There is an increasing emphasis on leveraging operational analytics throughout the enterprise, but some businesses can end up less than happy with this strategic investment if they don’t approach it properly. Supply chain professionals can easily avoid some common errors as they implement the business intelligence (BI) solutions that provide those analytics.
Whether it is an entire suite of analytic applications, an inventory management solution or customer relationship management (CRM) analytics, here are five steps that every enterprise should take as it brings new operational analytics solutions online:
1. Set Measurable Objectives
Make sure you set metrics for your BI initiative that take into account your supply chain’s processes, prioritized objectives, and the possible challenges or constraints within your enterprise.
There are usually two types of business objectives that should be addressed in this stage: monetary gains and process gains across the enterprise. In the case of a food manufacturer, the most common monetary gains may come from having better visibility into data on pricing, promotions or expired inventory in order to better manage these areas. For process gains, it may be as simple as being able to distribute information deeper throughout the business to employees who can more quickly take action on what the data is telling them.
Whether companies focus on process or monetary gains or both, it is important to prioritize them first before taking the next step, so the enterprise remains focused on the key objectives throughout the implementation. It is simple to set an objective of reducing inventory by 3 percent in order to realize $5 million in savings on the bottom line. However, it may be better to take a tighter focus by trying to reduce out-of-stocks on “A” items, or even raising inventory slightly to increase customer satisfaction. That might be of more immediate value to the company and easier to do in an implementation phase.
Companies need to understand the strategic value of the operational analytics they are implementing. Yes, vendors will tell them that their suite can do a whole laundry list of things across the enterprise, even throughout the supply chain. However, business goals should be prioritized with an eye on how quickly value can be returned in the eyes of both company executives and end users, so they maintain their focus and interest throughout the implementation.
Every organization has challenges and constraints that it must address early in this first step of the implementation stage. These include the availability and quality of the information already generated by internal business systems, as well as the availability and skill sets of the team members working on the internal business systems, along with the availability and skill sets of the team members working on the project. Organizations have to ensure they are being realistic about what data they have on hand, and they have to be willing to commit the right people with the right knowledge and skills throughout the implementation to make it a success.
2. Maintain Project Governance Throughout the Implementation Phase
The keys here are to realize your business objectives by making sure that all lines of business within your organization are aligned with project objectives, that you have strong executive sponsorship and involvement; and that there are strong project management capabilities available through a structured implementation methodology.
Presumably, the business objectives should already reflect what’s important from a supply chain strategy perspective. That is where strong executive sponsorship is so important to the implementation. The executive sponsor can help ensure the success of the rollout of the new operational analytics by insisting and monitoring that the project team starts small and stays focused on those prioritized business objectives.
Taking a structured implementation is crucial. It begins with the design stage, where the project team defines the scope, timeline and necessary resources. However, during this design stage, they also have to leave enough flexibility within the project to change if needed. Businesses are ever-changing, so the metrics a company may be looking at today may not be the ones it needs to consider six months from now. The new owner of the project after the rollout has to be able to continually tailor and morph the solution into what makes the most sense for the business at that time, not six months earlier.
Another key component of the structured implementation is the conference room pilot. If the design stage has been thoroughly thought through, the conference room pilot should result in just a few tweaks before a full rollout. However, the conference room pilot, which will include a significant number of end users, should take place two to three weeks before the operational analytics go live. That will give the project team enough time to revalidate the changes that were made before it is rolled out to the rest of the end users.
3. Ensure End User Adoption
The biggest challenge to ensuring end user adoption is change management. Identify, understand and address the needs of key stakeholders throughout the company by developing and executing a communications plan early and often. You can enhance this effort by leveraging informal thought leaders within the organization as well.
Unfortunately, some companies make the mistake of throwing the whole project over the wall to IT to deliver on the project. Sometimes it’s because there isn’t enough executive involvement to make people pay attention, or other operational responsibilities pull people from the business side of the house away from the project. That’s a recipe for failure.
If key influencers within the end user community are not invested in the success of the project, it will be much more difficult to get the other business users to embrace the changes these new analytics bring. Going through a process change can be a pretty big deal not just to the people in the primary department, but also to end users in other business areas who may have to input data in a different format. The benefits of change have to be communicated clearly, and management has to enforce the expectation that how they use the new analytics will have a direct impact on their evaluations.
One other audience not to overlook is the informal thought leaders within your organization. This might be the sales managers who are known as the “gadget guys” — people who always have the latest gee- whiz technology on their cell phone or BlackBerry and are respected by their peers for their technology know-how. Involve these informal thought leaders throughout the design phase, the conference room pilot and even communications, so your project can benefit from their sterling reputations within the end-user community.
4. Take a Phased Implementation Approach
Don’t try to implement your new operational analytics across every facet of your enterprise all at once. You can realize your business objectives and a greater return on investment (ROI) more quickly through shorter, easier-to-manage phases implemented throughout the supply chain according to your prioritized business objectives.
For instance, it’s nearly impossible to build a dashboard around 50 different metrics. It is best to pick anywhere from five to 12 key performance indicators (KPIs) that focus on the key business initiatives you have prioritized within your organization. Coming up with too many metrics — especially during an initial rollout — puts end users in an unenviable position where they can’t make a decision because they are looking at 100 different things.
Later on, after a successful implementation, the executive sponsor can help expand the scope of the operational analytics. For instance, the original objective may have been focused on inventory management. Next, the executive sponsor can assist in getting the same solution expanded into other areas, such as sales, marketing or supplier relationship management.
5. Best Practices are Best
In today’s rapidly evolving business technology environment, you should be able to quickly leverage your technology investment out of the box through solutions that incorporate proven industry best practices. If you have to replicate existing practices with new technology that’s more complex in nature, then you have a greater chance of failure.
BI solutions should already have built-in best practices that will enable companies to achieve a rapid ROI. They should include pre-defined data models and views, KPIs, and processes that have already been tested in the field. No, these models and views won’t fill all of the information gaps in your company, but they will get you 70 to 80 percent there. It is relatively simple to adjust these models and views in order to better fit your company or industry.
Businesses today have the opportunity to take advantage of operational analytics throughout their enterprises in ways they have never been able to before. By taking these five steps, they can ensure that the investments they make in these new solutions will result in a positive impact on their bottom line.
This post was written by Frank Bunker