Balancing and Optimizing Inventory Positions in a month, even less!

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With such a title, you must be thinking:

- “Are you kidding Daniel? "

- "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 you pretend doing the same is a month and a low budget!"

The 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 the reader to understand the idea, which is not a trick! This is a concept that can easily be developed, to support the explicit process of demand matching also known as ATP of Response.

The situation:

Before going into detail, let’s describe the common business situation with the above figure. 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 nor transportation failure. Frankly, this will never happen.

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The SAP standard options:

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.

1. Run a WDSR (Weekly Demand, and Supply Review) meeting focusing on issues. This is time-consuming. It repeats every week and involves manual expediting. It generates conflicts and politics and does not provide an optimized situation. Priorities are organized by louder speakers.
2. Iterate faster on S&OP, but this will spoil the base idea of S&OP, which is to focus on the mid-term to long-term. I don’t recommend it as your Supply Chain would become a fire station.
3. Implement an allocation logic in selling, to limit excess demand to what is planned. Seems okay as you would not overcommit to customers preserving your OTIF, however, you may lose opportunities. Better than nothing. To do so in the SAP world, this means you run an aATP (advanced ATP) in S4, in association with an engine that helps to decide the allocated qty per dimension (customer, product, region, priorities, etc) for the next cycle. Typically constrained forecast supply planning would help, like in IBP-Response, although I am not a fan of this as planning does not know everything about sales dimensions and challenges.
4. Implement a Response engine that links demands and supplies according to demand priorities. The difference with bullet 3 above is that your ATP engine is the IBP Response, meaning each demand is assessed individually. It is a no-go for CPG/FMCG businesses as they manage millions of orders. This kind of engine is available in APS like SAP-IBP Response, etc.. It takes 12 months to implement and costs you harm and leg indeed.
5. Run a better planning engine, like IBP-Optimizer, every week to quickly adapt to short-term evolutions on the inventory position. One of the best available system propositions nevertheless this calls for complex IT projects, support, etc.. See my previous articles on optimization
6. Adopt a DDMRP replenishment processing. Certainly the best of the above propositions however, DDMRP is not easily adopted by Supply Chain organizations, anyway leading to major transformations.

That’s it! At least in SAP solution portfolio

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The other solution, "Inventory Balancing and Optimization"

Now let’s come to the core proposition of this 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, this could be implemented in a couple of weeks!

I designed it in the past, with very successful implementations, like for instance for a major european customer saving about 20 mEur within 6 first months of operation. Don’t search in SAP portfolio, this is not a standard from our favorite Software Editor. It was coded and implemented on a project basis only, in an APO landscape and later with other customers on S4 landscape. No issue running this in an IBP landscape as well.

The base concept considers planning never match 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 react quickly 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 realtime the planning situations, 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.

The MRP Failure

Against this situation, MRP calculates each position individually and detects a shortfall in top DC, propagating the requirement on the factory for 180.

We can agree that the wise action plan would better be either to 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 case, there are limits like demand relocation is not an option because of delivery cost, or re-deploying goods crunches the Supply Chain margin because of transport (E.g. water or can businesses).

Re-deploying goods or replenishments

As mentioned, one of the options is to redeploy 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 redeployment as a valid option. On the other hand, moving goods in the future by generating planned transportation requests, does not cost anything, and provides a good assessment of the current planning situation, until the next cycle when you will have to confirm another revised replenishment plan.

Repositioning demand

Whenever possible, this is the smartest way to balance your supply chain inventory.

I am sure your are thinking: why not doing this in Demand Planning and then download again the demand plan in Supply planning. Yep 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 next cycle, but for now in short term, moving demand according to an Inventory Balancing process looks really efficient.

The solution "Inventory Balancing and Optimization" in detail:

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). Technical implementation is less than 1 day. Maintaining master data extension depends on the scope. Maintaining the selection criteria to run the engine is 5’. Project implementation is less than a simgle month as in fact it consists in setting the appropriate rules versus the targeted scenario, then 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), 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 these realtime data, the engine creates 2 time-phased matrices (day, week, or month).

1. The first matrix is called ATB (Available to Balance). Note the ATB qty calculated by the engine is freely defined in rules. You may say in scenario 1 the ATB is only excess stock, whereas in scenario 2 you want to balance in the future therefore include also planned productions or purchase requests. The rule definition lets you select any of the MRP segments provided by the MD04 transaction in ECC/S4, like safety stock, firmed production, planned, etc..
2. The second matrix is called "Requirement", say Req Matrix Again it can selectively be built based on rules. For instance in scenario 1 you want to only consider the sales orders, whereas in scenario 2, you want to include forecast and dependent transport and production demand. At the same time you can define priorities attached to each demand, which helps decide which demand to serve first.
3. Thirdly the engine uses the selected rule to explore and distribute available qty from the ATB matrix to cover Requirement from Req matrix. A rule can be made of several lines. For instance you wish to use first the Excess stock, then consider safety stock before generating new orders (production, transport of procurement).
4. The engine starts processing with line 1 of the selected rules. If the requirement is satisfied it goes to the next product. If there are still uncovered requirements, it goes with line 2 of the rule.

Imagine line 1 defines that you want to cover demand with potential excess stock, then if requirement are still not fully covers, continue with line 2 of the rule which allow 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 then can adopt any of the proposals by creating Stock transport requisitions or stock transport orders (STOs) representing the redeployment of goods, or PIRs corrections (Planned Independent Requirements) representing the transfer of demand between locaitons.

Transactional results

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 depend on the number of SKU being selected. Each line represents one calculation session with the corresponding cumulated KPI for several products and locations together. Planner can quickly assess a session is solving of not ths targeted issues. Note there is a Margin column that is actually a virtual one comparing the cost of transport versus the additional revenue that the redeployment would allows .

For each scenario, depending 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

Master Data setup

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 whith specific fields that are used in Inventory Balancing session

No transportation lane exist 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

Nothing fancy sexy in this screen, only relevant and efficient parameters to instruct the application how to determine the generation of the proposals.

Last words:

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 run either on demand or within the normal Supply chain jobs. It is easy to install to enhance and to operate. The kind of scenario 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, it can also help in returning obsolete goods from shops to platform (E.g phone and retail business), to simulate option to cover explicit products, and even to perform a planning run on a rule based basis, not like MRP.

Useless saying the ROI of such a solution is very, very quick. My past experiences in food and cosmetic business were about 3 to 6 months even less.

If you are pragmatic, you may have already developped such a solution, that requires coding skills. If not, let's get in touch and discuss you requirements.

#SAP #IBP #S4 #SUPPLYCHAIN #RESPONSE #ATP #SOP #MRP #BOP #OPTIMIZATION