4 Ways Big Data is Changing Inventory Management
- Big Data and Master Data Management
- 4 Ways Big Data is Changing Inventory Management
Big data is a term used to describe a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity of current technology applications. Despite these problems, big data has the potential to help companies improve operations, profitability and can help management make faster, more intelligent business decisions.
The benefit gained from the ability to process large amounts of information is the main attraction of big data analytics. However having too much data without the proper technology infrastructure can create more problems than value. Large data volumes present the most immediate challenge to conventional IT structures as on-premise solutions typically are not designed to manage the weight of large data sets. Big data is heavily influenced by the 4 main components including, volume, velocity, variety and veracity.
- Volume – The size of the data captured
- Velocity – The speed at which data changes
- Variety – The different forms of data sources
- Veracity – The uncertainty of data accuracy
The real value to businesses is the ability analyze data trends in real-time to extract business critical data for management and operational execution.
The data will involve data from a variety of sources, software and hardware, which is being collected from devices and products all over the world ranging from jet engines and heavy equipment to solar powered trashcans and electric toothbrushes. Companies are using big data to change the way they monitor product performance, research, development and innovation.
Big Data and Master Data Management
It is tempting to draw a direct comparison between big data and master data management (MDM), but the two terms differ. Big data exists in real-time and involves instantaneous access to data, alerts and inventory management and control metrics through cloud computing systems. Where master data management data can have a shelf life, big data value is realized with how quickly patterns and trends can be identified.
Whilst MDM involves collating company-wide information from different departments and bringing it together on one file, this will still be basic information that involves smaller data sets. It may involve inventory levels, customer information, sales and other basic data that can be moved onto one file.
In contrast, big data will take information from a variety of sources such as scanners, CRM, sales systems, web-based data, enterprise resource planning tools, and accounting systems to collate this information into a high-velocity real-time overview of operations. The speed and size of this type of data would overwhelm conventional processing and storage systems.
4 Ways Big Data is Changing Inventory Management
In the wholesale distribution of non-perishable goods, big data is helping to integrate business systems to improve operational efficiency enterprise wide while delivering higher profits than ever before. Innovative leaders in the supply chain industry are realizing the following benefits that are the result of harnessing big data analytics across supply chains. Below are 4 ways Big Data is changing the way companies manage inventory.
- Improved Operational Efficiency: Operations managers have a minute-to-minute overview of the operation helping to remove bottlenecks and improve efficiency, owing to better access to metrics. Big Data enable supply chains to proactively enhance efficiency and performance compared to older reactionary models.
- Maximized Sales & Profits: In the wholesale distribution industry, access to real-time data is helping finance directors to manage traditionally tight profit margins with greater insights to ensure that maximum profits can be realized from investment into inventory.
- Increased Customer Service Satisfaction: Having access to real time customer demand pattern data helps service managers match inventory and inventory levels to customer orders accurately, helping to increase customer satisfaction. Data can be analyzed to predict seasonal trends, spikes or depressions in customer demand to ensure the right levels of inventory are on hand at all times.
- Reduced Costs By Migrating to the Cloud: A Software-as-a-Service (SaaS) approach to IT management means that the cloud-based nature of big data reduces hardware and maintenance costs. It can also be seamlessly integrated to existing systems with a minimum of expense.
Before looking for a specific solution that supports big data operations, it is highly recommended to have a general checkup of the systems already in place as well as of the implemented processes. In the area of inventory, we identified 12 questions one has to examine and answer to determine how well your business manages inventory. Have a look at this guidebook which contains the questions in detail: