Businesses are collecting more data as well as having access to unprecedented amounts of data from other sources. This has led to technological revolutions such as data warehousing and big data tools and technologies. This data holds useful information and patterns that can help businesses and firms to achieve their goals and improve their operations.
However, making sense of all this data and using it meaningfully can be a challenge given its sheer volume. To leverage this data, firms are increasingly turning to various models to meet various objectives like optimization and prediction. The insight that can be drawn from these models, as well as business-related benefits, is what is driving more and more firms to seek ways of making sense and using the available data.
Motivation for leveraging big data and large datasets
Depending on the firm’s objectives, large datasets and big data can be used to meet various objectives. One company may be interested in streamlining and increasing the efficiency of the supply chain. A manufacturing firm can use demand optimization in manufacturing for increased sales and profits. A retail chain may be seeking ways to optimize the use of shelf space to maximize profits. There are as many uses of big data and large datasets as there are good ideas on how to leverage them.
Various technologies, methodologies and approaches are being used in order to leverage big data including the following:
• Conventional Business Intelligence (BI) tools and technologies
• Integrated business planning (IBP)
• Predictive analysis
• Prescriptive analysis
• Demand optimization
Conventional Business Intelligence tools use the data available to firms to examine data and determine what has happened in the past, also known as descriptive analytics. Some of the technologies used are data mining and data aggregation.
Predictive analysis attempt to predict future scenarios to determine what could happen based on models and available data. Some of the techniques used are forecast techniques and statistical math models.
Prescriptive analysis provides management with actionable information on what should be done under various scenarios. Some of the technologies used are simulations and optimization. Applications include scheduling, planning and demand optimization.
Integrated Business Planning (IBP) aligns strategic plans with operation plans harmonizing strategy and execution. IBP seeks to connect planning functions in all departments of a firm to more efficiently align strategy and operations with financial performance.
Understanding Demand Optimization
Demand optimization seeks to utilize the available resources and processes to maximize sales volumes, revenue and profits. Some of the applications of demand optimization are determining optimal solutions for:
• Meeting market share objectives
• Meeting inventory goals
• Achieving gross margin targets
• Achieving desired sales
• Demand optimization in manufacturing
Demand optimization is being applied in more fields and industries. Like most high tech and highly competitive industries, manufacturing firms have been quick to adopt the technique for increased competitive advantage.
Manufacturing firms face complex supply and demand issues involving numerous variables. These variables impact on the products as well as the business as they affect quality, profitability and yields. Some of the supply side variables management has to take into consideration include:
• Production capability and capacity constraints
• Availability of inputs and raw materials
• Multiple production and assembly sites
Demand variables that affect decision making and business outcomes for manufacturing firms are:
• Sales projections
• Promotions and marketing
• Historical orders
• In-house orders
• Contract obligations
By leveraging IBP, analytics and demand optimization, businesses not only increase profitability but also serve customers better. This is result of creating better synergy between various departments like production, marketing, sales and distribution to meet organizational objectives.