In today's challenging economy, companies can't wait to find out that a particular plant isn't producing work orders on time or that a supplier needs to be more reliable. Instead, today's businesses demand real-time visibility into their supply chain to manage day-to-day operational performance. No wonder the best business intelligence solutions are fast gaining recognition as mission-critical components in any supply chain analytics solution.
What is the Main Need for Supply Chain Analytics?
"The need for supply chain analytics is within the top three priorities for [companies]," said Nari Viswanathan, a principal analyst at research firm Aberdeen Group. "We have found a correlation between companies showing best-in-class behavior toward supply chain analytics and those demonstrating best-in-class behavior toward operational performance."
Supply chain analytics can help organizations make data-driven decisions, optimize operations, and gain a competitive advantage with BI in the marketplace. By leveraging suitable types of supply chain analytics, businesses can improve their efficiency, reduce costs, and better meet the needs of their customers.
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What Are the Types of Supply Chain Analytics?
There are several types of supply chain analytics, each with its unique focus and objectives.
Descriptive analytics involves the analysis of historical data to understand what has happened in the past. This category of supply chain analytics helps organizations identify trends, patterns, and anomalies in their supply chain operations and better understand their current performance.
Diagnostic analytics aims to identify the root causes of issues or problems in supply chain analytics. It involves using data to determine why certain events or trends occurred and to develop a deeper understanding of the underlying drivers of supply chain performance with supply chain analytics.
Predictive analytics uses statistical and machine learning techniques to forecast future supply chain performance. This variety of supply chain analytics can help organizations to anticipate changes in demand or supply, identify potential disruptions, and optimize their operations to improve efficiency and reduce costs.
Prescriptive analytics goes beyond predictive analytics and recommends specific actions to improve supply chain performance. This version of supply chain analytics uses algorithms and optimization techniques to determine the best course of action based on available data, constraints, and objectives.
The Importance of Choosing the Right BI Solution for Supply Chain Analytics
So how can supply chain analytics solutions from vendors such as Cognos, Dimensional Insight, IBM, Oracle, and SAS Viya help? For starters, the right supply chain analytics solution can help you track your most profitable products, flag production problems, identify product quality issues, better forecast raw material needs, establish more accurate lead times to fulfill orders, highlight outstanding supplier balances, and more.
Supply chain analytics can be used for demand forecasting. By analyzing past data, companies can predict future product demand, allowing them to optimize inventory levels and production schedules. This can reduce the risk of stockouts and overstocking, leading to a more efficient and cost-effective supply chain.
This is accomplished by pulling together information from disparate silos into one leading ERP solution, including accounts payable, inventory control, vendor management, and production analysis, to produce an in-depth and holistic view of the supply chain analytics— bottlenecks and all.
Transportation analytics is another area where supply chain analytics can be used. By analyzing all the transportation data, companies can identify inefficiencies in their fleet, such as blank miles, and optimize their routes and modes of transportation. This can reduce transportation costs and improve delivery times.
An example of how BI can do this would be when a company may be experiencing high transportation costs. Of course, it would be easy to assume that these price hikes result from rising fuel prices. But a supply chain analytics tool can drill down into the supply chain for a more in-depth understanding of cause and effect. As a result, a company might discover that fuel charges need to ramp up production costs, but suppliers are expediting products via high-priced couriers.
"Visibility is key," said Viswanathan about supply chain analytics. "It's not enough to know that your transportation costs are rising. You need to be able to drill down and identify the real causes."
Another practical application of supply chain analytics is creating "what-if" scenarios. Supply chain risk analytics can be used to identify and mitigate potential risks to the supply chain, such as supplier bankruptcy or natural disasters. By using data to assess the likelihood and impact of these risks, companies can develop contingency plans to minimize the effects of disruptions using supply chain analytics solutions.
For many companies, preparing for unexpected changes requires keeping enough inventory to meet monthly customer supply and demand variations — a costly contingency plan. However, by using real-time supply chain analytics to simulate what-if scenarios, a company can assess the impact of replacing suppliers, switching from train to truck transportation modes, establishing new routes, increasing product prices, etc.
The Power of Automatic Alerts in Supply Chain Analytics
Many supply chain analytics tools include automatic alerts. For example, if orders for a particular product fall below forecast levels, a plant manager can request to receive a sign so that the necessary adjustments can be made. But there's a flip side to receiving notification of production snafus, warns Viswanathan. "Alerts are becoming increasingly important as it becomes more difficult for companies to keep tabs on what's impacting their business," he said. "But alerts can also be dangerous. Too many alerts can create problems."
After all, no one wants to receive an alert every time there's a slight modification in the supply chain. Viswanathan recommends that users configure supply chain analytics tools so that signs are not just informational but are based on production specifics.
Fortunately, supply chain analytics is more than just gathering the details of a company's production cycle. Instead, suppose supply chain analytics solutions are deployed and leveraged correctly. In that case, the right supply chain analytics tool can turn data into unique insights that reduce costs, streamline operations and bolster customer satisfaction.
Compare the best BI solutions before investing in supply chain analytics strategies in your organization.