production planning & control (ppc)

PRODUCTION PLANNING &

CONTROL

Simple Sustainable Solutions 1

Production Planning & Control

Trainers:

Hakeem–Ur–Rehman

&

Sajid Mahmood

Simple Sustainable Solutions


Outline:


DAY–1:

o What is Production Planning & Control (PPC)?

o Forecasting Methods & Its Applicationso Understanding Data Patterns & It’s Methods

DAY–2:

o Aggregate Production Planning (APP)

o Inventory Management

DAY–3:

o Master Production Scheduling (MPS)

o Material Requirement Planning (MRP)

Hakeem-Ur-Rehman & Sajid Mahmood


What is Production Planning & Control (PPC)?

Simple Sustainable Solutions Hakeem-Ur-Rehman & Sajid Mahmood 3

The highest efficiency in production is obtained by

manufacturing the required quantity of the product, of the

required quality, at the required time, with the best and

cheapest method.

PPC regulates and controls “how”, “where”, and “When” work is to be

done.

PRODUCTION

SYSTEM

Four Factors involve:

o Quantity

o Quality

o Time

o Price

Production Planning

& Control (PPC)

BRAIN


Why Forecasting?


The Effect of Inaccurate Forecasting on the Supply Chain


Forecasting:


“A prediction of future events used

for planning purpose”

Principles of Forecasting:

1.Forecasts Are Almost Always

Wrong (But They Are Still Useful)

2.Forecasts are more accurate for

shorter than longer time horizons

3.Forecasts are more accurate for

groups or families of items rather

than for individual items.

Objective: “Better future Forecast

by Minimizing the error (Actual

Vs Forecasted Demand)”


Types of Forecasting Methods:


FORECASTING METHODS

Little or no quantitative

data available

Quantitative historical data available

Evidence of a relationship between the variable of

interest and some other variable(s)

Quantitative Techniques

Qualitative Techniques

o Market surveys

o Delphi method

o etc.Time Series Models: “Future

is a function of time”

o Moving Average

o Weighted Moving Average

o Exponential Smoothing

o Linear Regression

o etc.

Causal Models: “Future is a

function of ‘any other factors’

other than time ”

o Linear Regression

o Multiple Regression


Forecasting Methods – Demand Patterns Over Time:


Any time series (i.e. data over time) is composed of the following:

DATA = Level + Trend + Seasonality + Cycles + Random Variation

DATA = PATTERN + Random Variation

Time

Qu

an

tity

(a) Level or Horizontal Pattern: Data

follow a horizontal pattern around the mean

Time

Qu

an

tity

(b) Trend Pattern: Data are progressively

increasing (shown) or decreasing

(c) Seasonal Pattern: Data exhibit a regularly

repeating pattern

Time (Quarters)

Qu

an

tity

Time (Quarters)

Qu

an

tity

(d) Cycle: Data increase or decrease over time

(Data patterns created by economic fluctuations)


Time–Series Forecasting Methods: Level or Horizontal Pattern


Averaging Techniques: work best when a

series tends to vary about an average (i.e.

smooth variations around mean)

They can handle step changes or gradual

changes in the level of a series Techniques:o Simple Moving averageo Weighted moving averageo Exponential smoothingTime

Qu

an

tity

(a) Level or Horizontal Pattern: Data follow

a horizontal pattern around the mean

Case Study–1:(Open Excel

Sheet for the case

study data)

Case Study-1.xls

Week Demand Week Demand Week Demand Week Demand

1 415 14 365 27 351 40 282

2 236 15 471 28 388 41 399

3 348 16 402 29 336 42 309

4 272 17 429 30 414 43 435

5 280 18 376 31 346 44 299

6 395 19 363 32 252 45 522

7 438 20 513 33 256 46 376

8 431 21 197 34 378 47 483

9 446 22 438 35 391 48 416

10 354 23 557 36 217 49 245

11 529 24 625 37 427 50 393

12 241 25 266 38 293 51 482

13 262 26 551 39 288 52 484

Week Demand Week Demand Week Demand Week Demand

1 415 14 365 27 351 40 282

2 236 15 471 28 388 41 399

3 348 16 402 29 336 42 309

4 272 17 429 30 414 43 435

5 280 18 376 31 346 44 299

6 395 19 363 32 252 45 522

7 438 20 513 33 256 46 376

8 431 21 197 34 378 47 483

9 446 22 438 35 391 48 416

10 354 23 557 36 217 49 245

11 529 24 625 37 427 50 393

12 241 25 266 38 293 51 482

13 262 26 551 39 288 52 484


Time–Series Forecasting Methods: Level or Horizontal Pattern…


0

200

400

600

800

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

Dem

and

Time (Weeks)

Demand Pattern Over Time

Demand

Case Study–1: Neither seasonality

nor cyclical effects

can be observed

Is Seasonality or Cyclical effects Present?

Is Trend Present?

C oeff. S tand. E rr t-S tat P -value Lower 95%U pper 95%

Intercept 369.27 27.79436 13.2857 5E-18 313.44 425.094

W eeks 0.3339 0.912641 0.36586 0.71601 -1.49919 2.166990.71601

Run Linear Regression to test 𝜷𝟏 in the model 𝑦𝑡 = 𝛽0 + 𝛽1t + 𝜀𝑡

P–Value > 0.05; No Linear trend in

the data

Conclusion: A stationary model is

appropriate (i.e. Averaging techniques).


Time–Series Forecasting Methods: Level or Horizontal Pattern…


AVERAGING TECHNIQUES:

Simple Moving Average: Technique that averages a number

of the most recent actual values in generating a forecast.1

tMA

n

t i

it

A

Fn

Weighted Moving Average: The most

recent values in a time series are given

more weight in computing a forecast.1 ( 1) 1 1...t n t n n t n tF w A w A w A

Simple Exponential Smoothing: The smoothed value Lt is the weighted average of

Ft+1 = 𝐿𝑡 = 𝛼𝐴t + (1 − 𝛼)𝐹𝑡

o The current period’s actual value (with weight of α).

o The forecast value for the current period (with weight of 1–α).

The smoothed value Lt becomes the forecast for period t+1.

An initial “forecast” is needed to start the

process


Performance of Forecasting Methods: Error Performance Measures:


Which one of these forecasting methods gives the “best” forecast?

𝑴𝒆𝒂𝒏 𝑨𝒃𝒔𝒐𝒍𝒖𝒕𝒆 𝑫𝒆𝒗𝒊𝒂𝒕𝒊𝒐𝒏 𝑴𝑨𝑫 = 𝐴𝑡 − 𝐹𝑡

𝑛

𝑴𝒆𝒂𝒏 𝑺𝒒𝒖𝒂𝒓𝒆 𝑬𝒓𝒓𝒐𝒓 𝑴𝑺𝑬 = (𝐴𝑡 − 𝐹𝑡)

2

𝑛

Smaller the Error Performance Measure (i.e MAD, MSE, MAPE) better the method.

𝑴𝒆𝒂𝒏 𝑨𝒃𝒔𝒐𝒍𝒖𝒕𝒆 𝑷𝒆𝒓𝒄𝒆𝒏𝒕 𝑬𝒓𝒓𝒐𝒓 𝑴𝑨𝑷𝑬 =

𝐴𝑡 − 𝐹𝑡𝐴𝑡

𝑛∗ 100

MAPE > 30% Forecast is more or less inaccurate

MAPE < 30% Forecast is reasonably good

MAPE < 20% Forecast is good

MAPE < 10% Forecast is Very good


Performance of Forecasting Methods: Selecting Model Parameters


Use the performance measures to select a good set of values for each modelparameter. For the Simple Moving Average:

o the number of periods (n).

For the Weighted Moving Average:o The number of periods (n),o The weights (Wi).

For the Simple Exponential Smoothing:o The exponential smoothing factor (α).

Excel Solver can be used to determine the values of the model parameters.

Relationship between exponential smoothing and simple moving average:

2k

An exponential smoothing forecast “based on large number

of periods” should have a small “α”


Time–Series Forecasting Methods: Linear Trend Pattern


Time

Qu

an

tity

(b) Trend Pattern: Data are progressively

increasing (shown) or decreasing

Techniques for Trend:

Linear Regression equation

Holt’s Linear Exponential Smoothing

(Trend-Adjusted Exponential Smoothing)

Non-linear trends

o S–Curve trend

o Exponential trend equation

o etc.

Case Study–2:(Open Excel

Sheet for the

case study data)

Case Study-2.xls


Time–Series Forecasting Methods: Linear Trend Pattern…


TECHNIQUES FOR LINEAR TREND:

Linear Regression: Construct the regression equation

based on the historical data available.

The independent variable is “time”.

The dependent variable is the “time-series value”.

𝑦𝑡 = 𝛽0 + 𝛽1𝑡 + 𝜀𝑡

Holt’s Linear Exponential Smoothing: The trend adjusted forecast consists of two

components.

Smoothed factor (i.e. Level)

Trend factor

+1Ft t tL T

Adjust the Level Lt , and

the Trend Tt in each period

Level:

Trend:

Initial Values:


Time–Series Forecasting Methods: Seasonality Pattern


Seasonality: “Seasonality is expressed in terms of

the amount that actual values deviate from the

average value of a series.”

(c) Seasonal Pattern: Data exhibit a regularly repeating pattern

Time (Quarters)

Qu

an

tity

o Multiplicative: Seasonality is expressedas a percentage of the average (or trend)amount which is then used to multiply thevalue of a series in order to incorporateseasonality.

𝒚𝒕 = (𝑻𝒕)(𝑺𝒕)(𝑪𝒕)(𝜺𝒕)

“Many time series exhibit seasonal and cyclicalvariation along with trend”

MODELS OF SEASONALITYo Additive: Quantity (i.e. added or subtracted)

from the time-series average in order toincorporate seasonality

𝒚𝒕 = 𝑻𝒕 + 𝑺𝒕 + 𝑪𝒕 + 𝜺𝒕

THE CLASSICAL DECOMPOSITION TECHNIQUE: To develop an additive or multiplicative model;

The time series is first decomposed to its components (trend, seasonality, cyclical variation).


Time–Series Forecasting Methods: Seasonality Pattern…


Case Study–3:(Open Excel Sheet for the case study data) – Case Study-3.xls

The graph exhibits long term trend The graph exhibits seasonality pattern


Day–1: Review


Why Forecasting?

What is Forecasting?

Time Series Data Patterns

Forecasting Methodso Smoothing Techniques (i.e. Averaging Techniques)

o Linear Trend Techniques

o Decomposition Technique

Forecasting Methods – Performance Measures

Forecasting Methods – Selection of Model Parameters o Using Excel Solver


Outline:


DAY–1:



DAY–2:



DAY–3:





Overview of Production / Operations Planning Activities:



What is Aggregate Operations Planning?


Suppose the figure to the right represents

forecast demand in units.

Now suppose this lower figure represents

the aggregate capacity of the company to

meet demand.

What we need to do is to balance out the

demand and the capacity supply?

Balancing Aggregate Demand and Aggregate Production Capacity – to specify

the optimal combination of:

o production rate; workforce level; inventory on hand …

A poor aggregate plan can result in lost sales, lost profits, excess inventory, or

excess capacity


Aggregate Operations Planning: Understanding Process


Aggregate

Planning


Aggregate Operations Planning: Inputs, Outputs & Goal


Resourceso Workforce

Policy statementso Subcontracting

o Overtime

o Inventory levels

o Back orders

Demand forecast

Costso Inventory carrying

o Back orders

o Hiring/firing

o Overtime

o Inventory changes

o subcontracting

Aggregate

Planning

Total cost of a plan

Production/operation plano Projected levels of:

• Inventory

• Output

• Employment

• Subcontracting

• Backordering

Goal: Specify the optimal combination (tradeoff) of “Production rate, workforce level,

overtime production, subcontracting (i.e. outsourced capacity), inventory / backlog” to

maximizes the firm’s profit over the planning horizon

Product group ((or) family) or broad category (aggregation)

Intermediate-range planning period: 6-18 months


Aggregate Operations Planning: Procedure (Process Steps)


1. Develop the aggregate sales forecast and aggregate planning value.

2. Translate the sales forecast into resource requirements.

3. Generate alternative production plans.

Aggregate Planning value: The term aggregate refers to a line of products (i.e.

Product family), not just one individual product. An aggregate unit is some kind of an

“average” unit.

Example: A firm produces 6 different models of washing machines

Model

Number

Working Hours

Required

Total

Sales (%)

A5532 4.2 32%

K4242 4.9 21%

L9898 5.1 17%

L3800 5.2 14%

M2624 5.4 10%

M3880 5.8 6%

Aggregate Planning value = (0.32)(4.2) +

(0.21)(4.9) + (0.17)(5.1) + (0.14)(5.2) +

(0.10)(5.4) + (0.06)(5.8)

= 4.856 hours per aggregate unit

The manager decides to define an aggregate

unit of production as a machine requiring

some weighted total working hours where the

weights are taken from the total sales (%).


Aggregate Operations Planning: Procedure (Process Steps)…





Example: ABC Electrical performs

three services: cable TV installations,

satellite TV installations, and digital

subscriber line (DSL) installations.

Estimated labor hours per installation:

0.4* 2 + 0.4 * 3 + 0.2 * 4 = 2.8 hours

Estimated supply costs per installation:

0.4 * $15 + 0.4 * $90 + 0.2 * $155 = $73

Estimated Resource Requirements at ABC’s Electrical

ABC expects (i.e. forecasted) total installations for the next three months to be 150,

175, and 200, respectively.







CASE STUDY: XYZ Cabinets Manufacturing Company

XYZ is a manufacturer of several different lines of kitchen and bathroom cabinets that

are sold through major home improvement retailers. XYZ’s marketing vice president

has come up with the following combined sales forecast for the next 12 months:

XYZ Cabinets Manufacturing

Company; Sales Forecast

Case Study: (Open Excel

Sheet for the case study

data) APP-Case Study.xls







Generate alternative production plans: Production Plan Strategies

Pure Production Plan Strategies:

o Level production plan – in which production is held constant and inventory

is used to absorb the differences between production and the sales forecast.

o Chase production plan – in which production is changed in each time period

to match the sales forecast.


Aggregate Operations Planning: Alternative Production Plan


Level vs. Chase Production Plan

Advantages:

o Investment in inventory is low

o Labor utilization in high

Disadvantages:

o The cost of adjusting output rates

and/or workforce levels

Advantages:

o Stable output rates and workforce

Disadvantages:

o Greater inventory costs

o Increased overtime and idle time

o Resource utilizations vary over

time


Aggregate Operations Planning: Basic Formulas & Relationships


Labour hrs. Req. per month = (Labour hr. per unit) * (Month’s forecasted

demand)

# of workers Req. per month = (Labour hrs. Req. per month ) / (Working hrs. per

month per employee)

# of workers in a period = (# of workers at the end of previous period) + (# of

workers at start of the period) – (# of laid off workers at start of the period)

Regular Production per month = (Actual # of workers * Working hours per

month per employee)/(Labour hr. per unit)

Inventory at the end of a period = (Inventory at end of the previous period) +

(Production in current period) – (Demand in current period)

Cost for a period = Output Cost (Reg.+OT+Sub.) + Hire/Lay off cost + Inventory

cost + Back order cost


Aggregate Operations Planning: Alternative Production Plan…


Generate alternative production plans: Production Plan Strategies …

Mixed Production Plan Strategy:

o Combination of Level Production and Chase Demand strategies

o Examples of management policies

• no more than x% of the workforce can be laid off in one quarter

• inventory levels cannot exceed x dollars

Optimization Modeling to Aggregate Production Plan:

o Linear Programming Model – Using Excel Solver


Aggregate Operations Planning: Selecting the Production Plan…


LEVEL CHASE LP Model

Regular Prod. Cost $20,160,000.00 $19,712,000.00 $20,160,000.00

Overtime Prod. Cost 0 $474,260.00 0

Hiring/Layoff costs $16,250.00 $39,000.00 $26,625.00

Inventory Cost $114,800.00 $50,240.00 $20,400.00

Total Costs $20,291,050.00 $20,275,500.00 $20,207,025.00

Key Factors Flat Production

level

Inventory level

grow high

Minimal Inventory

Significant

overtime

production

required in peak

months

Optimum levels

but fractional

values

Assumption

(Start & End

Inventory is ‘0’)

Summary of Alternative Production Plans for XYZ Cabinets Manufacturing

Company:


Inventory Management: What is Inventory?


INVENTORY is the stock of any item or resource used in an organization. These

items or resources can include: raw materials, finished products, component parts,

supplies, and work-in-process

Independent Demand

A

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand

Independent demand is uncertain.

Dependent demand is certain.

starting point of inventory management is customer

demand; Inventory exists to meet customer demand


Inventory Management: Inventory Costs


1.CARRYING COSTS: Cost of holding an item in inventory; Vary with the level

of inventory and the length of time an item is held

Carrying costs include: Rent, Heating, cooling, lighting Security, Record

keeping

2. ORDERING COSTS: Cost of replenishing inventory; Expressed as a dollaramount per order Vary with the number of orders made; As the order size increases, ordering

costs decrease and carrying costs increase Ordering costs include: Purchase orders, Transportation and shipping,

Receiving, Inspection, Handling and storage

3.SHORTAGE COSTS Temporary or permanent loss of sales when demand cannot be met because of

insufficient inventory Customer dissatisfaction and loss of goodwill For internal demand, shortage can cause work stoppage or create delays


Inventory Management: Definition & ABC Inventory Classification


An INVENTORY MANAGEMENT SYSTEM is the set of policies and controls

that monitor levels of inventory and determines:

o what levels should be maintained,

o when stock should be replenished, and

o how large orders should be.

THE ABC CLASSIFICATION SYSTEM: An inventory classification system in

which a small percentage of (A) items account for most of the inventory value.

o In ABC analysis each class of inventory requires different levels of inventory

control

o The higher the value of inventory, the tighter the control

Class A

o 5 – 15 % of units

o 70 – 80 % of value

Class B

o 30 % of units

o 15 % of value

Class C

o 50 – 60 % of units

o 5 – 10 % of value


Inventory Management: ABC Inventory Classification…


Example: The maintenance department for a small manufacturing firm has

responsibility for maintaining an inventory of spare parts for the machinery it

services. The department manager wants to classify the inventory parts according to

the ABC system to determine which stocks of parts should most closely me

monitored. The parts inventory, unit cost, and annual usage are as follows:


Inventory Management: Inventory Control Systems


Continuous Inventory Systems (fixed-order-quantity)

o Constant amount ordered when inventory declines to predetermined level

referred to as reorder point

Periodic Inventory System (fixed-time-period system)

o Order placed for variable amount after fixed passage of time

ECONOMIC ORDER QUANTITY (EOQ) MODELS: To determine how

much to order in a continuous system the economic order quantity (EOQ) model is

used

o BASIC EOQ MODEL: EOQ model is to determine the optimal order quantity

that will minimize total inventory costs

• ASSUMPTIONS:

Demand is known with certainty and is constant over time

No shortages are allowed

Lead time for the receipt of orders is constant

Order quantity is received all at once


Inventory Management: EOQ Model …



Inventory Management: Safety Stock


SAFETY OR BUFFER STOCK

o Inventory level might be depleted at a slower or faster rate during lead time

o Buffer added to on hand inventory during lead time

Reorder Point = (Average daily demand)*(Lead Time) + safety stock

𝑅 = 𝑑𝐿 + 𝑧𝜎𝑑 𝐿

𝑅 = 𝑑𝐿 + 𝑧 𝐿𝜎𝑑2 + 𝑑𝜎𝐿

2


Day–2: Review


What is Aggregate Production Planning?

o Understanding the Process of APP

o Aggregate Production Planning – Procedure

o Aggregate Production Planning – Strategies

o Level Strategy

o Chase Strategy

o LP Model

Inventory Management

o What is Inventory?

o Inventory Costs

o What is Inventory Management?

o ABC Classification

o Inventory Control Systems

o EOQ Model

o Safety Stock


Outline:


DAY–1:



DAY–2:



DAY–3:





Resource Planning for Manufacturing:



Master Scheduling: Def. & Linking with APP


Master Production Schedule (MPS): Time–phased plan

specifying how many and when the firm plans to build each

end item

tracks production output and matches this output to actual

customer

Aggregate Plan

(Product Groups)

MPS

(Specific End Items)

Product–A

Product–B

Product–C

Breaking by Week & by Product

In Reality

o Demand and Production numbers in the

master schedule are unlikely to match the

sales and operations plan exactly.

o Actual capacity requirements might not

match the planning values.

safety stock,

schedule overtime, or

take other measures to make up

difference between the plan and

reality.


Master Scheduling Process & Its Linkages:


MASTER

SCHEDULING

Beginning Inventory

Forecasted

Demand

Customer Order

(Booked Order)

Projected Inventory

Master Production

Schedule

INPUTS OUTPUTS

Master production schedule

linkages:


Developing Master Schedule Record:


Master schedule records track several key pieces of information:

o Forecasted Demand (Estimated demand) 𝐹𝑡

o Booked orders (Confirmed demand) 𝑂𝐵𝑡

o Projected inventory levels 𝐸𝐼𝑡

o Production quantities (Master production schedule) 𝑀𝑃𝑆𝑡• The amount of product that will be finished and available for sale at the beginning of

each week.

o Units still available to meet customer needs (Available To Promise) 𝐴𝑇𝑃𝑡

𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐄𝐧𝐝𝐢𝐧𝐠 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐸𝐼𝑡 = 𝐸𝐼(𝑡−1) + 𝑀𝑃𝑆𝑡 − 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 (𝐹𝑡 , 𝑂𝐵𝑡)

Available To Promise (ATP):

o ATP for first week of the Master Schedule Record:

• 𝐴𝑇𝑃𝑡 = 𝐸𝐼(𝑡−1) + 𝑀𝑃𝑆𝑡 − 𝑖=𝑡𝑧−1𝑂𝐵𝑖

o ATP for any subsequent week in which “MPS > 0”:

• 𝐴𝑇𝑃𝑡 = 𝑀𝑃𝑆𝑡 − 𝑖=𝑡𝑧−1𝑂𝐵𝑖

Case Study: (Open Excel

Sheet for the case study

data) MSR.xls


Material Requirement Planning (MRP):


Materials requirements planning (MRP) Computerized inventory control andproduction planning system

how do firms actually organize things to turn materials into finished products?

Independent Demand

A

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand

Independent demand is uncertain.

Dependent demand is certain.

“Once the independent demand is known,

the dependent demand can be determined”

Dependent demand drives MRP

When to Use MRP?

Dependent and discrete items

Complex products

Job shop production

Assemble-to-order environments


Material Requirement Planning System: Def., Inputs & Outputs


Based on a master production schedule, a material requirements planning

system:

o Creates schedules identifying the specific parts and materials required to

produce end items

o Determines exact unit numbers needed

o Determines the dates when orders for those materials should be released,

based on lead times

Material

Requirement

Planning (MRP)

Master Schedule

Bill of Materials

Inventory Records

Planned order releaseso Work Orderso Purchase Orderso Rescheduling notices

INPUTS OUTPUTS

MRP process consists of four basic steps:

o Exploding the bill of material

o Netting out the inventory

o Lot sizing

o Time-phasing requirements


Material Requirement Planning System: Inputs …


MRP Inputs:

1. Master Production Schedule

2. Bill of Material (BOM)

3. Inventory Record file

2. Bill of materials (BOM): A list of all of the items

needed to produce one unit of a product.

o Product structure tree: Visual representation of

BOM, where all components are listed by levels.

EXAMPLE # 1 (Product Chair): BOM – Product Structure Tree

3. Inventory Record File: A database of informationon every item produced, ordered, or inventoried.Gross requirements, Amount on hand, Lead times, &more....

How many Legs we need

in order to produce 50

Chairs?


Material Requirement Planning System: Inputs…


EXAMPLE # 2 (Product Clip Board): BOM – Product Structure Tree

Time-phased BOM: An assembly chart shows the lead time required to manufacture an item.

Assume Lead time for each item =1 week

How long it will take to assemble a

clipboard from scratch? Forward scheduling: start at today‘s date

and schedule forward to determine the

earliest date the job can be finished.

Backward scheduling: start at the due date

and schedule backwards to determine when

to begin work.


Material Requirement Planning System: MRP Process & Matrix


Exploding the bill of material

Netting out the inventory

o Netting = (on-hand quantities + scheduled receipts) – Gross requirement

Lot sizing: determining the quantities of items produced or purchased

Time-phasing requirements

Lot Sizing in MRP Systems: MRP generates material orders; Order sizes / lots

can be chosen according to various objectives

o Lot-for-lot (L4L): Produce to cover next period

o EOQ: Apply the EOQ approximation for yearly demand

MRP

Matrix:


Material Requirement Planning System: MRP Process & Matrix…


EXAMPLE: School Mate Products




Following the same logic Gross

Requirements in Periods 4 and 5 develop

Net Requirements, Planned Order Receipts,

and Planned Order Releases




Following the same logic, the Lapdesk

MRP matrix is completed as shown


MRP & JIT / Lean Production:


How does

MRP work?Units are PUSHED forward

according to the plan!

What about

JIT / Lean

Production?

Units are PULLED forward

only when needed!

Push / Pull

Decoupling

Point

Units are PUSHED forward to

a certain point.

Final configuration (PULL)

occurs only when the customer

demand occurs.




ANY QUESTION?

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