The Journey to Advanced Analytics
By Henry Canitz, Director Product Marketing and Business Development, Logility
Advanced Analytics is the autonomous or semi-autonomous examination of data using sophisticated techniques and tools to discover deeper insights, make predictions or generate recommendations. Data used for advanced analytics can come from a company’s internal sources, such as its enterprise resource planning (ERP) applications, from a partner, and/or from public sources. Companies strive to use data to support “fact-based” decision-making to mitigate risk and capture business opportunities. Advanced analytics allows companies to leverage the tremendous amounts of big data available today, including both structured data, such as from sensors, and unstructured data, such as from cameras and social media.
The use of advanced analytics in supply chain can drive the next competitive breakthrough. However, the journey to advanced analytics requires a strong foundation in a number of areas including:
In addition to these foundation building blocks, the journey to advanced analytics in supply chain management generally follows a number of steps along a maturity curve. A diagram laying out the Value vs. Attainment Difficulty for the five stages of analytics, as defined by Gartner Research, is useful when envisioning the journey to reach advanced analytics.
Advanced Analytics Maturity Curve
Let’s explore each of these levels of maturity (Descriptive, Diagnostic, Predictive, Prescriptive and Cognitive) and highlight the processes and solutions you might want to investigate during your journey to advanced analytics.
Descriptive Analytics is easiest to achieve. Just about every supply chain planning organization has the ability to determine“what happened” through reports and dashboards. Descriptive analytics often involves the use of data clustering, pattern-based analysis and visualization. Most systems provide descriptive analytic capabilities, however, only knowing “what happened” is often inadequate to make adjustments that improve future performance and add business value.
Diagnostic Analytics helps to answer the question “why did it happen.”Root-cause analysis is a classic example of diagnostic analytics. Visualization of key supply chain performance indicators can help to determine why something happened. Determining why something took place is a good first step in making improvements but still falls short in providing insight into what might happen in the future. Often companies that have not matured their analytics capabilities beyond diagnostic analytics are still “firefighting” and reacting to events in the supply chain.
Predictive Analytics helps companies get out in front of events and disruptions to enable a proactive approach to determine “what will, or could, happen.”Statistical forecasting is a great example of predictive analytics. Predictive analytics often involve analyzing data using simulation, “what-if” analysis and queries. Some companies use network and production simulation to predict and plan for changes in the supply chain. Knowing what will, or could,happen helps managers be proactive in approaching those events. Britain’s exit from the European Union, Brexit, is an example of having pre-knowledge of a major potential supply chain disruption. However, just knowing something like Brexit will occur doesn’t necessarily help determine the best course of action.
Prescriptive Analytics answers the question, “what should I do” to maximize profits, minimize costs, and/or meet customer requirements. 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. Stochastic optimization is the process of maximizing or minimizing the value of a mathematical or statistical function when one or more of the input parameters are subject to randomness. The word stochastic means involving chance or probability. One common use of stochastic optimization is to analyze and plan inventory in situations where statistical forecasting methods provide inadequate results such as with intermittent demand.
Cognitive Analytics is the highest stage of analytics and involves automated resolution through artificial intelligence (AI) and machine learning. Artificial intelligence is the simulation of human intelligence processes by computer systems. These processes include learning, reasoning and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision. Machine learning is a type of artificial intelligence where computers have the ability to learn without explicit programming. AI and machine learning programs teach themselves to grow and change when exposed to new data. When applied in the supply chain, artificial intelligence provides new insights and/or “foresight” into what might happen and allows companies to respond in ways that create unique competitive advantages. Automation enabled by cognitive analytics enables supply chain teams to focus more time on value-adding activities.
Building an environment where advanced analytics drives supply chain decision-making is achievable today. To reach an operational state of semi-autonomous and optimized decisions requires a strong foundation of organizational, process, and solution capabilities and a determination to climb the analytics maturity curve. Where is your company on its journey to attaining advanced analytics in supply chain management?
Quotes: Advanced analytics allows companies to leverage the tremendous amounts of big data available today, including both structured data, such as from sensors, and unstructured data
Cognitive Analytics is the highest stage of analytics and involves automated resolution through artificial intelligence (AI) and machine learning
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