ddmrp vs mrp

What is MRP, and why is it the most widely used method of inventory management?

Order Point, in which each point is independent, was the most widely used strategy before MRP. Bimodal inventory distribution, where you have too little of the right inventories and too much of the wrong inventories, as well as overall too much stock to make up for the approach’s lack of adaptability, occurs more frequently as operational environments become more complex (broader SKU list, frequently changing SKU in assortment, and customer demand). Due to the introduction of dependent planning, MRP has become the most widely used inventory management strategy.

The reasoning behind this is quite simple. First, you create a sales forecast, which planners typically obtain through the S&OP process (learn what is S&OP). It is simple to determine how much inventory should be on hand to cover anticipated sales of each SKU when you “know” (predict) how much and when you are going to sell, as well as the rate of forecast error. Most businesses maintain up-to-date WIP and inventory data (Work in Progress). The difference between them will be a requirement for manufacturing, assuming how much and when is needed in comparison to how much is accessible.

But in practice, things are a little more complicated. Because there are multiple SKUs and particular equipment requirements, planners must order their tasks to be “operationally efficient” as well as to meet demand (usually means cost minimization through enlargement of production baches). Now that the job is scheduled precisely in terms of both quantity and duration, determining the material requirements using the BOM (Bill Of Material) is simple. It is called dependent planning for this reason. This indicates that the system only produces supply orders for components when and in the quantity required to meet the manufacturing plan.

Let’s define MRP using the APICS definition:

“A set of techniques that uses bill of material data, inventory data, and the master production schedule to calculate requirements for materials. It makes recommendations to release replenishment orders for material. Further, because it is time-phased, it makes recommendations to reschedule open orders when due dates and need dates are not in phase. Time-phased MRP begins with the items listed on the MPS and determines:
1. the quantity of all components and materials required to fabricate those items
2. and the date that the components and materials are required
Time-phased MRP is accomplished by exploding the bill of material, adjusting for inventory quantities on hand or order, and offsetting the net requirements by the appropriate lead times”.

APICS Dictionary

BOMOn the “BOM and Routings” images for Products A and B, you can see a straightforward depiction of the Bill of Material structure, as well as what machinery is used to transform raw materials into completed goods, as well as how quickly and in what order. Depending on the BOM level, the three boxes icon represents finished goods or stored resources. Each rectangle in the BOM represents an SKU, and each circle with a number represents a particular piece of production machinery. As you can see, it takes 30 days to create finished product A and 14 days to create finished product B.

The corporation must make plans at least for this time frame because the lead periods for supply and production are longer than the customer tolerance time. The S&OP procedure and forecast recalculation need a lot of time and effort. BOM2Additionally, every MRP-run organization receives a fresh result afterward that differs from the prior one, confusing everyone—especially those on the factory floor who were supposed to put it into practice. Therefore, a production schedule is typically created monthly, one to two months in advance, to stabilize the system.

A monthly plan makes management easier since it turns a plan into a vital management tool for running a business. If the sales department sold more or less than expected, if the operational department built as planned or not, etc., it is simple to understand.

You can see a straightforward visualization of MPS in the picture “Production schedule” (Master Production Schedule). A specified volume and number of SKUs are scheduled for manufacture in order each day. Once more, the material requirements for “A” and “B” from the picture “BOM and Routings” are computed using the companies’ lead times and BOM.

If your suppliers, equipment, materials, master data, and forecast are all completely accurate, MPS and MRP are a fantastic solution. Let’s talk about what transpires if it is not the case:

production schedule

Building what was intended rather than what the market demands. An artificially stable and effective manufacturing schedule

Consider yourself a plant planner. After going through the S&OP process, the management team has approved both the production and sales plans for the upcoming month. Because you’ve had enough experience to know that salespeople frequently request changes to the plan, you ask them again: “Is this the way you want me to produce, or do you think something has to be changed? Do you aware that this schedule will be followed for all activities, including the purchase of materials?

At the beginning of each month, you always receive the same straightforward response: “This is the best plan we should follow.”

In addition to taking into account anticipated sales, the production schedule is specifically created to maximize equipment efficiency and save production costs by, for example, cutting down on material waste and idle time. No one in the firm can dispute that it is the finest strategy possible. The KPI management has now assigned will encourage you to carry out the ideal strategy, such as the execution %. Therefore, following the strategy is all that is necessary to earn a bonus and ensure the success of the company.

Day 7’s morning is right now (please refer to the “Production schedule” graphic”). We intend to complete Product “N” and have to lunch Product “M”. After seven days of the scheduled execution, we are getting ready to create product “O” for two days straight. Because we recently received a very profitable order from our client and current inventories are insufficient to satisfy it, we unexpectedly receive a request from the sales department to build items “L” and “K.” Although we still have safety stock, sales of product “O” were exactly as anticipated. Let’s talk about our choices for Day 8:

Option “A”: Construct “O.” We lose a sales order, additional revenues, and possibly a client as a result. As we follow the timetable, however, our KPI indicates that we did very well. At the end of the month, we receive our bonus.

Build “K” and “L” as per option “B”. Material preparation requires more labor, there are more setups, and there is greater idle time and material waste as a result. And the execution rate of the timetable. There is no bonus for planners. When the sales department exceeds its sales goals, it is rewarded.

Option “С”: The planner decides to select choice “B,” but is unable to. MRP views everything as dependent; the system is not designed to construct what was not planned, and there may be a shortage of materials or capacity. A planner was left with only “A” as an option.

Conflicts between corporate functions are made more likely as a result of this circumstance. Planners want to keep up with the timetable, but they can’t or don’t want to do what the sales urge. In most businesses, which again encourage internal strife, it is difficult to identify what the real cause (option “A” or “C”) is. Sales believe that planners are just concerned with their OOE and timetable execution and are unable to see what is beneath their role.

This results in two clear possibilities for solutions: run MRP more frequently to adapt to market changes or improve forecasting to reduce the need for scheduling modifications. Better forecasting frequently becomes the number one manager’s priority in search of answers if more frequent runs produce greater chaos on the shop floor and with material planning, leading to conflicting recommendations after every run. Longer lead times, greater product, diversity, worldwide sourcing, and increased demand all contribute to the complexity of supply chains, which in turn lowers prediction accuracy. There is simply no answer in the pats data for the question of what the future will look like due to the ambiguity, variability, and complexity of the environment in which we work, not because we have forgotten how to construct forecasts.

Bullwhip result Safety stock does not reduce variability; rather, it increases it

The bullwhip effect is defined by APICS as: “An extreme change in the supply position upstream in a supply chain generated by a small change in demand downstream in the supply chain. Inventory can quickly move from being backordered to being excess. This is caused by the serial nature of communicating orders up the chain with the inherent transportation delays of moving product down the chain”. What is not stated in this definition is the bidirectional nature of the effect. Because of this missing element, the most common attempts used to mitigate the effect are efforts to increase forecast accuracy and the use of safety stocks. Safety stock is usually meant to cover the difference between what was expected and what was sold. But when the inventory level lowers even one unit below safety stock it triggers resupply, which, with the use of MOQs, even more, amplifies variability and distortion in the bullwhip effect. It leads the company to a situation where it needs access to inventory to cover these fluctuations or access capacity, which is to build extra stock when needed. Both options are bad for business.

The production schedule cannot be followed even if we have a 100% forecast accuracy

Any planner with knowledge of the shop floor is aware of that. The failure of suppliers might result in the failure of your personnel, equipment, materials, etc. in addition to demand fluctuation. It is also referred to as operational variability. Please have a look at “BOM and Routings Product A” in the image. Let’s assume that each purchased SKU is 95% of the time available for production to simplify the scenario. The remaining 5 % occurs when the supplier is tardy, there are concerns with the product’s quality, etc. In a similar vein, let’s assume that every resource has a 95% reliability rate.

Look at the left part of the picture and let’s calculate the possibility of simultaneous availability of two raw materials for successful equipment “1” processing. It is 0.95 multiplied by 0.95 and multiplied by 0.95 (equipment “1” reliability) which equals 0.86%. Since we have five raw materials in total and 5 consecutive equipment jobs to process it, the overall chance that the shop floor can build “A” on time and in good quality is about 0.95^10, or about 60%.

As any company, we have more than one SKU to produce with limited time, resources, and materials. To do it effectively, the company uses a Master Production Schedule (MPS). In the picture “Production schedule & execution chances” you see a simple example of MPS for 8 days, including the launch of product “A” on the first day.

We`ve just reviewed a number of materials and the working center needed to be available on time and in the right quantity to finish the job perfectly. In our example, the chance of success is about 60%. Let’s assume, that chance of successful schedule execution each day is the same and equals 60%. So what is the chance that everything goes according to plan 2 days in a row? Right: 0.6*0.6=36%. It’s not hard to resume the calculations and understand that the chance that everything goes according to plan 8 days in a row (0.6^8) is less than 2%.

Feel free to use this logic and your data to estimate what is the chance of schedule execution in your company.

It doesn’t matter how soon you arrive at the airport, you still have to wait for the plan. And still, it doesn’t protect you from being late at the final destination, because your plane could be rescheduled due to bad weather conditions or any other reason. The same logic is on the shop floor. If you did your work much faster than expected, in a dependent system it doesn’t matter, because the next working center can’t start the next job before finishing the current. Even more. You create extra WIP that could complicate shop floor logistics and cause delays for finished products. But, if you get late, the next working center could not start work on time and gets late too. That’s the fundamental feature of a dependent system, which could be stated as: ”delays accumulate, gains never do”. 

How is DDMRP different? No bullwhip effect. Buffers are designed especially for bidirectional variability mitigation

The only way to stop the bullwhip effect is to stop the variability amplification and transfer across the supply chain and inside the production. That`s why the DDMRP buffers were designed for.

Red Zone is a safety embedded in the buffer. The job of the Red Zone is to ensure reliable output, or in other words, a high service level for our customers, and protect against both: supply and demand variability.

The job of the Green Zone is to aggregate demand variability and provide relatively uniform orders further down the supply chain. See picture DDMRP buffer.

No need for detailed long-frozen production schedules

The purpose of DDMRP buffers is to decouple dependencies by establishing points of independence with stored resources, capacity, and time at crucial supply chain transition points and inside the BOM structure. Examine the images of the BOM and routings Products A and B in MRP and DDMRP views. The operating strategy is different, but products A and B, the BOM, and the equipment routing path are the same. According to MRP, we must schedule Product A at least 30 days in advance and wait for all related and follow-up processes to complete without any errors or delays. How trustworthy is that supposition? Also, product B has a 14-day return policy.

As DDMRP buffers create points of independence in BOM structure with stored materials, time, and capacity that allows reducing lead time for product A for 14 days and one operation and product B for 5 days and one operation. If lead time is shortened, there’s no need for long schedule freezes. And as we spoke before, the chance that everything would go according to plan was about 60% (0.95^10, because of 10 dependencies with 95% reliability each) for product A. In DDMRP, to produce product A we need to finish only operation “4” because the result of previous operations is already stored in the buffer. Even more, now we need to rely on one final operation, with a reliability of 95%, which is more consistent than 60% in MRP.

The same logic applies to product B. Lead time compressed from 14 to 5 days, and schedule reliability increased from 70% (0.95^7, because of 7 dependencies with 95% reliability each) to 95% (one final operation). That dramatic change in approach makes new results possible:

Without improving the forecast, we require less inventory and achieve higher SL

We all recall from our undergraduate years that increasing forecast accuracy is the only method to simultaneously decrease inventory levels and raise service levels. Since the notion is so deeply ingrained in the sector, many specialists commit their careers to find the “Holy Grail” of supply chains—precise forecasts. They spend the remaining time trying to find the ideal balance because they are aware that an accurate forecast is by definition impossible to achieve. That is yet another industry myth. There is a misconception that says you will be alright if you can find the ideal balance between inventory quantity and service level for a customer. Ignoring the obvious truth that the dependent nature of MRP makes it impossible to anticipate accurately and execute a faultless schedule.

DDMRP’s shorter lead times and more adaptability enable us to maintain less inventory and better and without compromise handle changes in demand or supply. We shall construct what the market demands if the demand changes and the planning priorities change. The key distinction is that we have a DDMRP buffer or simply the ability to store time, material, and capacity, which allows us to truly adjust to market changes. To make the planning system operate, the ideal world is no longer necessary. To be able to carry out our timetable, we don’t need to know in detail the market demand 30 days in advance and can only pray that all of our suppliers and shop floor activities will run smoothly. Since that is what buffers are intended for.

Because of shortened lead times, capacity, and stockpiled resources, we only build what the market demands. No conflict between functions

DDMRP leverages actual sales orders for planning. To assign shop floor operations to meet that demand, the production planner may be the first person in the firm to notice and respond to spikes in orders. There is no schedule, thus there is no need for the sales department to call planners and request revisions. Since demand drives production, there is no need to request that it be adjusted for the market. Demand Driven MRP is the methodology’s term for it.

See the case study of using DDMRP

DDMRP is an inventory management strategy that can adapt to a dynamic and unpredictable environment and be created for the VUCA world. At the same time, it is more user-friendly and effective, setting a new benchmark for the sector. It enables the breaking of a closed-loop when improving forecasts is the only option to boost service and decrease inventory. But when uncertainty and volatility rise, the prognosis cannot be improved. DDMRP improves shop floor operations by bringing consistency and visibility, which will please your employees. And low inventory to please your finance and warehouse teams. High-quality service to satisfy you and your clients.

See how DDMRP can streamline your supply chain Request a demo

Sviatoslav Oliinyk

Co-founder of Demand Driven Institute Representative office in CIS region, business trainer and supply chain management consultant at ABM Cloud. Experienced in business process re-engineering, project management using Lean, 6 Sigma and TOC in retail, distribution and production. Certified expert in Innovative Solutions for Supply Chain optimization, specializing in DDMRP methodology education and implementation. Author of various case studies and business media publications, event speaker, founder and coordinator of business training clubs, practicing expert in corporate culture development, coaching, team building.

Author: Чорна Карина

Co-founder of Representative office of Demand Driven Institute in CIS region, business trainer

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