The world is changing rapidly into the digital era, where every single operation of enterprises, either small or large, is done digitally. One key enterprise operation is procurement, which will assist the enterprise in cost efficiency. Therefore, it is critical to transforming procurement into a digitalized operation to ensure the cost-efficiency mentioned above. Implementing a digital procurement platform can have a powerful and immediate impact on the enterprises’ financial health and competitive agility, create a new level of collaboration between the enterprises and the suppliers, improved the effectiveness of strategic planning which will lead to more effective decision making within the enterprises. Furthermore, by investing in the new digital technology, procurement will help the enterprises to generate a stronger value chain, larger competitive advantage, and an agile resilient business. This article discusses the crucial factors to implement digital procurement in an enterprise.
Table of Contents
Strategy for Digital Implementation
The key strategy of digital procurement is to make purchasing process of goods and services more economical, efficient, and effective, and to increases transparency and accountability as well. Data and automation are the foundation of digital procurement strategy. Digital transformation strategy is a personalized map to bring enormous modifications to the business’ operations, which requires a lot of financial investment, time, and technical expertise. It is better to ensure the teams are being led by experienced technical leaders to reduce the risks. It is also crucial to choose the right digital tools to organize the data from over the years. One of the main reasons behind adopting digital procurement is to eliminate the business’ pain points for the procurement team as well as the customers. The diagram below shows the crucial component for digital procurement implementation.
Top Management Support
The transformation to digital procurement needs top management support. A leader who performs his leadership skill with a correct mindset results in the improvement, cost-effectiveness, and productivity in the planning that digitally transforms the business. With the digital landscape changing so rapidly, a successful leader should be ready to try new technologies, be innovative, adapt, and be flexible in his approach. Leaders must nurture a culture that embraces changes and should be aware that digital procurement makes better visibility, reduces risk and boosts compliance, and also drives more value for the business. Top management support is crucial for a successful digital procurement project as the management gives the right direction with key messages and accurate communications to the enterprises on what is changing in the enterprise road map of the procurement process. By encouraging more users to use online procurement tools, more data will be collected based on the number of transactions done by the users.
Data Gathering and Collation
Data is the main fuel of the digital procurement system and it underpins everything in the company by predicting the needs of the people. Digital procurement is defined by deep and rich data. Procurement is not able to control and oversees all the large scale of data about suppliers, pricing, market, and other key factors that contribute to a more informed business decision, therefore all the available data needs to be transformed into automation as an input so as to have effective daily sourcing operation. The sourcing operation is operated by knowing which good services are available to better meet the needs and determines which suppliers are the right ones. Also, to identify the right price to pay and compiles an intentional strategy to capture more data from internal and external resources. Data can be used to create information and it has intrinsic value, for example, a supplier profile, market overview, or descriptive analytics about the average market price to discover correlations between the attribute of sourcing decision and profit outcome that build analytics-based predictive models which ultimately become AI (Artificial Intelligence).