How to Optimize Supply Chain Efficiency with Big Data Analytics
Modern supply chains are full of major inefficiencies. While business leaders are typically aware of their pain points, they’re often unable to act meaningfully because they lack an accurate overview of their situation. Can big data analytics optimize their operations?
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Common Supply Chain Inefficiencies
Supply chain inefficiencies create bottlenecks and contribute to declining customer satisfaction, causing long-term, widespread issues. Many in logistics experience similar pain points.
Inadequate Data Tracking
Many companies struggle to implement comprehensive information collection and aggregation strategies because their operations are widespread and prone to disruption. Without an operational overview, they can’t generate data-driven insights.
Poor Quality Control
Poor-quality products and performance cause upstream inefficiencies. Whether defects go unnoticed or third-party vendors overlook their finer contractual obligations, employers face higher rates of unexpected inventory and productivity losses.
Lackluster Inventory Management
Poor inventory management can create cost and operational inefficiencies, causing upstream delays. While stockouts trigger unplanned downtime, the alternative is excess, which is often worse. Unfortunately, many decision-makers have no choice but to overstock.
In the United States, storage availability decreased to about 3.9% in 2022, which is down from 9.2% around four years prior. Inopportunely, the cost of renting warehousing space for distribution increased from $5 per square foot to $7 in the same span.
Supply Conflicts
Brands often misalign their inventory turnover with downstream production or upstream distribution because of demand surges, delayed delivery times, warehouse stock discrepancies, or new client acquisitions. Consequently, they typically experience delays and financial losses.
Benefits of Leveraging Big Data in Supply Chains
Big data analytics can provide a data-driven operational overview, enabling decision-makers to address their most common supply chain inefficiencies. For example, analysts can use it to generate insights on how often third-party suppliers are late, allowing them to pinpoint the source of delays and streamline operations.
Data-driven insights offer unparalleled visibility into inventory, demand, transport, and partners, giving decision-makers the tools to approach each choice fully informed. Whether they analyze information on return rates, failed deliveries, or demand surges, they make valuable observations about their pain points.
Optimized supply chain performance is among the top benefits of utilizing big data analytics in supply chains. When business leaders know specifics on scheduling, production, or inventory management, they can eliminate existing — or potential — inefficiencies.
Big data strategies may even improve customer satisfaction. According to a recent study from the International Business Machines Corp. and the National Retail Federation, 71% of consumers agree traceability is very important. They consider an accurate, near real-time overview of shipping and last-mile delivery valuable.
How to Utilize Big Data Analytics to Enhance Efficiency
The global big data analytics market value will increase to over $650 billion by 2029 — up from $240 billion in 2021. Since its penetration rate is so significant, an abundance of proven strategies and effective tools already exist.
Strategize
Business leaders must ensure the data they aggregate is relevant and meaningful — an overabundance of information could undermine instead of serve them. Of course, preprocessing and cleaning are vital, too, since duplicates, missing values, and outliers can skew insight accuracy, making their efforts ineffective.
Ensuring data availability during preparation is critical, considering the average organization has over 2,000 data silos and lacks interdepartmental communication. Leaders must use accessibility to assess the feasibility of their data science project because it affects whether or not they will succeed in delivering real-life impacts.
Implement
While the ideal application of big data analytics is comprehensive, staggered implementation may be more feasible for most in the logistics sector. Starting off, they should determine whether to prioritize demand forecasting, disruption risk management, cost modeling, or statistical quality control. This way, they simplify management during the early adoption period.
One commonly overlooked big data analytics implementation requirement is alignment. Decision-makers should make sure they are on the same page with third-party suppliers, vendors, and distributors to ensure their data-driven insights remain precise and relevant. A comprehensive solution only works if all involved parties actively participate.
Maintain
Naturally, utilizing big data analytics is not a one-and-done solution, especially in a constantly shifting sector like logistics. Rather, analysts must continuously aggregate and analyze information, periodically reviewing their sources to make sure they remain accurate. This way, they ensure long-term optimization is possible.
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The Bottom Line of Optimizing With Big Data
While big data analytics can potentially improve efficiency, streamline operations, and improve performance, its effects aren’t guaranteed. Analysts should stress the importance of proper collection, preprocessing, and cleaning procedures to ensure their data-driven insights remain as precise and relevant as possible.
Originally posted on OpenDataScience.com
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