delivery
At the delivery stage, it’s all about speed (getting the product out on time), accuracy (ensuring the packages reach the right destination), and efficiency (finding optimal route/combining deliveries). Real-time delivery data superimposed with external data, such as traffic and weather patterns, can result in significant performance improvements in logistics management.
Big data for return
Currently, product returns are estimated to be 30% for certain product categories, which is a major deterrent for companies maintaining their profitability. Examples of reverse logistics costs are restocking expenses, transportation costs in returning the product to the retailer/warehouse, shipping overheads in sending another product to the customer, and decisions costs on assessing the returned product.
Analytics can help reduce these costs and provide the visibility needed to make seamless returns by combining data from inventory and sales systems, and inbound and outbound flows.
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3 examples of big data in supply chain management
The supply chain economy is a web of multiple industries, and big data analytics has made an impact on most of them. Here, we look at three examples.
Supply chain big data in manufacturing
In the manufacturing industry, data is spearheading the fourth industrial revolution. And the use cases are plenty — collecting telemetry data for predictive equipment maintenance, gathering contextual intelligence to eliminate bottlenecks for high throughput, and forecasting demand.
Domino Printing Sciences, for instance, gained visibility into daily operations and made its business model more efficient by integrating data across multiple sources — ERP, CRM, Oracle, and Salesforce. The company went one step further and shared reports with suppliers, making the impact more tangible across the entire chain.
Supply chain big data in consumer goods
For consumer packaged goods (CPG) firms, big data analytics helps plan for what-if scenarios and answer questions on whether strategies, such as marketing spends, are bringing in expected returns or if a new product feature will result in a better customer experience.
Sound United LLC, a company focused on developing audio/video products and services, used IoT data from its product’s IP and MAC address as well as app data to arrive at insights on its customer experience. Using these insights, Sound United gained a sense of customer preferences and developed new features accordingly. Additionally, the company had a better understanding of how much inventory retail partners were holding versus selling, which allowed it to improve demand planning.
Supply chain big data in agriculture
Dealing with perishables, the food industry is constantly seeking ways to revamp the supply chain. One way to achieve this is to combine historical and real-time data to enhance operational efficiencies and reduce delivery cycles.
Another use case in using data revolves around food safety assurance and sustainability. The food industry is experiencing a rise in conscious consumers, who prefer to be aware of their food sources. Lœul & Piriot, a food company specializing in the processing of rabbit meat, aimed to track its meat from breeding to delivery. But the company used two different systems — one for production flows (from the time orders are received to preparation, logistics, and delivery), and another for the purchasing process. By bridging these systems, the company built a way to trace the journey of the livestock in its supply chain.
The cloud and the future of big data for the supply chain
Cloud computing has many known benefits: scalability, cost-effectiveness, and reduced outages. Specifically, for a layered ecosystem like supply chain, the cohesiveness that cloud computing can bring to data, is invaluable. For instance, GPS and weather data can optimize delivery routes, IoT sensors in conjunction with AI can bring down asset downtime, and social media can reveal customer insights and help companies better understand competitor strategies.
Without migrating to the cloud, most organizations will be operating in silos reinventing the wheel. The plug-and-play nature of cloud-native components makes business transformations easier and on-par with the competition. This can be in the form of augmented IT capabilities such as machine learning that are available off-the-shelf for analytics or even customized SCM solutions. In fact, the market value of cloud-based SCM solutions is expected to surpass $11 billion by 2023.
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Big data for the supply chain: Case studies
Many companies have already started to take advantage of the potential of big data analytics to solve their core business problems. Let’s look at two such case studies.
Capgemini uses big data to achieve a 6-month supply chain view
Capgemini is a global leader in consulting, technology, and outsourcing services with more than 190,000 employees in over 40 countries. The company’s growth model has historically been acquisition based; Capgemini has expanded by acquiring companies such as IGATE, Fahrenheit 212, TCube, and Idean most recently.
This rapid growth (100,000 employees in 2011 versus 180,000 in 2016) challenged the company’s ability to anticipate and optimize management and planning of human resources. To meet this challenge, Capgemini launched a strategic initiative called "Resource Supply Chain."
As part of the initiative, Capgemini invested in a data integration service that unified almost forty applications. For instance, all CRM applications were consolidated into Salesforce. End-to-end integration of applications that performed HR activities right from resume analysis to recruitment and user creation, and then transporting the data into payroll systems all helped Capgemini outline a long-term outlook. Today, Capgemini has a six-month overview of its staffing needs.
Office Depot’s big data hub feeds an improved supply chain
In January 2017, the AURELIUS Group (Germany) acquired the European operations of Office Depot, creating Office Depot Europe. Office Depot Europe operates in 14 countries as a leading reseller of workplace products and services.
The retail industry is a highly competitive space and Office Depot Europe had to retain its customer base amidst competition. To counter this challenge, the company wanted answers to questions such as: Why have we lost customers in certain segments and gained in others? What would it mean if we are to increase our spending on certain channels?
However, as the company’s data was fragmented across multiple channels (website, offline catalog, and customer call centers), it wasn’t easy to perform such analytics.
Office Depot Europe adopted a data management solution to gain an enterprise-wide view of its operations and customers. By centralizing its data, the company was able to get a 360-degree view of the customer and create tailor-made strategies for them.
Moreover, reusable components used in the integration solution increased efficiencies and brought down the total cost of ownership. The hub approach also enabled Office Depot Europe to funnel high-quality data to back-office functions such as supply chain and finance.
Getting started with big data for supply chain management
68% of supply chain leaders believe that supply chain analytics are critical to their operations. However, a key ingredient for the success of supply chain analytics is consolidating data from all participants and flows in the system, essentially breaking the boundaries between them.
Talend Data Fabric achieves that by offering a single suite of self-service apps for data integration and data integrity. Talend lets you stream data from multiple sources in real-time and derive crucial insights on the basis of trusted quality data. Try Talend Data Fabric today to begin benefitting from big data.