“An Exploratory Study of the Utiva Data Analytics Fellow s— Abimbola, Kunmilade & Abdullahi”
Our students took on the challenge to explore the space of the supply chain because it is rated as the 4th most-affected field of management that will be disrupted by data analytics. The Utiva Fellows are professionals that are trained within the Utiva Data School
Abstract, in recent years, the amount of data produced from end-to-end supply chain management practices has increased exponentially. Moreover, in the current competitive environment supply chain professionals are struggling in handling the huge data. Organizations want to leverage their data to optimise supply chain business strategy. Therefore, they must build on these trends and to cope with the changed requirements, supply chains need to become much faster, more granular, and much more precise.
INTRODUCTION
Big data is a term that describes the large volume of data — both structured and unstructured — that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. “It’s what organizations do with the data that matters”. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
SUPPLY CHAIN
A supply chain is the connected network of individuals, organizations, resources, activities, and technologies involved in the manufacture and sale of a product or service. A supply chain starts with the delivery of raw materials from a supplier to a manufacturer and ends with the delivery of the finished product or service to the end consumer.
Supply chain management is the handling of the entire production flow of a good or service — starting from the raw components all the way to delivering the final product to the consumer. To accomplish this task, a company will create a network of suppliers (the “links” in the chain) that move the product along from the suppliers of raw materials to the organizations who deal directly with users.
COMPONENTS OF SUPPLY CHAIN MANAGEMENT
• Planning — Plan and manage all resources required to meet customer demand for a company’s product or service. When the supply chain is established, determine metrics to measure whether the supply chain is efficient, effective, delivers value to customers and meets company goals.
• Sourcing — Choose suppliers to provide the goods and services needed to create the product. Then, establish processes to monitor and manage supplier relationships. Key processes include ordering, receiving, managing inventory and authorizing supplier payments.
• Making — Organize the activities required to accept raw materials, manufacture the product, test for quality, packaging for shipping and schedule for delivery.
• Delivering (or logistics) — Coordinating customer orders, scheduling delivery, dispatching loads, invoicing customers and receiving payments.
• Returning — Create a network or process to take back defective, excess or unwanted products.
• Enabling — Establish support processes to monitor information throughout the supply chain and assure compliance with all regulations. Enabling processes includes finance, human resources, IT, facilities management, portfolio management, product design, sales and quality assurance.
THE FIVE “Cs” OF EFFECTIVE SUPPLY CHAIN MANAGEMENT
• Connected: Being able to access unstructured data from social media, structured data from the Internet of Things (IoT) and more traditional data sets available through traditional ERP and B2B integration tools.
• Collaborative: Improving collaboration with suppliers increasingly means the use of cloud-based commerce networks to enable multi-enterprise collaboration and engagement.
• Cyber-aware: The supply chain must harden its systems and from cyber-intrusions and hacks, which should be an enterprise-wide concern.
• Cognitively enabled: The AI platform becomes the modern supply chain’s control tower by collating, coordinating and conducting decisions and actions across the chain. Most of the supply chain is automated and self-learning.
• Comprehensive: Analytics capabilities must be scaled with data in real-time. Insights will be comprehensive and fast. Latency is unacceptable in the supply chain of the future.
SUPPLY CHAIN MANAGEMENT AND BIG DATA ANALYTICS
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes. Supply chain management is a field where Big Data and analytics have obvious applications. Until recently, however, businesses have been less quick to implement big data analytics in supply chain management than in other areas of operation such as marketing or manufacturing. Big data is increasingly becoming key to having an efficient supply chain and a reduction in costs.
In fact, it’s now standard practice to gather and analyze massive amounts of information to help boost revenue.
Lisa Dunn (2014), cited that experts predict the trend will continue to expand, and the cost-savings alone in efficiently re-structuring supply chains are potentially enough for not only significant additional profit but also for efficient, streamlined operations moving forward. According to Forbes, Big data analytics helps organizations reduce costs, make faster, better decisions, and create new products or services to meet customers’ changing needs. In fact, the future of supply chain digitization will be driven by data and analytics. Data is a commodity which is not necessarily valuable in and of itself — insights from that data are far more useful. Numerous advances powered by technologies like predictive analytics and location intelligence are improving the way the entire supply chain makes use of data. Supply Chain entities are interconnected by a significant physical flow that includes raw materials, work-in-process inventories, finished products and returned items, information flows, and financial flow. Paul Myers, professor of practice in supply chain management at Lehigh University explained the use of big data analytics as a solution which helps companies make more informed decisions with a greater level of insight and have access to better models and simulations to some supply chain issues such as various economic factors such as rising fuel costs, changing supplier bases, increased competition from low-cost outsourcers, and the continuing global recession significantly impact the supply chain and create waste.
According to Computerlink, prioritizing the development of a big data analytics strategy will help an organization overcome the following supply chain challenges:
• Better Predict Customer Needs and Wishes In the age of the customer, offering the right product, to the right person at the right time and place is key to gaining (or retaining) customer satisfaction and loyalty. Smart organizations will leverage big data to get a full 360-degree view of your customer to better predict customer needs, understand personal preferences, and create a unique brand experience.
• Better Assess Supply Chain Risk Big data can help assess the likelihood of a problem and its potential impact, and support techniques to identify supply chain risk. Combining the analysis of historical data, risk mapping, and scenario planning can enable a risk management approach for early warning.
• Improve Supply Chain Traceability Traceability and recalls are by nature data-intensive. Big data has the potential to provide improved traceability performance; it can also reduce the thousands of hours involved with accessing, integrating, and managing product databases that capture products that should be recalled or retrofitted.
• Improve Supply Chain Efficiency
It ensures cost efficiency, cost reduction, and spend analytics will continue as top business priorities in supply chain management.
FedEx handles nine million shipments a day and all the accompanying data. For Efficiency and expediency, the company recognizes the growing trend of big data, so they created an information service that combines a GPS sensor device and a web-based collaboration platform: “SenseAware”. Originally used by the healthcare and life sciences industries as a means to track high value and/or extremely time-sensitive shipments (and now available to all industries), SenseAware attaches digital information to packages, providing: Precise temperature/weather readings, Information about a shipment’s current location, notification when a shipment is opened or if the contents have been exposed to light, real-time alerts and analytics between trusted parties regarding the above vital signs of a shipment. Since the device is equipped with a radio that constantly broadcasts information back to FedEx, an enormous amount of data is generated — information that must be acted on in real-time. Therefore, analysis of the data is critical (Fedex.com).
Challenges in Implementing Big Data Analytics for Supply Chain Management According to Mohamed et al (2018), the following are the issues and challenges in adopting Big Data Analytics for Supply Chain:
• Time-consuming: Factors such as the volume of Big Data, Complexity of Supply Chain and interpretation goals for the datasets along with external factors such as lack of access to data contribute in making the analytics process time-consuming.
• Insufficient resources: For better results, the availability of real-time data is crucial. Supply Chain is a platform that generates complex cross-functional data for interlinked entities, collection and storage of cross-functional data should be streamlined.
• Privacy and security concerns: Data sharing across a Supply Chain Network is a major factor in collecting data from various sources, analyzing it and giving insights. Although, regional or global Supply Chain Networks might face difficulties in sharing data across its different sources due to various Privacy, Security laws concerned with the sharing of data. Lack of shared data in such cases can affect the accuracy of the insights that Big Data Analytics might generate.
• Inadequate skills: The complexity of Big Data generated from Supply Chain source requires a combination of good domain knowledge analytics skills and the ability to interpret the usability of data. According to surveys, such a combination along with experience is difficult to find.
• Data Quality: Quality of the stored and utilized data can affect the performance of the results of the analytics techniques. Data being intangible and multidimensional based on its sources and applications. Dimensions of the multidimensional dataset can be classified as intrinsic and contextual. For consistent and reliable results for decision-making purposes, the quality of data should be consistent. The variety of data and type of sources for data in the supply chain may affect the quality of the collected data.
Data scalability: Issue of Data Scalability is considered a major technical issue in the process of utilizing Big Data Analytics in any system. The inability of organizations to shift from traditional limited databases to distributed databases or cloud storage databases affects the insights from Big Data Analytics as the amount of relative data is compromised.
CONCLUSION
With ever-increasing streams of data, Supply Chain function is receiving greater pressure to not only manage procurement and supply chain but to reduce spend, enhance strategic business relationships and drive an organization’s overall objectives. Analysis from Deloitte highlights how chief procurement officers are struggling with this role.
Top-performing Chief Procurement Officers (CPO) and Supply Chain Directors must become masters of data complexity, not for the data itself, but the insights & opportunities that flow from them.
The fact that procurement & Supply Chain touches multiple areas of the enterprise, from strategic supplier relationships and compliance to spend and cash flow management — presents an opportunity for Analytics to greatly enhance the efficiency of multiple points in the supply chain function. Big Data Analytics applied to supply chain management can be expected to drive significant annual savings and exponential return on investment (ROI).
