Which of the following statements regarding MRP in services is TRUE? Show
A) MRP can be used in services, but only in those that offer very limited customization. B) Services such as restaurant meals illustrate dependent demand, and they require product structure trees, bills-of-material, and scheduling. C) MRP is for manufacturing only, and it is not applicable to services. D) MRP only works in services for demand that is independent. E) None of the above is true. 34. Which of the following lot-sizing-techniques results in the lowest holding costs?A. lot-for-lotB. EOQC. part-period-balancingD. Wagner-Whitin algorithmAns: A35. What lot sizing technique is generally preferred when inventory holding costs are
extremely high?36. For the lot-for-lot lot-sizing technique to be appropriate Get answer to your question and much more 37. MRP II is accurately described as Get answer to your question and much more 38. Enterprise Resource Planning (ERP) is Get answer to your question and much more A. severely limited by current MRP computer systemsB. not related to MRPC. an advanced MRP II system that ties-in customers and suppliersD. not currently practicalAns: 39. The extension of MRP which extends to resources such as labor hours and machinehours, as well as to order entry, purchasing, and direct interface with customers andsuppliers is40. Distribution Resource Planning (DRP) is Get answer to your question and much more Get answer to your question and much more NAIMMCQ ISDS 3115 Which of the following lot-sizing techniques results in the lowest holding costs? a. lot-for-lot Answer: A How do manufacturing companies currently know when to order how much raw materials?The goal of this article is to present and discuss the most widely used methods adopted by manufacturing companies for deciding when to order how much inbound materials. We also illustrate each method using a concrete example and evaluate the strengths and weaknesses of each method in the context of industrial purchasing. Manufacturing companies purchase large quantities of inbound materials and base components to produce finished goods, most of which are bought from external suppliers. As of today, manufacturing companies use a process named “Material Requirements Planning” to estimate the quantity of each material needed for production and when it is required. The material planner (who is the person in charge of placing the order) must decide how large the order should be, as placing numerous orders each month could be very costly as well as inventory holding costs. Their decision should be made to find an appropriate trade-off between the costs to find a viable financial solution. This optimization problem is called “lot sizing”. As more and more constraints are added to the equation such as quality control or perishability, it becomes quite thorny. In this article, we will present how lot sizing is done as of today and review the lot sizing techniques available to industrial companies to plan their purchases of raw materials and components. 1. Material Requirements Planning: the first step before computing lot sizesDetermining adequate lot sizes is needed to maintain acceptable inventory and service levels. Minimum order quantities demanded by suppliers often blow up inventory levels especially for items with long inventory turn cycles. To determine the lot size that will minimize the costs, industrial companies use Materials Requirements Planning (MRP). Materials Requirements Planning (MRP) is a software-based solution used by every Entreprise Ressource Planned (ERP) system whose purpose is to:
MRP is especially suited for manufacturing environments where the demand of many materials and components depend on products with external requirements.
 The three major inputs of an MRP system are:
After running the Material Requirements Planning, industrial companies know the following:
However, they must now decide when to order what quantity of the desired material in order to minimize costs, which is when lot sizing comes into the equation. 2. What are the different methods applied to determine optimal lot size?The different lot sizing techniques implemented across industrial companies can be categorized into static, periodic, or dynamic. Static lot sizing consists of placing a fixed order quantity or ordering exactly the amount that is needed to cover forecasted demand. Periodic lot sizing groups together the requirements that lie in a determined period. For dynamic lot sizing, the cumulative forecasted demand throughout the entire time horizon is taken into account to determine the optimal order quantities. As time progresses and the production requirements for the new time horizon adjusts, the previously developed planned orders might be adapted. Static lot sizing proceduresStatic lot sizing methods consist of ordering a fixed quantity or the exact amount of requirements for the date needed. 1. Fixed Order Quantity: This method involves ordering a fixed quantity when the reorder point
is reached. The quantity often depends on the supplier-specific constraints. 2. Economic Order Quantity: This formula was developed in 1913 by Ford W. Harris which results in an order quantity that minimizes the total holding costs and ordering costs. 3. Lot for Lot (L4L): It consists of ordering the exact amount that matches the net requirement for each period. 4. Single Lot: It entails ordering the total requirement for the defined period in one go. Static lot sizing procedures are easy to automate but do not provide much flexibility as a high demand variability could result in high inventory holding costs. The Lot for Lot procedure stands out as an exception as inventory is minimized which on the other hand results in extremely high ordering costs. Periodic Lot sizing proceduresPeriodic lot sizing groups several requirements within a time interval together to form a lot. Periodic lot sizing procedures are effective when used with cheap items when inventory cost is low. 5. Period Of Supply (POS): A period of supply such as 3 weeks is specified, for which the net requirements across
that period are ordered together each time. 6. Period Order Quantity: It consists of applying the EOQ model to a subset of the entire period under consideration at a time, where the demand is translated into the average requirement of each subset period. Dynamic lot sizing proceduresDynamic lot sizing considers the effect of cumulative needs across time to determine the best order quantities. As time advances and new production requirements for inbound materials are known, previously developed planned orders may end up changing. This could also be the result of forecast variability. In the examples for each technique provided at the end of this article, we oversimplify the situation for ease of comprehension, by assuming perfectly forecasted requirements which in reality is often not the case. 7. Least Unit Cost (LUC): An order size is determined such that the demand for the next “n” periods will be met, where “n” minimizes the
average cost per unit. 8. Least Total Cost (LTC): In this heuristic technique the optimal solution corresponds to the order plan where the order costs approximate the carrying costs. 9. Part Period Balancing (PPB): This method represents a variation of the LTC approach. It converts the ordering cost to its equivalent in part periods, “the economic part period (EPP)”, by dividing the ordering cost by the cost of carrying one unit for one period. When “the cumulative parts period” which corresponds to the excess inventory x the number of weeks that it is carried, exceeds the EPP, we take it as the optimal
lot size. 10. Silver Meal (SM): Silver and Meal developed this heuristic in 1973, which determines the average cost per period. It first considers a lot that covers the demand for a period and calculates the costs. It then increases the lot size to cover the requirements for another period and calculates the
average cost for that period. One period is added at a time until the average cost per period increases, after which the process stops. 11. Uncapacitated Multi-Supplier Order Quantity Problem with Time-Varying All-units Discounts (UMSOQPVAD): Developed by Horst Tempelmeier in 2002, it is the model
implemented by SAP APO (Advanced Planner and Optimizer) the software of SAP AG. The heuristic starts with a LUC solution and iterates on improvement steps until the solution cannot be further improved given the input parameters. Lots of other methods could be applied to lot sizing like the McLaren’s order moment (MOM), Groff Reorder Procedure, Economic Order Interval (EOI), Maximum Part-period Gain algorithm (MPG), Wagner and Whitin’s (WW)… We will not cover all techniques as some are very difficult to illustrate easily and some are better applicable in the context of production planning. On the table below we compared the different lot sizing procedures, evaluating their strengths and weaknesses according to 8 criteria. 3. How lot sizing optimization should be done today?Many could think it would make more sense to only apply dynamic lot sizing procedures as static and periodic procedures allow a lower level of flexibility resulting in unnecessary high levels of inventory or too many orders. Still, many industrial companies continue to use static and periodic lot sizing techniques like EOQ because of their simplicity to implement, although supplier constraints such as minimum order quantities or rounding values are not considered in the original formula. This implies that an optimum is difficult to reach, especially in terms of costs, resulting in financial hidden losses tied in the order planning processes (Nydick 1989). For dynamic lot sizing techniques, every method could perform well depending on the environment and type of material (Collier 1980). Some methods still managed to stand out as academic studies have shown that the Silver Meal (SM) represents the best trade‐off between cost‐effectiveness and robustness (Jeunet 2000) and the Wagner Whitin (WW) algorithm performs best for the dynamic lot sizing problem and is often used as a benchmark for simpler heuristic techniques (Baciarello 2013, Beck 2015). However, if these methods perform well in academic studies, there are some clear real-world limitations, as for example, the Silver-Meal is not easily expandable to include real-life constraints such as minimum order quantities or multiple quantity discounts (Benton 1996). Furthermore, the Wagner Whitin (WW) algorithm is not used in practice as it is not available on ERP systems due to its complexity and difficulty to implement (Bahl 2009). Given the increased adoption of data analytics into business decision-making, the problem has to be solved algorithmically in order to realize substantial savings as it allows for more flexibility to integrate all the constraints relative to the complex lot sizing problem (Kulkarni 2019). The UMSOPQVAD provides good results integrating most supplier constraints but requires a high level of data requirement in order to perform well and does not consider material perishability. To summarize, many techniques consider the requirements as a starting point to compute an optimal lot size, however perfectly forecasted requirements are not possible. Moreover, supplier constraints such as minimum and maximum order quantities or rounding values often have a significant impact on the final order quantity (Enns 2005). Adding material constraints such as perishability, we realize that constraints should be integrated in the optimization problem from the beginning to ensure optimal purchasing. For all the above reasons, at GenLots*, we undertook the challenge to develop a proprietary algorithm built with machine learning especially for industrial use, considering every real-world constraint as a starting point to find the solution that will minimize the Total Cost of Ownership in less than 5 seconds. The algorithm is easy to integrate with most known ERPs and has already been tested in production with Merck (pharmaceutical industry) effectively saving up to 10% of the total cost per material. * GenLots is the first company fully dedicated to order planning, optimizing lot sizing with machine learning concepts. If you are interested in learning more about GenLots and evaluating how our software can meet your needs, don’t hesitate to get in touch with us at . 4. Illustrations and examples of classical lot sizing techniquesFor all examples we take the following
variables: Static Lot Sizing Techniques1. Fixed Order Quantity It specifies the number of units arbitrarily to be ordered each time an order is placed. Reorder Point: In the example below, the supply planner may arbitrarily decide that an acceptable order quantity is 500 units (which might represent a pallet’s volume) according to their business knowledge. In the first two weeks no orders can be received as it takes two weeks to be received if we place an order as early as in the first period. Our starting inventory is 500 units. In the fourth week, we have requirements of 260 units that will cause the available inventory to go below the safety stock level if we do nothing (280-260<200), that is why we plan an order of 500 units in the first week that will arrive in the third week. We plan another one to be received in week 5 to cover the demand until the end of the horizon.
Order costs: 200€ 2. Economic Order Quantity (EOQ) Q= ((2*7995*100)/(0,17 x 25)0,5 = ( 1’599’000 / 4.25 )0.5 = 613 In the example below, we will need to order this quantity in the first week for it to be delivered in the third week and avoid going below the safety stock level. Similarly, we have to reorder in the fifth week so as to not go under the safety stock level during week 7.
3. Lot for Lot (L4L) For the Lot for Lot procedure, we must order the exact amount that matches the net requirements in each period. In the simple example below, we have a lead time of two weeks which means that we cannot order during the first two weeks but we assume that we had already placed some orders before to cover for the demand. As in the third week, we plan to consume 120 units, we are going to plan an order in week 1 for the third week, of 120 units which correspond to the expected requirements of this period.
Order costs: 600€ 4. Single Lot For the Single Lot procedure, the order quantity is equal to the total requirement and only one order is to be placed. In the example below we place in the first week an order for the third week of 930 units which corresponds to the total requirements for weeks 3-4-5-6-7-8.
Order costs: 100€ Periodic Lot Sizing Techniques5. Period Of Supply (POS) The Period Of Supply procedure computes the lot size will be equal to the net requirements for a given period of time in the future.
Order costs: 200€ 6. Period Order Quantity (POQ) The Period Order Quantity uses the EOQ model for a limited period where the demand is represented as the average requirements per week. The formula used is the following: EOQ / Avg. Period Usage. EOQ: 613 (as computed before) In the example below, the POQ computed above is 4 periods which means we have to consider the demand for four periods for each order. For weeks 3,4,5 and 6 the total projected gross requirements is 815 units, this is why we schedule an order of 630 units in the first week. Available inventory will then go below safety stock in week 7 if we do not reorder, but we need to know what the requirements are beyond the end of the horizon.
Order costs: 200€ Dynamic Lot Sizing Techniques7. Least Unit Cost (LUC) The goal of the Least Unit Cost method is to minimize the average cost per unit. Unit is defined as “one piece of equipment/raw material/component”. We find the order size that will cover the next “n” periods, where “n” is set to minimize the average cost per unit. The Least Unit Cost method is a dynamic lot-sizing technique that adds ordering and inventory carrying cost for each trial lot size and divides by the number of units in each lot size, picking the lot size with the lowest unit cost. For this example we’ll use a different table to compute the optimal lot size according to this technique.
Cum order quantity: cumulative quantity to order each week In the example below the Least Unit Cost technique would recommend ordering 630 units which would cover the projected demand until week 6 as the cost per unit would be 0,27€ per unit per week. After week 6, we start the process once more when we have more visibility over the projected demand of the next few weeks.
Order costs: 200€ 8. Least Total Cost (LTC) The Least Total Cost method is a dynamic lot sizing technique that calculates the order quantity by comparing the carrying cost and the ordering cost for various lot sizes and selects the lot in which these are most nearly equal (Chase 2002). To obtain a result close to the optimum, lots of different scenarios must be analyzed to see which one will minimize the total cost, this is why we will only look at three different examples. a. Two weeks coverage scenario: order less often to balance order costs and carrying costs
Order costs = 300€ b. Two orders
Order costs = 200€ c. Two orders placed differently
The ideal lot size would be 510 units for the third week and 420 units for the sixth week as it is the solution where the order costs get as close as possible to the carrying costs. Order costs = 200€ 9. Part Period Balancing (PPB) This method represents a variation of the LTC approach. It converts the ordering cost to its equivalence in part periods, “the economic part period (EPP)”, by dividing the ordering cost by the cost of carrying one unit for one period. When “the cumulative parts period” which corresponds to the excess inventory x the number of weeks that it is carried, exceeds the EPP, we take it as the optimal lot size. EPP = Order cost / carrying cost per period per unit
1620 > 1223 -> order 815 to cover demand until week 7 On the example below, we place an order during the first week of 815 units which will arrive in the third week. We’ll have to reorder during the sixth week but as we don’t know future requirements, there is no point computing the lot size.
Order costs = 200€ 10. Silver Meal (SM) The Silver Meal heuristic developed in 1973 by Silver and Meal requires determining the average cost per period as a function of the number of periods the current order is to span and stopping the computation when this function first increases. K = order cost = 100€ C (1) = K
C(1) = 100 In week 6: In the example below, we order 510 units in the first week and 420 in the sixth week.
Order costs = 200€ 11. SAP APO heuristics – UMSOQPVAD UMSOQPVAD stands for Uncapacitated Multi-Supplier Order Quantity Problem with Time-Varying All-units Discounts. This particular implementation was developed by Horst Tempelmeier in 2002. It is the model implemented in SAP’s APO (Advanced Planner and Optimizer) software. The heuristic starts with a LUC solution and iterates on improvement steps. The iteration stops when the solution cannot be improved given the input parameters. The improvement steps are about:
→ Back to UMSOQPVAD description GenLots stories August 17th, 2020 | 20 min reading time When the setup cost is low which lot sizing technique is the most appropriate?Periodic lot sizing procedures are effective when used with cheap items when inventory cost is low. 5.
What lot sizing technique is generally preferred when inventory holding cost are extremely high?Question: What lot-sizing technique is generally preferred when inventory holding costs are extremely high? lot-for-lot EOQ.
What is the lot sizing technique that generates exactly what was required to meet the plan a Wagner Whitin B Economic Order Quantity c lot for periodic order quantity?A lot-sizing technique that generates exactly what was required to meet the plan is. the Wagner-Whitin algorithm. part period balancing.
What does MRP system nervousness mean?MRP nervousness is defined as instability and frequent rescheduling of orders in terms of timing and quantity [2] [3] [4]. Later studies extend the concept of nervousness to MPS level, highlighting that nervousness propagates in the planning hierarchy [5] [6] [7].
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Which of the following lots techniques results in the lowest holding costs?
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