Business planning is undergoing a revolution. Data availability and technological developments are radically altering planning solution possibilities. Delivery expectations, sustainability imperatives, and unprecedented disruptions are exposing the limited capabilities of traditional supply chain solutions.
Next-generation, cloud-based solutions with machine learning (ML) capabilities can enable process agility, improve forecast accuracy, and provide more granular business insight faster than legacy time-series-based solutions. However, implementing a new planning solution is not without risk. Corporations with multiple monolithic enterprise solutions face huge data structure, connectivity, process, and infrastructure challenges when trying to pivot to new ways of working.
Let’s look at some of the most significant hurdles to be overcome (or better still, avoided altogether) and some solid strategies to deploy when undertaking a digital transformation of your supply chain.
A seriously futile process
System selection is fraught with possibilities that can cause deep regret. One of the most common approaches for whittling down vendors and partners for a new solution is to use a Request for Proposal (RFP). An RFP is often built using a list of legacy-inspired requirements and a capability target gleaned from a magic quadrant of capability.
Various technology and integration companies will respond to RFPs resulting in months—or even years—of presentations, proofs of concept, and negotiations. Ultimately a choice is made and the implementation process begins.
At some point in the process, the pudding will be proofed and it will taste like regret (because there are too many “bakers” around the oven). It’s time to try a new recipe.
Stepping away from the kitchen analogy, here’s what this means for planners who are experiencing a challenging implementation. By focusing on legacy thinking that is driven by sales cycles and building out requirements to fix old methods, you’ll likely end up with capabilities that won’t necessarily suit your business needs.
Instead, try properly partnering with vendors to create planning solution examples. Work with selected technology companies and have bake-offs using real data and clearly defined accuracy, performance, and user experience goals. Don’t wait multiple years to find out the choice was wrong. Instead, take solutions for realistic test drives.
Step outside your planning comfort zone
Many implementation projects fail to deliver their full potential because they are inward-looking. It’s too easy to fall into the trap of simply making a faster version of an existing legacy solution. Instead, try to learn from your peers and industry experts. Go to expositions and conferences and connect with supply planning groups.
Discover the stories and lessons learned from others—especially professionals from different industries. This may sound strange, but it is incredibly revealing to understand the particular process, system, and data challenges that other companies are facing—even if they are in utterly different industries. If you do this, you will discover approaches and possibilities that will offer interesting and alternative strategic pathways.
Strategic thinking cannot be implemented without understanding technological advances and provider capabilities. For example, a knowledge graph-based solution has the potential to deliver truly radical change over relational databases. Cultivate partnerships with vendors to move beyond the slide decks, and actually try the systems out. For a complete 360-degree view, get involved in Project Zebra—the ultimate vision of outside-in planning.
Stop striving for perfection
Trying to build a perfect system the first time is both time-consuming and, to some extent, futile. Futile? Yes, because what you think you want in a planning solution may not be what you really need. This implementation lesson is most keenly felt when trying to build a quality solution.
Let’s paint a picture. Endless requirement gathering workshops will result in a huge list of “must,” “should,” and “could” have priorities. Custom solutions to fit the exacting requirements will suffer from poor data, misinterpreted expectations, and conflicting assumptions. Resources will be crammed, costs will spiral, and the go-live will be a flickering mirage on the far horizon.
This isn’t about project management failure per se but that the cost of all that perfectionism will most likely not be worth it. A new solution design needs to be properly tried in business over time to truly understand what the design should have been. A planning solution with all the possible bells and whistles will need an orchestra and lots of practice time, but they’ll never play the tune you’re thinking of.
Change is constant
Another reason why a long list of requirements and a big build time is a risky implementation endeavor is that demand planning is not static. Even during an implementation, demand planning is often seen as a solid and unchanging process. As a result, it’s treated similar to other back-office functions like order, inventory, and receivables management. But demand planning needs to evolve as data and methods to create and evaluate forecasts change. A decade ago, using social media as a data feed would have seemed both bizarre and impossible but now it is highly desirable and quite achievable.
The problem is that traditional solutions are not designed to change after implementation and project teams can suffer from this mindset too. Expect change! Prepare for additional dimensions, hierarchies, data streams, and processes. Anticipate that the methods to create forecasts will alter and that resources will need to improve their skills over time.
Don’t assume full-suite capability from the start. Instead, prepare to not know what the end state will be. Implement fast and light. Allow for future growth in the design or actually plan to re-implement with learnings. Implementing fast and light will be a lot more cost-effective, the lessons learned about what kind of solution is really needed will be understood far sooner and the next time it will be closer to what is really needed.
In summary, be wary of the traditional RFP process. Instead of a sales cycle, partnering with solution providers and test-drive realistic solutions. Lose the blinkers, think outside-in, and learn from your peers. Don’t try to create a perfect solution the first time out but instead implement light and fast in phases. Plan for change and refinement because there is no end-state.