Amid the general buzz in the supply chain planning field regarding optimization solutions and algorithmic planning, a diagram of value vs. difficulty lays out four stages of analytics by their difficulty and potential value. In the quest to do more with less, drive costs out of the supply chain, and provide higher levels of customer service, optimization and algorithmic planning are subject to a company’s analytics abilities and level of sophistication.
Let’s explore each of these stages (Descriptive, Diagnostic, Predictive, and Prescriptive) a bit and highlight the processes and solutions you might want to investigate to climb the ladder of analytics value.
Canitz Says... |
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| In the quest to do more with less, drive costs out of the supply chain, and provide higher levels of customer service, optimization and algorithmic planning are subject to a company’s analytics abilities and level of sophistication. |
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What do you say? |
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The Descriptive Analytics level is easiest to achieve. Just about every supply chain planning organization has the ability to determine “what happened.” It’s often achieved through dashboards, reports and event management, using data analysis tools like clustering, pattern-based analysis, visualization and reporting. Most systems provide these types of descriptive analytic capabilities, however knowing “what happened” is often inadequate to make adjustments that improve future performance.
Diagnostic Analytics help to answer the question “why did it happen” (root cause analysis is a classic example of diagnostic analytics). Often supply chain performance and visualization can also help to determine why something happened. Diagnostic analytics often involves analyzing data using simulation, “what-if” analysis, and queries. Determining why something took place is a good first step in making improvements but still falls short in getting out in front of new problems. Basically you are still “firefighting,” reacting to events in the supply chain.
Predictive Analytics help companies get out in front of events and disruptions to enable a proactive approach (“what will, or could, happen”). Statistical forecasting is a great example of Predictive Analytics, as well as the application of risk management and mitigation through simulation and “what-if” scenario analysis. Some companies use network and production simulation to predict and plan for changes in the supply chain. Machine learning is all about adopting technology that can learn from past events and predict what might happen in the future. Many companies invest in Sales & Operations Planning to try to determine what will or might happen. Knowing what will, or could, happen helps proactively design the business to be proactive in approaching those events. Britain’s exit from the European Union, known as Brexit, is an example of having advanced knowledge of a major potential supply chain disruption. However, just knowing Brexit is going to occur doesn’t necessarily help in determining the best course of action for your supply chain and company (read more in the post, Brexit: Supply Chain Risk or Opportunity?). You need the ability to evaluate multiple scenarios to predict the optimal plan for your business.
Prescriptive Analytics is the highest stage of analytics. It answers the ultimate question, “what should I do?” Determining the best path forward generally involves some form of deterministic or stochastic optimization. Deterministic optimization focuses on finding an optimal solution to a problem while meeting some predefined goals. Linear programming, mixed-integer linear programming, and non-linear programming models are all types of deterministic optimization. Commonly deployed examples of supply chain planning optimization solutions include inventory optimization, supply optimization, factory finite scheduling, network optimization, and transportation optimization. Optimization answers the question “what should I do” to maximize profits, minimize costs, and meet customer requirements. Optimization can also be used to automatically respond based on predetermined criteria, allowing the supply chain team to manage by exception and do more with less.
The ultimate goal for any company should be to embrace Prescriptive Analytics. As with many processes and technologies, it is best to build a strong foundation before trying to add higher level functionality.
Where is your company on the analytics ladder of value?
About the Author
Henry Canitz is The Product Marketing Director at Logility. To read more of Hank’s insights visit www.logility.com/blog.
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Your Comments/Feedback
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Charu Sharma
Analytics in supply chain , Holisol logisitics |
Posted on: Jun, 19 2017 |
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| Very informative. Most of the organizations are planning to increase their investments in their analytics with a bulk of it going to supply chain function because it holds the greatest potential for innovation and competitive advantage. With business analytics improving significantly in the last decade and offering decision support for the critical tactical and strategic supply chain activities, insights from these activities are helping the compnies to reduce their costs and also hepling in supply chain optimization. |
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Emelia
Logistics Marketing, BRI |
Posted on: Aug, 09 2017 |
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I agree with Charu Sharma, Analytics is becoming more and more important to the supply chain because it assesses the supply chain performance and it also identifies the inefficiencies in the supply chain with an ultimate objective of improving the end-to-end performance when it comes to operation and financials. Supply chain companies around the world needs to become more efficient and flexible because the industry itself is becoming more competitive.
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