The amount of information and improvement possible through big data can be overwhelming. Unfortunately, this may lead some supply chain managers or executives to simply avoid the topic until a more cohesive understanding of its possibilities can be made. Meanwhile, stakeholders do not get to reap the rewards of this new tech trend, and the overall enterprise suffers without realizing it. Yet the majority of companies have not defined a big data strategy, and others are barely starting to notice.
As a result, the simplest way of defining a big data strategy must begin with understanding how it will evolve and affect the company.
How to Get Started with Your Big Data Strategy.
1. Gather Stakeholders, and Explain the Potential ROI of Your Big Data Strategy.
Stakeholders need to be aware of what options are available for boosting the bottom line in any company. As explained by the Boston Consulting Group, supply chain leaders should gather all stakeholders from an organization. In addition, stakeholders should reflect the diversity in an organization. In other words, stakeholders across all organizational silos must be involved. This is where the explanation of big data begins.
2. Identify Areas to Use Big Data.
Big data can easily be implemented across any sector or process in a given business. However, its initial implementation should follow the logical flow of goods and operations within an enterprise.
For example, supply procurement, manufacturing processes and warehousing information should be the first considerations. Alternatively, managers may want to implement big data in areas that would not necessarily require much change management, such as the actual transportation of goods and products after leaving the facility. This can be easily completed for companies opting to work with a third-party logistics provider(3PL) that offers a solution combining traditional enterprise resource planning (ERP) with a transportation management system (TMS) and big data insights.
3. Start Collecting Data Through a Platform of Other Well-Known Resource.
Big data is not simply waiting on information to fall into your hands. It needs to have defined collection points, and depending on the size of your enterprise, you may want to consider using automated data capture points to further amplify manual data entry. This can include, as explained by Russell Reynolds Associates, using the following technologies:
- Radio-frequency identification.
- Bluetooth-connected sensors.
- Hand-held scanners.
Automated notifications via TMS or other connected platforms when an error occurs can also help to reduce inconsistencies or errors throughout the course of work. However, automated data collection points must not replace manual entry entirely. In other words, an organization implementing big data should be open to the ideas of audits, such as those conducted by Cerasis, to ensure all appropriate processes are cataloged or credited as needed. Ultimately, even the best technologies can still make mistakes. albeit more rare for technology to be in error than an actual person.
4. Wait a Minimal Amount of Time for Feedback.
Today’s big data systems are capable of providing nearly real-time data insight into an enterprise’s operation. However, initial implementation will require a little bit of time to collect and analyze information. As a result, you will need to prepare stakeholders and workers appropriately. This may include providing additional training on new platforms or other used resources, emphasizing the importance of using newer technologies in place of traditional standards and ensuring all employees embrace these changes. Unfortunately, technology has a tendency to be associated with lost jobs and fewer opportunities for skilled workers. However, you can change the tone of the conversation by emphasizing that some normal waiting time will be needed between implementation and realizing the potential of big data and automated systems.
5. Follow Recommendations Appropriately.
After recommendations have been made through the use of big data, including recommendations made during bidding processes by 3PLs or other value-added service providers, otherwise known as 4PLs, everyone in your operation must be willing to follow recommendations as described. This can vary widely, but the important thing to remember is that not following such recommendations will increase the likelihood of failure and further losses to jobs or the company. Essentially, it is in the best interest of all parties involved to adhere and adjust to recommendations or needs identified through the use of big data analytics.
6. Continually Monitor the Performance.
Initial findings from your big data strategy may lack value when not used repeatedly and continually to improve an organization’s value. In other words, your big data strategy should reflect an ongoing process of data collection, analysis and action to produce better results than the original starting point. This creates a means of continually monitoring the performance and benefits of big data across an enterprise. Through the use of big data and business intelligence tools, such as the aforementioned TMS, managers and workers can help to determine proper ways of enhancing workflows or operations without necessarily waiting on feedback from others. Essentially, this will reduce the overall costs by creating a way for all staff members to engage in self-management and improvement throughout the course of work.
Change can be hard, but failure to change will be detrimental. By following these six steps, you can maximize the success of implementing big data solutions in your enterprise. Ultimately, your primary competitors are already ahead of you, and others will follow soon. Why wouldn’t you take advantage of today and start working on developing your strategy now?