What is Business Intelligence and Predictive Analytics?
Business Intelligence is a technology that collects, analyses, and presents historical and current data in a compact and easy-to-read format. This makes it simple for decision makers to understand business data and make timely and informed decisions.
Business Intelligence is inherently capable of handling large amounts of data, it can assist warehouse operators and decision makers in identifying, developing, and creating strategic business opportunities. Since these opportunities are based on factual insights, they can provide businesses with a competitive advantage and long-term stability.
On the other hand, Predictive Analytics is a forward-looking analysis. It uses many techniques from statistics, data mining, predictive modelling, machine learning, and artificial intelligence to analyze current data and make statistical predictions about the future.
Predictive Analytics forecasts data with an acceptable level of reliability. Therefore, forecasts provides us with valuable data. Thus, allowing us to measure, analyse and understand customer behaviour, product performance, or any other aspect. Overall, understanding these aspects will allow companies to find opportunities and better manage risks.
The Value of Business Intelligence and Predictive Analytics in the Warehouse
A warehouse generates large amounts of data. Business Intelligence and Predictive Analytics are the technologies that can help analyze and convert the collected data into useful actionable insights.
Although both technologies have the capability to help warehouse operators understand their warehouse environments better, their difference lies in the timeframe of the insights produced. While business intelligence provides reports on current and historical data, predictive analytics employs business intelligence to predict future performance.
To differentiate clearly, here’s a brief description of each technology and the questions they can answer about the warehouse.
Business Intelligence can produce consolidated reports and real-time dashboards based on collected data resulting from warehouse operations. In turn, this can provide insightful answers on questions regarding historical performance and current operations. Here are some examples of the questions it can help answer:
Predictive Analytics, on the other hand, is capable of forecasting warehouse performance within a certain level of tolerance. It uses historical and current data to determine patterns and predict future outcomes and trends. Here are some examples of the questions it can help answer:
The Current State of Business Intelligence and Predictive Analytics
The S-Curve of Innovation portrayed below displays stages between take off and maturity. The take off and maturity stages represent business intelligence and predictive analytics.
Technologies have displayed their ability to overcome a significant obstacle and have been adopted by the Early Majority. Additionally, because the technologies are sitting between the Take-off and Maturity stages, the risks of implementing them are minimal. They are on track to become widely adopted across the industry.
To support the position of Business Intelligence and Predictive Analytics in the S-Curve of Innovation, MHI’s Annual Industry Report 2020 reveals 57% of their respondents agreed that such analytical tools and technologies will have a potential in disrupting the industry or creating a competitive advantage for businesses. These technologies are among the few that received the highest approval from the respondents.
Technology Adoption Lifecycle
In the case of the Technology Adoption Life Cycle, Business Intelligence and Predictive Analytics are being adopted by the Early Majority. This signifies that decision makers understand the benefits of the technologies and are seeing how these tools can be a fit for their businesses. The technologies have also crossed the Chasm of Death, which denotes that they are likely to be mass adopted by organizations.
In addition to the above, MHI stated in their 2020 Industry Report that 28% of the respondents are using Predictive Analytics today. In 2018, only 19% of the responders said that they were using the technologies, making 2020 a significant two-year jump.
Best Time to Adopt
As is evident from the current state of technology and its adoption lifecycle, Business Intelligence and Predictive Analytics should be adopted as soon as possible in order to give your business a competitive advantage. By doing so, you will be able to better understand the health and behavior of your operations based on facts and ever-changing data. Additionally, these tools will help you to detect rapid market changes and opportunities and respond in a timely manner.
Furthermore, since Business Intelligence and Predictive Analytics have crossed the chasm of death, wide adoption is bound to happen. It is important that you are able to adopt the technologies earlier than the Late Majority and Laggards, as this will give you a significant competitive advantage as compared to those that adopt technologies late in the cycle.
How can You Adopt These Technologies?
TEMPO works closely with its clients to provide a comprehensive operational transformation strategy and implementation plan for warehouses and retail locations through the elimination of wasteful and non-productive activities, increased uses of technology and more efficient staffing models. Schedule a call now to find out how business intelligence and predictive analytics can work for your business.
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