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As we noted in last week’s post in which we focused on the digital supply chain, we’re in the midst of a series based on the recently released Association for Supply Chain Management’s (ASCM) top ten 2024 supply chain trends:

  1. Digital Supply Chain

  2. Big Data and Analytics

  3. Artificial Intelligence

  4. Supply Chain Investment (systems and people)

  5. Visibility, Traceability, Location Intelligence

  6. Disruption and Risk Management

  7. Agility and Resilience

  8. Cyber Security

  9. Green and Circular Supply Chains

  10. Geopolitics and Deglobalization of Supply Chains

In this second post in the series, we’re focusing on number two on the list — big data and analytics — to discuss current trends and learn more about why it made the top ten. Over the eight weeks to follow, we’ll do something similar with each of the remaining categories as we move into the new year. As we progress through the list, we’ll see that many of the trends overlap in one way or another.

Defining big data and analytics

In an article published in the Journal of Big Data entitled, “Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities,” study authors Mahya Seyedan and Fereshteh Mafakheri define big data as “high-volume, high-velocity, high-variety, high value, and high veracity data requiring innovative forms of information processing that enable enhanced insights, decision making, and process automation.”

Citing various studies, they describe the “5 Vs” more specifically by noting that:

  • Volume refers to “the extensive size of data collected from multiple sources (spatial dimension) and over an extended period of time (temporal dimension) in SCs.”

  • Velocity can be defined as “the rate of generation and delivery of specific data; in other words, it refers to the speed of data collection, reliability of data transferring, efficiency of data storage, and excavation speed of discovering useful knowledge as relate to decision-making models and algorithms.”

  • Variety refers to “generating varied types of data from diverse sources such as the Internet of Things (IoT), mobile devices, online social networks, and so on.”

  • Value refers to “the nature of the data that must be discovered to support decision-making. It is the most important, yet the most elusive, of the 5 Vs.”

  • Veracity refers to “the quality of data, which must be accurate and trustworthy, with the knowledge that uncertainty and unreliability may exist in many data sources. Veracity deals with conformity and accuracy of data. Data should be integrated from disparate sources and formats, filtered and validated.”

In an article published in Big Data and Cognitive Computing entitled, “Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions,” study authors In Lee and George Mangalaraj use an oft-referenced phrase that combines the use of big data and analytics: “big data analytics,” aka BDA.

Describing how the proliferation of mobile technologies and advances in data mining techniques led to the “explosive growth of big data analytics (BDA) across industries,” they say that BDA “transforms low-value raw data into high-value information for business decision makers.”

The result? Organizations that use BDA may experience various benefits — including “cost savings, better decision making, and higher product and service quality.”

BDA in supply chain management

As Seyedan and Mafakheri note, supply chain management (SCM) “focuses on [the] flow of goods, services, and information from points of origin to customers through a chain of entities and activities that are connected to one another.”

They describe the following categories of supply chain (SC) data:

  • Customer

  • Shipping

  • Delivery

  • Order

  • Sale

  • Store

  • Product

As a result, Seyedan and Mafakheri describe supply chain data as “high dimensional generated across many points in the chain for varied purposes (products, supplier capacities, orders, shipments, customers, retailers, etc.) in high volumes due to plurality of suppliers, products, and customers and in high velocity reflected by many transactions continuously processed across supply chain networks.”

Lee and Mangalaraj note that SCM is one area in which BDA is increasingly used.

Referring to the supply chain as the “central organizing unit in the globalized economy,” they note that in the current business environment, it has become critical for businesses to “effectively manage increasingly extending supply chain activities beyond their boundaries.”

“BDA in the supply chains has surged in importance recently, as cloud computing allows supply chain partners to collect, transmit, store, and process an enormous amount of data economically and share the data/information in real-time,” Lee and Mangalaraj write. “Furthermore, BDA for the supply chains demonstrates great potential for process improvement, cost reduction, and better decision making for supply chain management.”

Using BDA to address demand uncertainties

Seyedan and Mafakheri single out demand uncertainties as having “the greatest influence on SC performance…”

In this context, they refer to demand forecasting as critical to addressing supply chain “uncertainties” — which is one way BDA can make a big difference.

“With the advancements in information technologies and improved computational efficiencies, big data analytics (BDA) has emerged as a means of arriving at more precise predictions that better reflect customer needs, facilitate assessment of SC performance, improve the efficiency of SC, reduce reaction time, and support SC risk assessment,” Seyedan and Mafakheri write.

Instead of applying conventional forecasting methods that use historical data to identify statistically meaningful trends, they note that intelligent forecasting which incorporates BDA can “learn from the historical data and intelligently evolve to adjust to predict the ever changing demand in supply chains.”

“This capability is established using big data analytics techniques that extract forecasting rules through discovering the underlying relationships among demand data across supply chain networks,” Seyedan and Mafakheri say. “These techniques are computationally intensive to process and require complex machine-programmed algorithms.”

Seyedan and Mafakheri note that BDA is increasingly being used in SCM for purposes that include:

  • Procurement management (e.g., supplier selection)

  • Sourcing cost improvement

  • Sourcing risk management

  • Product research and development

  • Production planning and control

  • Quality management

  • Maintenance and diagnosis

  • Warehousing

  • Order picking

  • Inventory control

  • Logistics/transportation

  • Logistics planning

  • In-transit inventory management

  • Demand management (e.g., demand forecasting)

  • Demand sensing

  • Demand shaping

“A key application of BDA in SCM is to provide accurate forecasting, especially demand forecasting, with the aim of reducing the bullwhip effect,” they write.

BDA methods

In “Critical analysis of the impact of big data analytics on supply chain operations,” published in Production Planning & Control, Ruaa Hasan and colleagues say that the potential for using big data is “limitless,” but is “restricted by the availability of technologies, tools and skills available for BDA.”

Citing various studies, they describe three analytical methods that can be applied to BDA efforts to “enhance decision-making and increase supply chain operational output”:

  • Predictive Analytics: “…This analytics approach is related to predicting future events such as best delivery time, individual customer behaviour, out-of-stock and shortages predictions, demand forecasting, point of failure for equipment predictions, and sales performance prediction. …”

  • Descriptive Analytics: Which is described as “…collecting and analysing data describing current and past events, and individual product functions and features to identify the cause of problem and identify the main reasons behind past success or failures. …”

  • Prescriptive Analytics: Which the authors say is used for “detecting, recognizing, and diagnosing of fault[s] and irregularities in [a] timely manner.”

Why did big data and analytics make the top ten?

In an analysis of ASCM’s Top 10 2024 supply chain picks, Informatica’s Scott Jennings refers to data as “critical to supply chain transformation.”

“It’s no coincidence that four out of the top five ASCM 2024 supply chain trends directly involve data,” he writes.

Noting that the ASCM board was “unanimous in their agreement on data’s role,” Jennings says that “Through this ongoing supply chain transformation, ASCM now emphasizes the paramount importance of data in bringing about a more agile supply chain.”

He also notes that the board underscored the need for data to be “relevant, clean, and governed” to ensure the accuracy of the analytics and predictions it enables.

“Otherwise, the risk of running down the wrong rabbit hole becomes very real,” Jennings writes.

Which he says is the reason big data got booted from the top spot this year.

“The primary reason cited by the ASCM membership for this change?” Jennings writes. “The data itself. Are analytics really telling the right story and why are spreadsheets driving the process? …”

Additionally, he says various data trends can “drive sustainability,” which many companies may find valuable to help meet new and ongoing ESG requirements.

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