Planning a supply chain is a tough job; it’s where the rubber meets the road and the best laid plans go horribly awry. If you listen closely enough to the complaints and criticisms about an APO implementation, you can actually learn a lot about a company. If you want to really see a reflection of the culture of a company, the best place to look is their master data.
Anyone who has dealt with large scale computer systems for any length of time knows that the data, specifically the master data, is the life blood of the implementation. The master data in an application like APO tells the system how to react to each scenario it encounters. Therefore the setup of this master data and its accuracy is a direct reflection of how well those in the organization know their business processes, and to what degree they are in control of those processes. So what can you really learn from someone’s master data?
1. Are your processes in control? – This is not the time for a wish list. “We’d like our processes to be…” or, “Our processes should work like…” At the end of the day, people running the day-to-day have to use the system and the master data in it. Therefore you need to take a very honest look in the mirror so that your implementation reflects reality. In the supply chain environment, this appears most often in the production rates or yield quantities. Everyone has yearly goals for production quotas or efficiency rates, but until they are demonstrated in a reliable fashion, it’s just a goal, not a reality. If you put goals into the system, your people will spend most of their time replacing those goals with reality. Goals are meant to be aggressive, and until you can demonstrate them reliably, they are just that… goals. In the meantime, use demonstrated data and change them to the goals. Once achieved, your users will thank you.
2. Ability to react to change? – How flexible is your organization? Would you define your typical meeting as a debate or a brainstorming session? APO projects require a large amount of change management. This is easier if your team is confident in what it is they really do and accomplish, as opposed to just knowing what buttons to push. An organization that is confident in what it does and why it does it can react much easier to change than an organization that consists of a bunch of disconnected button pushers. In reality, it should not matter which buttons to push, as long as the tasks and outcomes remain the same. The reaction your team has to this or any monumental system change will tell you the talent level, and the degree of business acumen that they have and how well they understand their job.
3. Are you detail-oriented? – It’s a cliché that the devil is in the details, but when detail-oriented data driven systems drive and direct your day-to-day distance, it is a truth as well. The attention to detail is a critical element that keeps your users in the system or drives them to the world’s most popular alternative, excel. The details, and the attention to detail, is an investment in time that always pays back when you work smarter, not harder (apologies for loading up on the clichés today). If the data in the system is “close enough,” then there is a moment in planning where this level of accuracy just flat does not work. This moment requires a decision by the users: either work around in the system, or work around outside it. Either way, there is a level of effort required to bring reality back into line with the system. There is a moment where the workaround for the N-th time is no longer worth it. At that moment, the user gives up on the system and tires of instating the detail on their own. They create the detail on their own in excel or in their head and it is at this point where the investment starts to be seen as a burden rather than an asset. Detail is needed where the rubber meets the road, whether or not there is that detail will drive your ability to see returns on your investment.
As we see there is a lot to be read in the tea leaves of master data and what it says about your organization. Reality is reality, and the bottom line is that as much of reality that you can model in your system, the more return you will see on that investment, no matter how painful that reality is to face. If the system is loaded with data representing the goals, rather than the here-and-now, those that deal with reality will find their own way of modeling reality in the world; it just might not be your world.