Twenty-five years ago, I bought my first personal computer. One of the first applications I installed was a cookbook -- the killer home PC application of the time. The second application was Quicken to manage my finances.
Funds were tight then and I really needed to keep tabs on my spending. Every transaction was meticulously entered, every statement validated against my records. Then I discovered the calendar function.
With the calendar, I was able to schedule my known income (paycheck) and my known expenses (car loan, mortgage, utilities, taxes, groceries, etc.) and Quicken would project my bank balance into the future.
For me, this was game changing! When making discretionary purchases, I could look at my projection to make sure that if I made that purchase, I would have enough money in the bank, not only now, but at the end of the month when my mortgage and car loan came out.
It was my crystal ball, and I regularly asked it questions like: What if I buy that awesome new 27" Sony Trinitron television this week, could I still make my mortgage payment? What if I save my money this month? Or don’t go out for supper on the weekend? Then could I buy it? Today's supply chain professionals need a crystal ball, too.
The only difference is that the decisions are much more complex and far reaching than balancing my finances. For example, if a supply chain professional accepts a new order, can they deliver it on time? If they offer a promotion, can the supply chain support it? When should they shut down the line for scheduled maintenance, and what orders are impacted if they do it now?
If we think about traditional supply chain planning systems, they’re like balancing your check book by hand, which while necessary, is time consuming and error-prone. Planning systems, however, are not designed to allow you to ask ‘what if?’ questions.
Traditional supply chain planning systems have rudimentary scenario support -- at best. Worse, once you have configured a scenario, understanding the impact of a change is very difficult given the silo-based data and the challenges of reporting from these systems. This tends to be why supply chain professionals are still forced to use Excel to model so many of the decisions they need to make.
This isn’t to disparage Excel because it’s a great tool for many things -- managing your supply chain just isn't one of them. If you were to design a crystal ball for your supply chain, what would it look like? What capabilities would be needed to help you anticipate and eliminate risk in your supply chain? How would you answer some of those what-if questions posed above?
- What-if planning
Most traditional ERP systems (if they support scenarios at all) limit them to either a subset of data or to a single scenario, significantly reducing the effectiveness of scenario planning.
Imagine having the ability to instantly create a scenario using a complete copy of all your supply chain data. You’d also be able to make changes to the scenario, and if necessary, create additional scenarios to further explore options and solutions. You also have the ability to understand the implications of the changes you've made to key corporate metrics, and finally, the ability to accept and implement those changes within the business process.
With all this, you would be able to make decisions based on real data, with a true understanding of the impact those decisions will have.
- In-memory analytics
Supply chain decisions can't wait. When a customer wants to place an order, you can't tell them to wait for three and half weeks while you figure out if you can deliver it when they want. Traditional systems are slow because they use disk-based IO to store and retrieve data, which is why they must run batch processes overnight.
Imagine if you can store your planning data in-memory and use highly optimized analytics. Now you can assess the impact of a demand change across your entire supply chain in mere seconds.
How would this impact your decision making process?
- End-to-End visibility
A supply chain is a collection of interconnected nodes. The problem is that with most large companies, these nodes were either managed as discrete units or were obtained through mergers and acquisitions. This means that a given company is often a mosaic of different ERP systems and versions. Changes in demand and supply are often communicated via system to system transfers, often with nightly batch job processing required at each node. A change can take days to work its way from one end of the supply chain to the other.
Imagine if the supply chain data from all these systems could be brought into a single environment. Within this environment, the analytics from each system could be emulated so that decisions made based on this data could be reliably executed in the host environment.
A supply chain is not a single person, it's the combined effort of many. And in most cases, supply chain decisions can't be made unilaterally. It takes the cooperation and agreement of several people to resolve issues and make decisions. Unfortunately, traditional ERP tools do a terrible job facilitating this interaction, leaving planners to rely on e-mails, phone calls, screenshots and again, Excel.
Imagine if the system helped identify the best person to collaborate with on a specific issue, then brought you together in a supply chain centric collaboration space within the tool. Then, as discussions occur and decisions made, these decisions are tracked and the impact of these decisions are displayed in a scorecard.
Now, let's put this all in our supply chain planning system crystal ball. Imagine you’re planning a promotion for a family of parts and that if successful, a 20 percent increase in demand would result. You create a new scenario within the tool and modify the forecast for the time period of the promotion. You instantly get visibility into a new supply chain risk associated with that change – an alert that’s a result of the demand planning system and supply planning system now reside in the same system, use the same software and share the same data.
Using interactive visuals, you can then drill into the data and identify a set of components and a production constraint that is causing the supply chain risk. You discover the issue is the components are at one plant and the constraint is at another. You can see this because data from multiple disparate ERP systems are brought together into a single view. Next, you create a collaboration that brings together the responsible planners who can see the scenario you created and who add scenarios of their own.
By sending messages using the built in collaboration tool, a couple of solution scenarios are proposed. By bringing all of the scenarios together, you compare them against key corporate metrics and discover that one solves the issue with a minor impact to margin, while the other solves it by stealing supply from another product line.
Based on this information, it’s determined that the margin hit is acceptable and the decision is executed. Total time? Hours. Total time using traditional ERP planning? Days. And you still probably didn't know for sure if that was the right decision. How do you make supply chain decisions today? Comment back and let us know!
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