
SAP PP-Predictive MRP, Something to Know!
In this article, we focus on the new proposition of SAP PP-Predictive MRP and explain the interest it.
When reading our title, you might say that we are joking because you think: “In our company, we are currently investing a huge budget to implement an Advanced Planning System, which is planned to go for the next 24 months. This is causing a lot of process transformations and a full redesign of our SCM IT. Our goal is exactly to allow our company to optimize its inventory position. And now, you are pretending you can do the same within a month on a low budget?”
Our answer is Yes! This is possible with a touch of good sense, a good amount of pragmatism, and a bit of programming. This article shall help you to understand our concept. It is a concept that can easily be developed to support the explicit process of demand matching, also known as ATP of Response.
Before going into detail, let us describe the common business situation with figure 1. The supply chain team goes over a complex monthly process to establish the planning to cover the demand in every inventory position. This is the basis of MRPII model.
At the beginning of the planning cycle, the situation looks green almost everywhere, as the plan has been established at the product/location level where any requirement has been covered with a replenishment proposition. Time goes on – and gradually, orange and red colors spot in the grid, denoting supply issues in supply chain execution or short-term planning. Although it may still be seen as green at the aggregated level on the right column, local problems are deteriorating the expected service level. A typical scenario with the situation being roughly good – however, precisely wrong! This is known as a WaterMelon KPI in reporting management which is green outside and red inside.
What does this mean? A simple fact: The operations never fit precisely with planning unless you have a demand plan accuracy of 100%, no replenishment issues, no machine breakdowns, and no transportation failure. Frankly, this will never happen.
To cover this gradually increasing mismatch situation between local demand and local supplies until the next planning cycle tries to re-establish a better and more controlled situation, supply chain planning can adopt various strategies.
That’s it! At least in SAP’s solution portfolio.
Let us come to the core proposition of our article. Once we have assessed the potential standard solutions and their implications in terms of investment and transformation, we may still be looking for something a little magic. The trick which does not engage huge transformation does not cost much and remains flexible enough to cover most of our requirements. The cherry on the cake: It could be implemented in a couple of weeks!
It is not a standard from SAP – it was coded by us in an APO landscape and on S4 landscape. But it would be no issue to run this in an IBP landscape as well. The base concept considers that planning never matches the reality. Planning does not spin fast enough to accommodate. Common issues in the supply chain that disrupt material flow are either demand being wrongly calculated, or demand is not being distributed against the proper inventory point – or many other reason like MRP is not the best in reacting with shortfall situations finding solutions.
In short, the current solution is flexible enough to quickly react to multiple situations. It can be run against different rules, covering different scenarios you are facing. It is easy to refine the scope of balancing to identified issues without replanning all your supply chain. It analyzes in real-time the planning situations and then proposes action plans to close arising gaps. It can work on operational situations as well as planned in the future. Last but not least, the actions are very simple elements that anyone is used to manage!
See below a usual situation with demand sitting in the wrong location and the available inventory or supply plan sitting in another location:
Against this situation, MRP calculates each position individually and detects a shortfall in the top distribution center (DC), propagating the requirement on the factory for 180.
We can agree that it would be better to either relocate the demand of the top DC in the bottom DC or transfer goods from the bottom DC to the upper one (called re-deployment). Obviously, in both cases, there are limits: Demand relocation is not an option since delivery cost and re-deploying goods would crunch the supply chain margin because of the transport (e.g. water or can businesses).
As mentioned, one of the options is to re-deploy goods physically and/or replenishment propositions. Although moving goods physically is never an economical option, it allows preventing customer disruption. This is an exception management option indeed, unless your product costs are high enough to afford that re-deployment is a valid option. On the other hand, moving goods in the future by generating planned transportation requests does not cost anything. And it provides a good assessment of the current planning situation until the next cycle when you will have to confirm another revised replenishment plan.
Whenever possible, this is the smartest way to balance your supply chain inventory. You might be thinking: Why not doing this in demand planning and then download the demand plan again in supply planning? This is possible – however, demand planning is often a monthly cycle, going along a quite rigid processing. So yes, the short term necessity to relocate demand will have to be considered in the next cycle. But for now, in short term, moving demand according to an inventory balancing process looks really efficient.
The solution was developed as SAP ABAP code, running on S4, ECC, or APO. The solution requires a few extensions of master data (product, transportation lanes, rule definitions) – but the technical implementation is less than one day. Maintaining master data extension depends on the scope. Maintaining the selection criteria to run the engine is five days. Project implementation is less than a month as in fact, it consists in setting the appropriate rules vs. the targeted scenario. Finally, a lot of testing to check results and order creation.
From the internal logic of this engine, the program selects product/locations to be optimized according to flexible sources and destination locations (plants) by using flexible criteria like planner, country, category, and hierarchy.
Once selected, the engine reads each product location planning situation using the stock requirement list BAPI. What you interactively do already for sure.
Then based on this real-time data, the engine creates two time-phased matrices (day, week, or month).
Imagine line 1 defines that you want to cover demand with potential excess stock. Then, if the requirement is still not fully covered, continue with line 2 of the rule which allows the engine to use safety stock, etc.
During this demand-to-supply matching processing, the engine can also consider location distance, transportation cost, margin, lot sizes, and priority. Anytime a demand is partially of completely served, the engine creates a proposal.
Finally, all proposals are shown to the planner in a proposal cockpit – each one being also presented in cost and revenue. The planner can then adopt any of the proposals by creating stock transport requisitions or stock transport orders (STOs), representing the re-deployment of goods, or PIRs corrections (Planned Independent Requirements), representing the transfer of demand between locations.
The below screenshots have been taken from the Excel based UI of the application, presenting the master data extensions (stored in S4), the global results (scenarios), and the details actions.
This screenshot shows the list of the last sessions. The flexibility of the solution allows running multiple sessions to assess many different cases without necessarily creating orders directly in operation. The calculation time depends on the number of SKU being selected. Each line represents one calculation session with the corresponding cumulated KPI for several products and locations together. Planners can quickly assess a session is solving of not the targeted issues. Note there is a margin column that is actually a virtual one comparing the cost of transport vs. the additional revenue that the re-deployment would allow.
For each scenario, depending on the scope of the calculation, the solution provides transfer proposals which ones have considered the rule attached to the session. The planner can validate or refuse any proposal. The ribbon allows converting proposals into orders or navigating to S4/ECC.
From a master data perspective, the below screenshots show material master, sourcing master, and balancing business rules.
Located in a Ztable, each product is extended with specific fields that are used in inventory balancing session.
No transportation lane exists in ECC / S4. However the application proposes this master data to define the possible links between locations so that proposition can be elaborated. In the end, the application can determine transport orders or request using this master data.
Conclusion
To conclude this long article, this idea of “Inventory Balancing and Optimization” is nothing complex compared to implementing an APS specifically to solve inventory positioning.
This remains compact as a single program to either run on demand or within the normal supply chain jobs. It is easy to install, to enhance and to operate. The kind of scenarios you can cover are countless: From a base re-deployment engine prior to each MRP run, it can calculate a deployment plan in S4/ECC from factory to DCs, also help in returning obsolete goods from shops to platform (e.g phone and retail business), simulate option to cover explicit products, and even perform a planning run on a rule based basis, not like MRP.
Additionally, the ROI of such a solution is very quick. Our past experiences in the food and cosmetic business were about three to six months – or even less.
If you are pragmatic, you may have already developed such a solution that requires coding skills. If not, let us get in touch and discuss you requirements!
In this article, we focus on the new proposition of SAP PP-Predictive MRP and explain the interest it.
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