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Increase supply chain visibility with better data analytics
Articles- Jul 18,22
Making effective use of supply chain data analytics is one of the industry's biggest ongoing challenges. Here, Emily Newton explains how to enhance supply chain visibility and transparency, and achieve a high ROI by using data analytics.
The citizens of Earth generate more data per day today than at any point in history. This is only trending upward, too. What are the world's movers and shakers meant to do with the 463 exabytes of data humanity will generate daily by 2025?
Making effective use of supply chain data analytics is one of the industry's biggest ongoing challenges. A wealth of information is available today, making sorting and prioritizing their findings a daunting prospect.
It's a challenge, but it's an exciting one. Here's how decision-makers can add value, improve supply chain visibility and transparency, and achieve a high ROI by improving their analytics.
High-quality supply chain data helps seize new opportunities
Gathering data isn't useful if people are not prepared to act on it. It took the shock of a pandemic to get small and medium-sized businesses to begin using their databases of potential insights. About 49% of surveyed companies are using data analysis "more or much more" often today than before COVID-19 began affecting commerce.
Pandemics are extreme and typically rare. However, COVID-19's scale revealed each source of redundancy, waste and vulnerability manufacturing, distribution and retail have been laboring under for years without working very hard to solve.
Major corporations got a head start on turning industry and consumer data to their advantage. The benefits for smaller businesses are coming into focus as the technologies become more accessible.
The following steps will ensure the investments small and medium businesses (SMBs) make in data analytics products yield the desired outcomes.
Don't forget data cleaning, organisation and aggregation
Not all data is useful out of the box. Any data set generated or purchased by a company might contain outliers and other hidden patterns that affect the quality of the analysis results.
Data cleaning should precede acting on gathered information. The process is largely automated, and it strips out quality issues that could make the results less accurate or objective. The result is a stream of more useful data feeding supply chain optimization efforts, computer models and other analytics tools.
Once a data set is cleaned, professionals can begin a preliminary type of analysis called aggregation. Aggregation is the identification of groups of similar objects. It might further categorize things by "likeness" within each set. Supply chain data cleaning provides objectivity, while aggregation gives it weight.
Enter into ethical data partnerships
It's common for unethical individuals and companies to buy and sell ill-gotten data on the dark web and elsewhere. Some personal information is worth a few hundred dollars, while stolen intellectual property could cost millions.
Not every company has the technological infrastructure in place yet, like networked sensors, to gather a meaningful amount of information and act on it. This is where data vendors, brokers and aggregators come in.
The temptation to turn a blind eye to the data source— when one believes it contains profit-boosting insights — is high. This is why ethical data aggregators help major brands engage in the proper aggregation and sale of industry-relevant information.
Companies throughout an industry or supply chain can engage in all kinds of value-adding data-exchange or aggregation business models. Companies must ensure it was gathered ethically.
Engage in demand modeling and forecasting
Manufacturers, distributors and supply chain operators can drill down into their data sets after aggregation and vetting any outside data partners to yield additional insights into demand and supply.
Observing and weighing trends among groups of customers, vendors and freight vehicles yields insights into expected behaviors and supply-demand imbalances. It also helps organizations anticipate disruptions.
Demand modeling used to rely on historical data to anticipate future trends and changes. That's no longer the case if supply chain operators use the right data analysis tools. Modern data analysis marries historical and real-time information to form credible predictions for the future.
Big companies got there first, but smaller ones are quickly closing the gap. Major pharmaceutical companies can uncannily predict where flu and allergy seasons will be the worst around the country, thanks to demand forecasting and modeling. Also, companies promising next-day or same-day shipping windows do it with smart analytics powering automation in their warehouses.
Focus on risk analysis, compliance automation and corporate citizenship
One of the most impactful use cases for data analysis in supply chain visibility involves risk analysis and compliance automation. Some 94% of surveyed business representatives say digital analytics is critical for achieving supply chain transparency.
Data gathered from the supply chain can yield insights into risks and opportunities related to transparency, compliance, safety and quality. The result is an organization that's more successful in the short term and better prepared for mergers, acquisitions and other opportunities in the future. Such doors open more freely to companies with a strong sense of corporate citizenship.
Here are just two ways improving data analysis fuels better corporate citizenship and supply chain visibility simultaneously:
- Automated data analysis replaces manual analysis, which doesn't always find every source of noncompliance. Hardware and software data sources can be leveraged to achieve process visibility and ensure operation within norms and permissible limits.
- Using historical and real-time data to anticipate manufacturing or distribution equipment failures, and reacting to them before this happens, ensures consistent product quality, consumer safety and company reputation. This serves compliance and visibility by creating strong sourcing and custody records, all the way back to the source of the raw material in some cases. Analysis yields transparency, which leads to accountability.
Better data management means better supply chain transparency
Gathering data from one end of the supply chain to the other inevitably brings transparency. The ability to see within previously siloed or isolated workflows and aspects of the business will, in turn, inevitably bring about awareness of shortcomings and opportunities.
These action items describe steps worth taking to clean, organise and think differently about data. Most importantly, remember that the results provided by data analytics can only be as intelligent as the questions people think to ask. The supply chain has more tools than ever to execute decisively on the answers.
About the author:
Emily Newton is a tech and industrial journalist and the Editor-in-Chief of Revolutionized Magazine. Subscribe to the Revolutionized newsletter for more content from Emily.
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