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1 The Bean Game A HandsOn Game for Understanding the InterRelationships Between a Single Producer and Many Customers Copyright Goldratt Schools 2005 2 Disclaimer Game play important roles in training ID: 807323

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Slide1

Copyright © Goldratt Schools, 2005

1

The Bean Game

A Hands-On Game for Understanding the Inter-Relationships Between a Single Producer and Many Customers.

Slide2

Copyright © Goldratt Schools, 2005

2

Disclaimer

Game play important roles in training:

They can be played to add variety.

Hands on solidifies theory.

Touching, feeling, seeing is effective.

But, don’t play games for no reason. Have a

Goal

or

Need

in mind. Choose a game that guides your group towards what you need to learn. Also consider time, logistics and adequate, “What did we learn” time.

The Bean Game can last 1 hour or a full day. Play wisely and Enjoy!

Slide3

Copyright © Goldratt Schools, 2005

3

How do we deliver now?

Consider a plant that makes four products and delivers them to six customers (other plants or retailers)

Each customer has variable demand each day.

The customer orders once a week and we deliver the next week.

The customer holds Inventory to hold them

over through

the

week until the next delivery.

The plant produces to stock and the delivers to each customer weekly.

The Bean Game

Slide4

2/19/2011

© Goldratt Schools 2005

4

The Bean Delivery System

Customer 1

Customer 2

Customer 3

Customer 4

Customer 5

Customer 6

Us

We

Deliver!

Production

Transportation

Warehouse

Consumption at Customers

Slide5

Copyright © Goldratt Schools, 2005

5

Bean Game Set Up

Work Assignments:

1 Person - Game Leader who Calls the Days

1 or 2 People at Each Customer location

The Customers place orders for future deliver based upon their expectations

of demand and current inventory levels. And they Complain LOUDLY if they

do not have the Inventory they need.

1 Person - The Plant Scheduler to sets up the order of production (which beans are produced first, second and so on) and sets the Batch Sizes

1 Person - Plant Financial Analyst - Reports to CEO

2 People in Plant production. They roll the many die, count up the daily production capacity, produce the beans as directed by the Plant Scheduler and move them to

the Plant Warehouse Inventory

2

People

in the Plant Warehouse

keep

the Beans separated and maintains count (buffer status). The Plant Warehouse loads the

cups

for the deliver truck.

2 Person - Delivery Truck. The delivery truck makes weekly or daily deliveries to all plants and delivers either The Customer’s Order or replenishes the Customer’s buffer to the Max.

Slide6

2/19/2011

© Goldratt Schools 2005

6

Player Positions (20 People)

Customer 1

Two People

Customer 2

Two People

Customer 3

Two People

Customer 4

Two People

Customer 5

Two People

Customer 6

Two People

Us

We

Deliver!

Truckers

Two People

Warehouse

Two People

Production

Two People

CEO

One Person

Factory Finance

One Person

Production Planner

One Person

Print 1 sheet for Factory

Slide7

Copyright © Goldratt Schools, 2005

7

Customer Set-Up

Sold Beans

Weekly Order

Each Customer receives Inventory they order. They roll the dice to see what sells.

Slide8

Copyright © Goldratt Schools, 2005

8

Customer 1 Demand

Products

Garbanzo Pinto Red

Roll Demand Roll Demand Roll Demand

1 0 1 1 1 1

2 0 2 1 2 2

3 0 3 2 3 3

4 4 4 2 4 4 5 5 5 3 5 5 6 6 6 3 6 6

2.5

Average

Per Day

Demand

2.0

3.5

13

Beginning Inventory

13

20

Print 1 sheet for this customer

Slide9

Copyright © Goldratt Schools, 2005

9

Customer 2 Demand

Products

Garbanzo Pinto Red

Roll Demand Roll Demand Roll Demand

1 1 1 2 1 1

2 2 2 2 2 2

3 3 3 2 3 3

4 0 4 5 4 4 5 0 5 5 5 5 6 0 6 5 6 6

1.0

Average

Per Day

Demand

3.5

3.5

8

Beginning Inventory

20

20

Print 1 sheet for this customer

Slide10

Copyright © Goldratt Schools, 2005

10

Customer 3 Demand

Products

Garbanzo Red

Roll Demand Roll Demand

1 1 1 1

2 2 2 2

3 2 3 3

4 3 4 4 5 4 5 5 6 6 6 6

3.0

Average

Per Day

Demand

3.5

20

Beginning Inventory

20

Print 1 sheet for this customer

Slide11

Copyright © Goldratt Schools, 2005

11

Customer 4 Demand

Products

Garbanzo Pinto Red

Roll Demand Roll Demand Roll Demand

1 1 1 1 1 0

2 1 2 1 2 0

3 2 3 1 3 4

4 2 4 6 4 5 5 3 5 6 5 6 6 3 6 6 6 6

2.0

Average

Per Day

Demand

3.5

3.5

11

Beginning Inventory

10

20

Print 1 sheet for this customer

Slide12

Copyright © Goldratt Schools, 2005

12

Customer 5 Demand

Products

White

Roll Demand

1 5

2 5

3 5

4 6

5 6

6 6

5.5

Average

Per Day

Demand

28

Beginning Inventory

Print 1 sheet for this customer

Slide13

Copyright © Goldratt Schools, 2005

13

Customer 6 Demand

Products

Garbanzo Pinto

Roll Demand Roll Demand

1 1 1 1

2 1 2 1

3 2 3 2

4 2 4 2 5 3 5 3 6 3 6 3

2.0

Average

Per Day

Demand

2.0

11

Beginning Inventory

10

Print 1 sheet for this customer

Slide14

Copyright © Goldratt Schools, 2005

14

Factory Statistics

Plant Capacity: 15 Die rolled daily

Min Production:15

Average Prod: 52.5

Max Production: 90

15 52.5 90

Daily Plant Capacity

Daily Customer

Demand Min Ave. Max

Cust 1 2 8.0 15

Cust 2 4 8.0 14

Cust 3 2 6.5 12

Cust 4 2 9.0 15

Cust 5 5 5.5 6

Cust 6 2 4.0 6

Total 17 41 68

17 41 68

Starting Plant Inventory:

At Plant At Customers

Garbanzo 24 63

Pinto 17 53

Red 24 80

White 11 28

Total Inv. ( 76 + 224)= 300

Traditional Operations:

1. Take Orders at beginning of week

2. Deliver Orders from Last Week

3. Operate plant for the week producing Beans in order in the batch sizes shown.

3. Deliver Order after Day 5. Repeat.

Daily Customer Demand

Beginning Batch Size

70

50

65

30

(Batches repeat every 4 days)

Slide15

Copyright © Goldratt Schools, 2005

15

How To Play

1. Each week, each Customer places their order to be delivered at the end of the week.

2. Each day, the Customers roll one fair die for each product to determine Sales. Customers Track Profits Daily.

3. Each day, the Factory rolls 15 fair die to determine production capacity and produces according to Batching Rules.

4. During the week, the Warehouse assembles the Customer Orders from Finished Goods.

5. The Trucks Deliver at the end of the week.

Slide16

Copyright © Goldratt Schools, 2005

16

Beginning Customer Inventory

Products

Garbanzo Pinto Red White

Cust 1 13 13 20 -

Cust 2 8 20 20 -

Cust 3 20 - 20 -

Cust 4 11 10 20 -

Cust 5 - - - 28

Cust 6 11 10 - -

Slide17

Copyright © Goldratt Schools, 2005

17

Order Tracking Sheet Customer _____

To Be Products

Delivered

End Wk Garbanzo Pinto Red White Total

Wk 1 ______ ______ _____ ____ ____ Wk 2 ______ ______ _____ ____ ____

Wk 3 ______ ______ _____ ____ ____ Wk 4 ______ ______ _____ ____ ____

Wk 5 ______ ______ _____ ____ ____ Wk 6 ______ ______ _____ ____ ____

Wk 7 ______ ______ _____ ____ ____ Wk 8 ______ ______ _____ ____ ____

Wk 9 ______ ______ _____ ____ ____

Print 1 sheet per customer

Slide18

Weekly Order Sheets

Customer _______

Week ______

Order Quantity

Garbanzo ___

Pinto ___

Red ___

White ___

Total ______

Print 2 sheets per customer

Customer _______

Week ______

Order Quantity

Garbanzo ___

Pinto ___

Red ___

White ___

Total ______

Customer _______

Week ______

Order Quantity

Garbanzo ___

Pinto ___

Red ___

White ___

Total ______

Customer _______

Week ______

Order Quantity

Garbanzo ___

Pinto ___

Red ___

White ___

Total ______

Customer _______

Week ______

Order Quantity

Garbanzo ___

Pinto ___

Red ___

White ___

Total ______

Customer _______

Week ______

Order Quantity

Garbanzo ___

Pinto ___

Red ___

White ___

Total ______

Slide19

Copyright © Goldratt Schools, 2005

19

Financial Sheet Customer_____

Customers Keep Track of their books.

Wk____ (Number Sold X $20) -(Inventory X$1)= Profit

Day 1 ( ______ x $20 ) -(_____ x$1 ) = ______

Day 2 ( ______ x $20 ) -(_____ x$1 ) = ______

Day 3 ( ______ x $20 ) -(_____ x$1 ) = ______

Day 4 ( ______ x $20 ) -(_____ x$1 ) = ______

Day 5 ( ______ x $20 ) -(_____ x$1 ) = ______

Week Total Profit $_______

Average Daily Profit $_____

Penalty For Lost Sale -$500 EACH!

Complain Loudly!

Print 10 sheets per customer

Slide20

Copyright © Goldratt Schools, 2005

20

Factory Operations

1. The Production rolls the dice and produces the products they are told to produce.

2. The Plant Scheduler decides which product to produce (using the Batch Order Rules). As soon as one batch is completed

the next batch can begin.

Batches repeat about every 4 days.

3. The Truckers deliver products

and return with next weeks orders.

4. The Factory Financial Person

Tracks Profits.

5. The CEO does what? Watches! Thinks! Decides!

Garbanzo

Pinto

Red

White

Beginning Batch Size

70

50

65

30

(Batches repeat every 4 days)

Slide21

Copyright © Goldratt Schools, 2005

21

Factory Financial Sheet

Customers Keep Track of their books.

Wk____ (Number Sold X $20)-(Inventory X$1)= Income

Day 1 ( ______ x $20 ) -(_____ x$1 ) = ______

Day 2 ( ______ x $20 ) -(_____ x$1 ) = ______

Day 3 ( ______ x $20 ) -(_____ x$1 ) = ______

Day 4 ( ______ x $20 ) -(_____ x$1 ) = ______

Day 5 ( ______ x $20 ) -(_____ x$1 ) = ______

Week Total Income $ ______

Weekly Operating Expense $ -2000

Weekly Profit

$ ______

Average Daily Profit $ ______

Penalty For Lost Sale -$500 EACH!

Print 10 sheets for Factory

Slide22

Copyright © Goldratt Schools, 2005

22

Beginning Factory Inventory

Products

Garbanzo Pinto Red White

Factory 24 17 24 11

The factory will produce Beans in order left to right:

70 50 65 30

It takes about 4 days to complete a pass through all four typese of beans, then the process repeats starting with Garbanzo Beans again.

Slide23

Copyright © Goldratt Schools, 2005

23

Factory Warehouse Management

Inventory Products

Garbanzo Pinto Red White Total

Begin ______ ______ _____ ____ ____ Day 1 ______ ______ _____ ____ ____ Day 2 ______ ______ _____ ____ ____

Day 3 ______ ______ _____ ____ ____ Day 4 ______ ______ _____ ____ ____

Day 5 ______ ______ _____ ____ ____

(This includes both those in the stock and those assembled awaiting shipment to customers)

Print 10 sheets for the Warehouse

Slide24

Copyright © Goldratt Schools, 2005

24

Results of Traditional System

We see the difficulty with managing such a system:

Customers act unpredictably.

There is not enough capacity in this system that has plenty of capacity.

Too much of some inventory, too little of other inventory

Costs are high--Profits are low

Everyone is non-cooperative.

Unhappy Factory, Unhappy Customers (Retailers), Unhappy final customers (Stock-outs to those who want to buy the beans)!

Slide25

Copyright © Goldratt Schools, 2005

25

Changing to TOC Replenishment

Create the Plant Warehouse.

Customers hold one day of Maximum Demand for each product.

The Truckers Deliver Daily-Replenishing Each Customer to the Maximum Daily Demand from their Truck (a Rolling Warehouse).

Customers never run out of product.

Customers’ Inventory is dramatically reduced.

Factory Uses Buffer Management and Batch Sizes to return to Max Inventory Level.

Slide26

Copyright © Goldratt Schools, 2005

26

Factory Using TOC Replenishment & VMI

TOC VMI Operations:

1. Set Total System Inventory at Three Days Max consumption.

2. Locate one days Max at the customer's site (except Customer 5 has all three days – this is called Vendor Managed Inventory or VMI)

3. Locate two days Max at the Plant (one day protection and one day paranoia)

4. Each Day produce the number of Beans needed to restore the Plant Inventory Levels to Full Buffer Levels. Start with the deepest Buffer Penetration first. Use a batch size equal to what is needed to return the stock to fill the buffer.

5. If there is excess production capacity(all buffer are full before the end of the day) , produce M&Ms and share them with everyone.

6. At the end of each day, make a milk run and replenish all buffers at the Customers.

7. Repeat daily!

Average Total System Inventory 200 Beans

42

28

14

34

24

12

48

32

16

18

12

6

Hold full Buffer at Customer 5’s Site

Plant Buffers

Garbanzo Pinto Red White

Slide27

Copyright © Goldratt Schools, 2005

27

Truckers and Buffer Size

42

28

14

Garbanzo

Beans

About 1/3 of the Buffer can be held in a Cup to make it easier to Pick-up Beans for the daily deliveries. Three cups for each buffer.

Hold one cup (red one) on the Truck with two replenished at the Factory (at the end of the day make sure the red cup is refilled for the next day from the yellow and green cups).

Slide28

Copyright © Goldratt Schools, 2005

28

The Traditional Delivery System:

What were the Problems?

What did we Learn

Slide29

Copyright © Goldratt Schools, 2005

29

The TOC Replenishment and Vendor Managed Delivery System:

What were the Benefits?

What did we Learn

Slide30

Copyright © Goldratt Schools, 2005

30

Why should we bother to implement the TOC Replenishment-VMI Solution?

What are the Financial Benefits?

Slide31

Copyright © Goldratt Schools, 2005

31

To embellish the Bean Game, Throughput could be added for each bean type.

Total Cost of Inventory Holding could be monitored week to week.

Customers Complaining about missing inventory could carry an actual financial Penalty.

You may allow emergency shipments (to make a special truck delivery)

for a price

.

You could consider the additional cost to Deliver Daily (say $50, even though in most cases, there is probably a solution with minimal extra costs).

The production of M&Ms could carry a nice ‘Free Product’ Throughput.

If needed, Additional Dice can be purchased for Production for a steep price. (Would having 20 Dice in Production make the Traditional environment work better?)

Measurements and Financials (Optional)

Slide32

Copyright © Goldratt Schools, 2005

32

Once you have the basics operating the Bean Game, increase or decrease the demand at the Customers to see how the system can complensate. This could be by adding additional customers, by giving additional Dice to the Customers or by adding products.

If your VMI Implementation will require some type of Consumption Communication, you can increase the Max Level at the Customers inventory and report consumption numbers daily while delivering every other day.

It is interesting to see the difference in required plant buffer to compensate for every other day delivery.

Further Enhancements

Slide33

Copyright © Goldratt Schools, 2005

33

A Workable Procedure:

Replenishment according to

the pattern

of demand

The point of sale is filled up daily according to:

Dynamic Buffer Management (DBM)

Consumption information

Replenishment of goods

Dealing with Distribution

Slide34

Copyright © Goldratt Schools, 2005

34

Introducing the “Buffer”

GREEN

= high inventory

YELLOW

= adequate inventory

RED

= low inventory

Size of the zones depends on the desired service level. Unless there are special circumstances making them equal in size is good enough.

Target

Dynamic Buffer Management

Slide35

Copyright © Goldratt Schools, 2005

35

Buffer Adjustment (Increasing)

48

32

16

If the inventory goes into the Red Zone too often, Increase the Buffer one Zone.

16

64

43

22

16

Slide36

Copyright © Goldratt Schools, 2005

36

Buffer Adjustment (Decreasing)

48

32

16

If the inventory stays in the Green Zone too long, decrease the Buffer one Zone.

16

16

32

21

11

Slide37

Copyright © Goldratt Schools, 2005

37

Answering Questions about Running the Job Shop Game

The Bean Game is self explanatory (if you have a person there who has done it before. But, if you haven’t, then it looks funny. Here are answers to questions posted by smart people (not dumb questions) that may clarify how to play the game. The [answers are within the square brackets.]

1.       What is the real function of the CEO? It is written watch. Watch what? What he must be aware of? [The person acting as the CEO is to the thing CEOs do, "Watch and decide." There is so much to learn, but most people will be caught up in the doing and complaining process.  The CEO represents the person who should be seeing it all (but probably won't, so you will have to lead a discussion at different times to draw out the effects of the system with the CEO person helping you or confirming your comments).]

2.       Slides 8-13 detail the initial inventory for all customers and the demand for 6 days. Do they need to follow the first 6 days these demand values? What the purpose for these initial restricted sales values? Also the week is 5 days and the data is for 6 days. I cannot understand the reason. [The data on those slides represent not the daily consumption but the mapping of the rolls of a fair die to the daily consumption.  For example, the Customer 4 rolls a die to determine the daily demand for Garbanzo beans, then rolls again to determine the Daily demand for Pinto Beans and a third roll for the daily demand of Red Beans.  The customers can only buy the things on their sheet using the demand distribution indicated.  Customer 5 is pretty boring.  Customer 5 sells 5 per day or 6 per day of just white beans.  Playing for 5 days will be enough that about 20% of the time a customer will run out of their initial inventory and should "Complain loudly".]

3.       Slides 8-13 describe also what the sales for each customer. They indicate that some products he is selling and some not. I understand that only those products that have values near them are sold by this customer and the rest no. I.e. not all customers sell all the products only those that have values near them. [Yes.  The type of beans and distributions of demand are carefully set so there is interesting variability and yet capability of the Plant to deal with it and all the customers.  The plant instructions are based on meeting these needs.  I try to do this such that those playing gain adequate insight in just two weeks.  But, if they are not getting it, play the normal way a third week and they will fully understand the chaos caused by traditional weekly orders.] 

4.       Why do we need the demand data for the first week? There is already data for the production batch for the first week and the first week is already running in the game? [I understand where this question comes from.  It's probably already clear from the previous answers.  The idea is to load each customer with a starting supply of beans they sell. At the beginning of the week before they roll any dice, they must place next week's order.  This is where the Beer Game behavior comes into play.  You play for a week. The customers receive their orders from the previous week (some may not get all they requested if the plant doesn't do well).  Before the second week, they must place their order for week 2 before any rolls for week 2.  And, so on.]

5.       The production cycle is around 4 days interval. Whereas the week is 5 days. As the consumption data at the stores is only received at the end of the week. So how the plant decides what to produce at the 5

th

 day of the week? [In the traditional method, the plant is disconnected from the customers. The plant cycle just continues producing in the batches they have "calculated as optimal" for the traditional system.  They go from Garbanzo to White and then immediately begin back at Garbanzo again when the White bean production is complete. This means the plant will create too many of some beans and too few of other beans.  But, It will all average out in the end. Right? Ha!!]

6.       What is the purpose of the data in the top left side of slide 14 which presents averages for the daily sales by customers? This is not even by product just total sales. [This should make sense by now.  This information is the aggregate total expected consumption in numbers of beans of all the customers combined.  This is to show everyone that the plant has more than enough capacity (and average of 11.5 beans more) than the demand.  And the inventory in the system should be sufficient to delivery all customer requests.  The data helps everyone see "How well the traditional system was set-up to deal with all the variation."  Without this information, people will believe the game was rigged."]

7.       In the TOC solution there is no indication to turn each day 15 dices to decide the capacity of the plant for this day as we did for the "normal" way of operation. Is this true or we still needed to use it? [  Yes, this is the daily capacity of the plant.  Roll all 15.  But, you will quickly find the excess capacity of the plant exceeds demand (11.5 per day).  So, you should do something to take advantage of that excess capacity.  I suggest Peanut M&Ms.  That is, after the daily demand is satisfied, the remaining production for that day makes M&Ms. The M&Ms are gathered and passed out to the participants daily as perks to eat along with the beans. Spread the M&Ms in any way you wish.]

Slide38

Copyright © Goldratt Schools, 2005

38

Answering Questions about Running the Job Shop Game (continued)

8.       Is there also written instruction or more material than the presentation you've so kindly sent me? [No.  Just these answers. Feel free to contribute any document you wish to write.]

9.

In the daily demand (slides 8-13) there are some customers for which a daily demand is shown as 0 whereas we can have only 1 to 6 during the simulation. This cannot cause unnecessary questions or reduce the validity of this simulation? [

The

daily demand of the customers is determined by the roll of a fair die (six sides).  Some demands are

sporatic

and some are fairly uniform.  This variation is simulated by rolling the die to determine the demand for that day.  Each Customer has a table showing for each product the translation from dice roll into daily demand.  For example, Customer 1’s demand for Garbanzo Beans seems to be either on or off.  Rolling a 1, 2 or 3 says the demand is zero for that day.  Rolling a 4 is a demand of 4. Rolling a 5 is a demand of 5 and rolling a 6 is a demand of 6.  When there is a demand for Garbanzos from Customer 1, the average is 5 per day.  But, when Customer 1 doesn’t want Garbanzos, the demand is 0.  On average, Customer 1 wants 2.5 Garbanzo Beans every six days.  This is sort of like a customer who buys in large batches.  There is a large order and then they have a lot so they don’t order again for a while. Customer 5 however is much different.  Customer 5 only wants White Beans and the demand is almost constant, either 5 (50%of the time) or 6(50%) per day for an average of 5.5 per day over a period of 6 days

.]

10.

      If there is a shortage for supplying all demand how the available quantity is divided among the customers? Who is not getting the delivery? Or everyone is getting an equal share of the quantity relative to his order quantity to the total quantity? [While there is in fact plenty of capacity at the plant, the behavior of the customers (because of having to order a week in advance and receiving the order a week later) causes some customers to order much more than they need.  This behavior is encouraged by the serious  PENALTY FOR EACH LOST SALE OF $500 ACCOMPANIED BY LOUD COMPLAINING!  And, the roll of the fair die often produces more sixes than expected in one week.  The factory, during the traditional simulation, continues to produce beans according to the optimized batch size schedule.  These behaviors (ordering in larger batches than needed, random variability of the fair die and the batch delivery method) create the same behaviors experienced in the Beer Game.  The plant is operating using “Optimal batch sizes” to produce as much as it can.  But, because of the variations (random and caused) over the week and the cycling of the optimal batch sizes, it’s highly likely the plant will run out of specific types of beans sometime.

 

Dealing with shortages.  Now, that is a great thought! What would you do? Not as a TOC expert, but as the CEO who is standing and just watching?  HUM?  Which customer should get beans when there is a shortage? Should we spread the shortage across all customers? Should we give them to the customer who has the lowest relative inventory? Or to the one who complains the loudest?  Who could be disappointed and potentially suffers a $500 penalty?  Maybe you could auction off the beans to the highest bidder?  This is what happens in reality, so just deal with it!  Have fun with it.  Many in your group will not really understand distribution until this event actually happens.]

11. I have thought and basically it is also possible to use buffer management and daily report in the Beer Game showing the use of TOC Supply Chain solutions using daily reporting and BM. Yes it will take much more time to reach the chaos situation initially and may be also more time in the TOC SC solution run but then it is more general. What do you think? [While there is in fact plenty of capacity at the plant, the behavior of the customers (because of having to order a week in advance and receiving the order a week later) causes some customers to order much more than they need.  This behavior is encouraged by the serious  PENALTY FOR EACH LOST SALE OF $500 ACCOMPANIED BY LOUD COMPLAINING!  And, the roll of the fair die often produces more sixes than expected in one week.  The factory, during the traditional simulation, continues to produce beans according to the optimized batch size schedule.  These behaviors (ordering in larger batches than needed, random variability of the fair die and the batch delivery method) create the same behaviors experienced in the Beer Game.  The plant is operating using “Optimal batch sizes” to produce as much as it can.  But, because of the variations (random and caused) over the week and the cycling of the optimal batch sizes, it’s highly likely the plant will run out of specific types of beans sometime.

 

Slide39

Copyright © Goldratt Schools, 2005

39

Answering Questions about Running the Job Shop Game (continued)

(answer to question 11 continued) 

Dealing with shortages.  Now, that is a great thought! What would you do? Not as a TOC expert, but as the CEO who is standing and just watching?  HUM?  Which customer should get beans when there is a shortage? Should we spread the shortage across all customers? Should we give them to the customer who has the lowest relative inventory? Or to the one who complains the loudest?  Who could be disappointed and potentially suffers a $500 penalty?  Maybe you could auction off the beans to the highest bidder?  This is what happens in reality, so just deal with it!  Have fun with it.  Many in your group will not really understand distribution until this event actually happens.

 

Yes, you can use the replenishment solution with the Beer Game.  Just report daily consumption and ship daily according to actual sales.  The in-between links will soon lower their inventory levels to reduce inventory costs and things would go just fine.  But, with the long linkages in the Beer Game, it may not be so obvious of what is happening.  Everyone will just think, “The system stabilized itself.” ]

12. I am afraid that using the relatively large deviation (1-6) for demand is reasonable but for production availability it might be too large and look artificial. What is you experience with SC management including CEO? Is it well accepted? [Since production is using 15 die instead of one (one averages 3.5 with Standard Deviation of 1.7 which is about 50% of the mean), the production follows a very tight normal distribution (average 52.6 with Standard deviation of about 10%of the mean).  And, the plant aggregates all the rolls (demands) of the customers.  The inventory is intended to smooth out the variations in the rolls over time.  I’ve never heard an objection to the plant production.  Everyone seems to understand that a “Calculated Optimal Batch Size Which Maximizes Production” is a good thing.]

13. What will differentiate the Bean Game from the Beer Game? [Having

the financial sheets for each customer helps a lot in the Bean Game.  The customer have time to easily complete the sheet during play.  They watch

1their

profit come and go daily!   The plant, on the other hand, it s flurry of activity.  Just like real life. The retail has plenty of time and the plant is constantly working.

 

 

The

essence of the TOC Replenishment solution is reducing the time from sale to replenishment.  This ‘time’ includes: Time to order, time to process the order, time to await the queue in production, work-in-process time, time in the plant warehouse, transportation time and shelf stocking time.  All of these things are dramatically increased by batching.

 

With the plant warehouse, the only time is transportation and shelf stocking.  Shelf stocking is dramatically reduced by frequent shipments of smaller quantities.

 

When the plant is using S-DBR and replenishing to the Plant warehouse only those things sold, the plant can easily over produce (not producing things that don’t sell).  The excess capacity in the plant can then be used in very profitable ways: Rush order (for a premium fee), custom orders (for a premium fee), adding additional products (maybe those that didn’t look profitable before but are now free product

).] Enjoy the game. Learn from it. Help others learn. Please contribute suggestions for improvement to: jholt@wsu.edu

Keep Thinking!James R. Holt, Ph.D., PEClinical Professor

Washington State Universiity