Explore why supply chain analytics are crucial to success in manufacturing and how it can be used for more informed decision making.
Explore why supply chain analytics are crucial to success in manufacturing and how it can be used for more informed decision making.
Behind every successful manufacturing company is a successful supply chain. The supply chain is the skeletal system of the manufacturing process, and as such, they generate loads of data. This data, although important, is difficult to gather, process, and glean insights from in order to optimize your supply chain. Enter: supply chain analytics.
Analytics provides the opportunity to make informed business decisions based on a summary of useful, trustworthy data which is often visualized by way of charts, graphs, reports, and more. Supply chain analytics make sense of the sea of data, breaking down which parts are useful, identifying patterns, and producing insightful solutions. These data-driven insights are crucial, especially over the next decade when analytics usage will skyrocket. These informed decisions lead to better operations planning, a streamlined supply chain, and ultimately, more profitability.
Why Are Supply Chain Analytics Important?
If you had access to a tool that predicted the future, wouldn’t you use it? Of course you would. Now, are we saying that supply chain analytics predicts the future 100% accurately? No. Are we saying that supply chain analytics identifies patterns and predicts future trends with some degree of certainty? Yes. Supply chain analytics techniques allow businesses to hone in on current patterns, glean data from those operations in real-time, and put big data to work in order to understand market trends, quantify demand, and determine accurate pricing strategies. Here are some of the predominant reasons that supply chain analytics are crucial to success in manufacturing:
Improve supply chain planning
By quantifying and analyzing data from customers, supply chain analytics is able to predict future demand with relative reliability, in order to help a business plan appropriately. It allows that business to decide which operations/products should be cut back on when demand slips, or become less profitable. Additionally, the business can better understand typical customer needs after an initial order, thanks to supply chain analytics.
Increase flexibility within the supply chain
In the modern manufacturing world, production can’t be stagnant or tied down to one single site, process, or methodology. The era of the flexible supply chain is here, and a business intelligence strategy based on advanced analytics is necessary to navigate that transition. Data gleaned from machines on the shop floor predicts maintenance schedules, which saves time and money. Supply chain data from third sources predicts customer behavior and toggles a change in production to match the change in demand. This streamlines the supply chain, allowing your business to save crucial dollars, or spend crucial dollars – all in the name of optimized profitability.
Understand trends or risks within the supply chain
Spotting patterns and trends throughout the supply chain is the easiest way to avoid a major exposure, and better understand the risks your organization faces on a daily basis. And many of these perspectives – these risk areas – are unknown to the manufacturer until the data is aggregated appropriately. Supply chain analytics may be able to inform a supply chain manager of a major pending issue days before it comes to fruition, which saves the company money and precious time.
Gain efficiency in staffing or sourcing
With an effective analytics strategy, resource scheduling becomes so wired for efficiency that it is nearly impossible to revert back to an analog manner of operation once you’ve glimpsed the competitive advantage provided by supply chain analytics. The software coordinates with vendors, schedules deliveries, and keeps all employees abreast of the latest information, all in real-time.
Understand key aspects of business
After you’ve implemented your supply chain analytics strategy, your business will streamline the following: planning accuracy, order management, procurement, increase working capital, mitigate risk within the supply chain, identify key trends, and make adjustments toward a flexible, lean supply chain. These key aspects of business are crucial to your bottom line. And the best part: supply chain analytics won’t just make these changes for you. Rather, the data will allow you to understand the flaws as they are, and enable you to make the proper changes as necessary. Supply chain analytics is as much an educational tool as it is a business planning aid.
How Do Supply Chain Analytics Work?
Supply chain analytics gathers data from multiple sources – infrastructure, applications, third-parties, IoT, and other emerging technologies – in order to heighten decision-making in the operational, tactical and strategic processes that comprise successful supply chain management (SCM). Supply chain analytics are key in the synchronization of supply chain planning by providing real-time visibility into critical operations and the impact they have on both the business’ bottom line and the customer satisfaction level. Real-time visibility increases supply chain flexibility, and allows decision-makers to more accurately evaluate any potential trade-off between customer service levels and cost.
Implementing supply chain analytics tends to begin with a data scientist. Data scientists – and data science in general – understand particular aspects of a business: cash flow, inventory, service, waste levels, etc. and they are utilized to locate trends, correlations, and outliers in the supply chain. These experts build supply chain predictions models which lean on this data analysis and data warehousing to optimize the supply chain output. Multiple variations are tested and analyzed before reaching a successful, repeatable, robust model that becomes a company’s supply chain analytics strategy.
Analytics models for supply chains that entertain a particular level of success are utilized into production by data engineers who skew towards selecting analytics strategies with a certain degree of supply chain performance and scalability. These three key personnel groups – data scientists, data engineers, and business users – work coherently to continually course-correct the manner in which data analytics are presented and put into practice.
Types of Supply Chain Analytics
Supply chain analytics offer a broad array of opportunities, but for the most part, it exists in these four main types:
Descriptive analytics enable supply chain visibility as well as a single source of reliable information across your supply chain. This exists for both internal systems, external systems, and data. Descriptive supply chain analytics utilizes dashboards or reports to assist in communicating past, current, or future realities. It uses a bevvy of statistical operations to sift through and organize useful data, and then present helpful information about the vitality of supply chain operations and provide visualization of peak potential. This type of analytics can answer questions like, “How have inventory levels changed over time?” or “What is our ROI on this particular piece of capital?”
Diagnostic analytics is a sort of troubleshooting. The key purpose of diagnostic supply chain analytics is to determine why a particular thing happened, or didn’t happen. This type of system is best at locating the shortcomings, and identifying the reason. This answers questions like, “Why did we run out of inventory this month?” or “Why haven’t we reached the same quantity of inventory turns as the other guys like Amazon?” or “Why did we stock out so soon?” or “How much do we have in safety stock?”
Predictive analytics are, well, predictors. This system serves to forecast a likely future event based on current trends, data, and past processes. Predictive supply chain analytics saw a huge spike in interest at the beginning of the pandemic, because companies wanted insight on what was about to happen to demand in their industry. Predictive analytics allow a business to understand business implications of a particular trend – allowing the company to mitigate risks or disruptions. Companies like Gartner are leading the charge in predictive analytics.
Prescriptive analytics allows organizations to gain the information necessary to collaborate and solve problems for peak operational value. Prescriptive supply chain analytics collaborates with shipments, logistics partners, materials vendors, etc. to minimize wasteful time, and maximize efficiency. This system automates the prime plan of action by employing embedded decision logic. This answers questions like, “When should we launch this product?” or “What is the best shipment strategy for our various retail locations?”
Supply Chain Analytics Data Sources
Where does data come from? Do supply chain analytics only access data that has been entered into a system by an employee? How do analytics systems know where to look? These are all relatively legitimate questions. The answer: data is everywhere. Supply chain analytics mines the data using algorithms, and is given access to computer systems by an administrator. So, it will find data that your employees have not yet reached. The following sources are where much of the data that your supply chain analytics strategy will come from:
Social Media Listening – This data is quite unstructured, and some of the hardest to glean. However, it is an effective data source for demand planning/demand forecasting efforts.
Geo Data – Utilizing the wonders of location based data, analytics can help to predict demand differences or success by location specific location. This will help trim down wasteful spending, as well.
Inventory Data – Speaking of wasteful spending, a good analytics system will bolster inventory planning and lead to cost savings.
Inventory turnover – Metrics from past performances is analyzed, and combined with any current and forecasted data to optimize inventory turnover rates and minimize inefficiencies.
ROI – Additionally, past performance on the supply chain network and finances as a whole will provide a robust view of which investments were quality, and which were poor.
Employee Workload – Staffing has forever been in the hands of the manager but with data that displays the most efficient schedules, employee workload can be best structured with the right analysis.
Production rate – Production is not a static process, there’s ebbs and flows to it. Supply chain analytics can help inventory planning to match the production rate of your business as it changes – saving money, time, materials, and effort.
Shipping Data – This bucket of data is booming, especially with the onset of IoT. Instead of maintaining a strict schedule, analytics software will maintain the most optimal schedule – for everyone.
Supply Chain Analytics History
It doesn’t come as a shock to anyone to say that the supply chain of today looks nothing like the supply chain of 100 years ago or more. But although today’s global supply chain is a whole new beast, it’s the same animal. The idea of a supply is no new concept, and supply chains have been in practice since products existed; albeit, they were much more local and hand-crafted than the ones in use today. Supply chain history is an interesting topic.
Prior to the initial industrial revolutions, the only supply chains anyone knew of were local, and defined by geography. Transportation, especially commercial transportation, was in its infancy; until everything changed around the turn of the 20th century. Educating the public on the topic of supply chains was Frederick Taylor in 1911 with the Principles of Scientific Management. He was right in the thick of the action in the early 1900s. The internal combustion engine. The invention of trucks. The invention of semi-trucks. The invention of the diesel engine. The invention of the forklift. The invention of pallets. These all happened between 1900 and 1930, and laid the foundation for which the global supply chain would build upon in coming decades.
The 1930s and 1940s saw an increase in logistical prowess, as well as global interaction within supply chains. World War II set in motion many efficiency standards that would never dial back. Supply chains were needed to manufacture supplies, both here and there. This was the period when operations strategy and industrial engineering combined to become supply chain engineering. The 1950s introduced to the world the wonders of containerization and standardization. Many consider this the greatest revolution to supply chains ever. The first shipping containers were invented in the mid-50s and at this same time, vehicles were built specifically to handle containers. This dynamic time period and series of innovations drove global trade, and made a lot of people a lot of money.
The 1960s to the early 1990s witnessed the onset of the computer. Computerized inventory management and forecasting systems were introduced by IBM in 1967. Everything up to this point was on paper. No longer. The first real-time warehouse management system – tracking orders more efficiently, monitoring inventory, etc. – was installed in 1975. This is also when barcodes and SKUs were introduced, which led to much higher product tracking capabilities. “Supply Chain Management” officially became a term in 1983. SCM saw a tweak in operations when the personal computer began to play a role in manufacturing in this time period. New software was taking over everywhere – flexible spreadsheets, route planning, air freight optimization, and the introduction of the Enterprise Resource Planning (ERP) systems.
And that leads us to today; on the verge of a massive revolution in manufacturing thanks to the opportunities provided by further computerization. Data has always mattered, but it’s never been accessible or transformable in the way it is today. Supply chain optimization through data analytics, Artificial Intelligence (AI), machine learning, supply chain software, and more is on the cusp of a massive overhaul. Although roughly only 10 percent of manufacturer’s have fully adopted Industry 4.0 technology into their supply chains, you can bet your bottom dollar that the other 90% is figuring it out as we speak. If historical data means anything, there’s too much money to be made for the rest of the industry to remain “old-school” on this.
Future of Supply Chain Analytics
Upward and onward. That’s the only feasible conclusion, right? With the introduction of 5G and the Internet of Things, alongside advanced analytics-induced automation, optimization algorithms, and exponentially-efficient warehouse floors – it seems logical that the future of supply chain analytics software will evolve at a faster rate than it is now. But before that happens, this initial group of futuristic technologies must be widely adopted.
According to Supply Chain Brain, “For now, just 12% of supply-chain professionals say their organizations are deploying AI as a supply-chain management tool.” So as it stands, the evolution of the technology may outpace the adoption from business. But those factors will level out soon enough. And the question is, where will you be when that time comes? Ahead of the curve and ready for the next innovation, or wading water in the deep-end, trying to play catch up because you didn’t get on board sooner? If you’re concerned about the latter, find out how we can help.
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