theory of constraints case study webinar

A case study on reclaiming hidden manufacturing capacity using techniques from “The Goal”

A practical example of the Theory of

Constraints in Food Processing

Ronak Shah

(c) 2010 MBAPDQ, LLC.2

Independent consultant providing:

� Operations improvement

� Supply Chain

� Performance metrics

Background:

� Director of Continuous Improvement, Schnitzer Steel

� Supply Chain Optimization, Intel Corporation

� MBA / M.S. Massachusetts Institute of Technology

The Goal popularized the Theory of Constraints


The Goal (Cliff Notes version)


� Alex Rogo, plant manager at an under-performing metalworking facility likely to be closed by corporate

� Jonah, his old college professor

� Alex learns the “Theory of Constraints”� How the bottleneck processes in a facility set the overall throughput of the facility.

� The impact of unleashing the constraint on plant output

� Coordinating plant production to the drumbeat of the constraint

� Spoiler: Saves the plant, gets promoted, and gets the girl

Herbie: the slowest hiker


Herbie at the back of the line, a half mile behind the lead hiker

Herbie at the front of the line, huffing & puffing away with everyone behind him

Herbie’s load lightened and shared; the whole troop makes good time

Hey, wait!

I am hurrying!

The Client


� Oregon-based food processor targeted at natural and organic consumers.

� Approx. 50 production employees

� Single facility in Oregon

� Now produces Deli Slices, Sausages, pre-packaged Holiday Roasts

Why this project, why now?


� Unrelenting growth had left little time to truly understand existing capacity and opportunities for efficiency

� Approaching an inflection point in scale of operations – flat organizational structure needing some hierarchy

� Considering significant capital expenditure to increase capacity

� Operations manager 100% dedicated to supporting this evaluation and implementing the recommendations

� Dec / Jan is a period of relative calm after holiday rush

Bowl MixerMixes raw ingredients based on recipe. Results in a 500 lb batch of product to be extruded (deli slices, roasts) or linked (sausages).


Tipper-Tie (extrusion)Creates 12lb “Chubbs” for deli slices, extruded and enclosed in a casing


Rack of Chubbs


Oven / FreezerCooks the chubbs, then cools to temperature

(Freezer shown full of Chubb Racks)


SlicerCreates individual stacks of slices for packaging at the Multi-Vac


Multi-VacVacuum packs product, feeds continuously into Lyco. Note that the photo shows sausages, but same process is used for deli slices, frankfurters, links and other products.


Lyco PasteurizerWhen complete, deposits packages into bins to await packaging at the Adco.


Bin of deli slice packages


Adco (filling)Individual packages manually slid into sleeves


Adco (cartoning)Packages first stacked and then cartoned into shipping cases and placed on pallets. 12 or 6 packages per carton, depending on sales market.


Adco (palletizing)Usually 216 – 288 cases / pallet depending upon product and market demand


Theory of Constraints Process


The Easy Way


� Look to see where the inventory is. One would expect that it would build up just before the constraint!

� The freezers and coolers are full of Chubbs…

� So the Slicer is the constraint!

Bowl Mixer

Tipper-Tie

Oven / Freeze

Chubbs

Slicer

Multivac / Lyco

Package Bins

AdcoPackaging

Sausage

Linker

Sausage

Peeling Sausages in totes

Package Bins

The Hard Way


Average of All Days Bowl Chopper Tipper Tie Slicer Peel Multivacs Lyco Pasteurizer Adco Packaging

Total Capacity 2 1 1 1 2 2 1

Daily Demand Cycle Time Needed 29,838 19,568 11,250 13,000 19,500 9,747 21,600

Per-Machine CycleTime 14,919 19,568 11,250 13,000 9,750 4,874 21,600

Hours 4.97 6.52 3.75 4.33 3.25 1.62 7.20

Deli Slices MultiVac R140

Pkgs/Batch 1,300.00 32.00 64.00 6.00 6,109.09 1.00

Cycle Time / Batch 1,200.00 20.00 80.00 5.00 2,340.00 1.44

CycleTime / Pkg 0.92 0.63 1.25 0.83 0.38 1.44

2 Daily Demand 9,000 9,000 9,000 9,000 9,000 9,000

Sausages Multivac R230

Pkgs/Batch 622.86 90.00 4.00 2,400.00 1.00

Cycle Time / Batch 800.00 195.00 8.00 2,520.00 1.44

CycleTime / Pkg 1.28 2.17 2.00 1.05 1.44

4 Daily Demand 6,000 6,000 6,000 6,000 6,000

Why the difference in results?


1. The Slicer is very close to being a constraint - exacerbated by fewer operating hours at this machine

2. Policy to hold some “safety stock” inventory in Chubb form

3. Slicer �Multivac� Lyco� Adco often run in a continuous, hand-to-mouth process due to matched cycle times

Bowl Mixer

Tipper-Tie

Oven / Freeze

Chubbs

Slicer

Multivac / Lyco

Package Bins

AdcoPackaging

Sausage

Linker

Sausage


Package Bins

Causes of downtime at Packaging


Coordination and Staging


Coordination and Staging: Changes


1. Organization change to make one individual at the Adcopackaging machine the “Team Leader”

� Euphemism for “gopher / runner”

� Stages all material: cartons, labels, full bins, empty pallets

2. Recognize that some workers are faster at filling� This is the pacing activity at the Adco

� No longer have everyone rotate through this station

� Up to 8% faster cycle times

What hasn’t changed yet


� No coverage for breaks and lunch� Company culture of socializing with the rest of the crew

� Attempt to schedule product line changeovers during lunch

� Adding a third cartoner when casing mass market product� 6 packages per case vs. 12 = twice as many cases

� Sub-constraint becomes the cartoning activity

� Third person could set up cases to maintain throughput

� No standard work for changeovers� 50% variation in setup time depending on employee

� Risk of incorrect setup resulting in packaging rework

Results on Dec. 16th


� Easily attain 30% increase in production level

� Fairly consistently achieved the day’s shipping schedule needs

� Nature abhors a vacuum; work expands to fill the time you give it

Current production scheduling process


� It’s all in production supervisor’s head!� Matches shipment schedule to existing inventory to start batchesand set the Adco packaging schedule

Current production scheduling process

ProsProsProsPros ConsConsConsCons


� Fast: less than 1 hour / day

� Little WIP inventory since pallets are built to order (BTO)

� One individual accountable for both setting the schedule and achieving it

� Reliant on one individual

� Near limits of scalability

� Limited ability to smooth demand across days

� Limited ability to set challenging goals at constraint

� BTO is not resilient to unexpected production issues

Revisiting the December results


Large day-to-day variations in Goal on production schedule are a result of limitations inherent in making the schedule in your head.

New production scheduling

process


� Treat the plant like two separate factories� Upstream processes operate on a replenishment basis

� Adco builds to orders, but smoothed over a week

Bowl Mixer

Tipper-Tie

Oven / Freeze

Chubbs

Slicer

Multivac / Lyco

Package Bins

AdcoPackaging

Sausage

Linker

Sausage


Package Bins

Kanban System with Buffer InventorySmoothedBTO

Day

F R R/G G O H P I C PS O P B I K

Monday 96 375 0 594 634 1400 354 140 243 36 143 68 458 1442 363

Shipments 0 0 0 0 36 468 36 36 15 36 0 0 36 522 36

Production 600 216 216 50 432

Tuesday 96 375 0 594 598 1532 534 320 228 0 143 68 472 1352 327

Shipments 0 0 0 0 0 1512 0 0 0 0 0 0 432 864 0

Production 800 216 216 432 250

Wed 96 375 0 594 598 820 534 320 228 216 143 68 256 920 577

Shipments 0 0 0 0 216 70 70 216 216 0 0 0 216 432 70

Production 648 648 216 432 216 216

Thurs 96 375 0 594 1030 1398 680 536 228 216 143 68 256 488 507

Shipments 0 0 0 0 248 278 196 54 54 39 36 68 133 301 143

Production 216 216

Fri 96 375 0 594 782 1336 484 482 174 393 107 0 123 187 364

Shipments 0 135 0 0 720 648 360 216 108 216 36 36 305 1116 426

Production 700

Ending Inv 96 240 0 594 62 1388 124 266 66 177 71 -36 -182 -929 -62

Natural

Holiday Deli Slices Jerky Sausages

Multivac hours - - - - -

Adco hours 8.39 7.46 7.70 1.99 2.15

Mon Tue Wed Thurs Fri

Adco Scheduling


Calculates Adco hours based on production schedule

Highlights days when inventory might run low

A simple 2-bin Kanban system


One bin in process at the work cell, another in backup right behind

1st bin returns to warehouse when emptied

2nd bin is at the work cell while 1st is refilled

1st bin returns to the workcellas backup

Work Cell Warehouse

Why implement Kanban?


We have three main needs from a production scheduling system for the upstream (non-bottleneck) processes:

1. Provide a buffer of material to ensure the bottleneck process (Adco packaging) is not starved of material

2. Limit inventory buildup; ie. Over-production of the wrong products

3. Be simple: brainpower is better spent optimizing schedule and performance of bottleneck processes

Kanban meets all these needs

Kanban cards attached to bowl mixer scheduleNote that sausages and other products not implemented using Kanban yet – this is planned for later.


Kanban tag on a Chubb rackThis is attached to an empty truck as Chubbs are placed on it from the Tipper-Tie.


Kanban tag on a bin of deli slice packagesTag is removed from Chubb Rack as it is sliced, Multi-vac’d and Lyco’d. It is then attached to the bin, and stays there as it sits in buffer inventory prior to the Adco packaging operation.


Wall-mounted envelope for finished tagsOnce the bin of deli slices is fully packaged at the Adco, the tag is considered complete and placed into a folder to be picked up by the production scheduler.


Calculating number of Kanban cards


We hold a time buffer of 1 day in addition to safety stock to allow for unexpected manufacturing events.

Safety stock set to 95% confidence level.There is always a day’s worth of cards either waiting in the finished card envelope or at the

mixer waiting to be started.

binper units

days leadtime demand average#

×=cards

Next Steps


� The client is considering new equipment which would move the constraint to other operations.

� New products coming online could change the overall flow of material throughout the plant.

� Continued growth of the company will lead to cultural and organizational changes

The Theory of Constraints, Kanban System, and Build-To-Order casing tools can grow with the client.

Outcomes


� Increased capacity significantly w/o capital investment

� Level loaded production reduces double handling & overtime

� Simplified production scheduling frees up production supervisor’s time to supervise production

Comparing techniques


� Theory of Constraints provided a the right bang-for-the-buck for the client’s needs.

� Had worked with OMEP on Lean in the past, but it didn’t stick.� ToC has co-opted many of Lean’s cultural and employee empowerment mantras over the years.

Questions

Ronak Shah


theory of constraints case study webinar

Business

deli slices

kanban tag

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time

product

individual

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