When a company really knows its manufacturing operation, it can adjust to market forces and stay competitive and efficient. In part I, we considered two new forces of customization that would help drive this change. In Part II, we look at how data–driven manufacturing, the third force, will now push manufacturing to find the right data to stay competitive.
The successful manufacturing operation needs good data to react and to predict market forces. This kind of intelligent decision-making comes from data-driven analytics that allow operations and their marketing teams to move quickly, anticipating customer wants and needs at the right time.
Data–driven manufacturing will help manufacturers use predictive analytics to extract or mine information from their data that can predict new market trends. In this way, they can anticipate market ‘behavior’ and deliver products and/ or services to meet demand.
A good example of a company using predictive analysis from proper data mining is Vodafone Netherlands (part of Vodafone Global –a telecom company.) Using a predictive analysis model, the data told them that many of their mobile customers wanted to go skiing for their next vacation. Vodafone ran a promo and launched a winter sports roaming campaign for ski markets in Europe with a roaming package making it easier and cost effective to stay connected when traveling. Using predictive analysis, Vodafone developed a predictive model based on profiling data of past customers who used their mobile phones at winter sports hotspots in order to properly determine the repeat market of ski enthusiasts. By offering a specialized roaming product with the probability of a fixed daily cost, this predictive daily model paid off in smart product creation and in more sell-up opportunities. Predictive analytics will turn the data into actionable insight, predict products customers want, and gain an edge on the competition.
The challenge is to get the right data so that smart changes and upgrades can be made in the system. Data-driven manufacturing is based on facts, not guesses. Emerging technology is enabling software systems to better collect and process the data needed to achieve better results. Driving manufacturing with data promotes integration and coherence across the organization throughout the supply chain.
Descriptive, Predictive, and Prescriptive Analytics
With the flood of data available in the supply chain, manufacturers now need the right mix of analytics to mine their data to help improve decision making. Historical data is usually the first go to in preparing for what might happen in the future. Looking at all the analytic options can be a daunting task. These analytic options can be classified into three distinct types. No one type of analytic is better than another, and they co-exist with, and complement each other.
Descriptive Analytics: Understanding the past to answer the question, “What has happened?”
Data aggregation and data mining is used to provide insight into the past and answer this question. Reports generated describe the past and are vital to learn from past behaviors, and understand how they might influence future outcomes. Descriptive analytics provide historical insights regarding the company’s production, financials, operations, sales, finance, inventory and customers
Predictive Analytics: Understanding the future to predict the future. “What could happen?”
To understand the future, this analytic makes use of statistical models and forecasts techniques to understand the future and predict possible outcomes. Predictive analytics and data mining use algorithms to discover knowledge and find the best solutions. Data mining is a process based on algorithms to analyze and extract useful predictive modeling. Using data mining and probability to forecast outcomes, each model is made up of a number of predictors, or variables, that are likely to influence future results. Once data has been collected for relevant predictors, a statistical model is made and the information designed to uncover hidden patterns and relationships.
Prescriptive Analytics: Understanding the use of optimization and algorithms to give advice on possible outcomes. “What should we do?”
Users “prescribe” a number of different possible actions as regards possible outcomes that will guide towards a solution. These analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predict not only what will happen, but also provide recommendations regarding actions that will take advantage of the predictions.
These statistics take the data and fill in the missing data with best estimates. They combine historical data found in ERP, CRM, HR and POS systems to identify patterns in the data and apply statistical models and algorithms to capture relationships between various data sets. When companies want to look into the future, prescriptive analytics can be used throughout the organization, from forecasting customer behavior and purchasing patterns to identifying trends in sales activities. They also help forecast demand for inputs from the supply chain, operations and inventory.
Global manufacturing is entering an era of radical change. Always challenged to be watchful of forces that could disrupt efficiency, smart manufacturing must pay careful attention now to data mining to stay competitive. Advanced data analytics and the customized smart products and services they produce will give manufacturing companies true enterprise value. The more centralized systems that sync up both production and customer demands now require a solid cloud ERP (Enterprise Resource Planning) to be sure all interconnected software keeps critical data relevant. It is about staying out in front of the headwinds now.