Lean Sigma Supply Chain » Definitions

Square Root Law – inventory in multiple locations

Lawrence Loucka — Tue, 02 Aug 2011 02:13:36 +0000

Got asked what would happen to inventory when the number of stocking locations change. I thought for a minute and remembered a quick estimate. The Square Root Law states that total safety stock can be approximated by multiplying the total inventory by the square root of the number of future warehouse locations divided by the current number.

X2 = (X1) * √ (n2/n1)

n1 = number of existing facilities

n2 = number of future facilities

X1 = existing inventory

X2 = future inventory

Here’s an example:

Current inventory is 4000 units, 2 facilities grow to 8. Using the square root law the future inventory = (4000) * √ (8/2) = 8000 units.

Demand Profile

Lawrence Loucka — Wed, 01 Jun 2011 16:28:00 +0000

Maslow’s hammer, or a golden hammer is an over-reliance on a familiar tool; as Abraham Maslow said in 1966 in A Psychology of Science, “It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.” So, must every product in every business segment be set up in a one piece flow cell? Or put on kanban with an heijunka to smooth demand? Or run on a rate-based assembly line? Certainly not! One size rarely fits all. But how to know which techniques make sense?

One place to start is to look at customer demand. All lean practitioners know about Takt Time, or the customer drum beat, and is used to match the pace of an operation with customer demand. Takt Time is calculated at Available Time/Demand, and is by definition an average. Customer demand is anything but average, and so we need to understand the variation or range of demand placed on our process.

Here’s an example …

To build a demand profile take the following steps:

Pick the product or product family or business unit of interest.
Determine an appropriate time unit – hourly, daily, monthly.
Gather the true customer demand as best you can. Be careful about using promise dates instead of requested dates, and be doubly cautious of schedules which are often smoothed, filtered, or otherwise manipulated.
Create the graph or time series plot as above.
Now calculate some simple descriptive statistics. In this example the average is 17 with a range of 49 and a standard deviation of 11.

What can we conclude? Should we design our operations control around a demand rate of 17 a day? Is the variation in demand something we can deal with? How?

ABC Analysis: how to

Lawrence Loucka — Tue, 22 Mar 2011 13:13:08 +0000

Make a list of part numbers
Determine total quantity used over some period of time
Obtaining the cost for each part
Calculate usage $ value for each part by multiplying the quantity and the cost
Sort the list from high to low $
Calculate the total usage $ value for all items
Calculate each item’s percent of total usage $ value
Select percentage cut offs for each ABC group, for example:

Here’s an example …

Next steps

Once you have classified your parts you can use this data to drive key materials management activities. For example, coordinating your perpetual inventory cycle counting program – you might routinely verify your Category A parts on a monthly basis but only review your category C parts twice a year.

You might use flow orders, kanban, or VMI for your C parts but require detailed negotiated purchase orders for your A parts.

In a warehouse you might want to be sure the A items are near the shipping dock and the C items are toward the back.

You might even want to take a close look at the C items and purge a few.

The main point is – one size doesn’t fit all parts, choose the materials management approach that best serves each inventory category.

Bullwhip Effect

Lawrence Loucka — Fri, 18 Mar 2011 20:07:00 +0000

The bullwhip effect is the result of uncertainty caused from distorted information flowing up and down the supply chain. The bullwhip effect is caused by fluctuations in information supplied to firms further up the supply chain. Distorted information causes firms to forecast demand incorrectly. Thereby, many unnecessary costs are put upon each of the firms along the supply chain. Nearly all industries are affected! Firms that experience large variations in demand are at risk. Firms that depend on suppliers upstream or distributors and retailers downstream may be at risk. Most firms are affected by the bullwhip effect. The bullwhip effect used to be considered a normal phenomenon. However, recently, many firms have been trying to focus on how to improve communication along the supply chain. The bullwhip effect can inflict many unnecessary costs on business firms. Inventory costs from stored inventory, problems with quality caused from rapid production, overtime expenses for increased employee labor, and increased units being shipped create costs far and beyond normal levels of production. Customers can also lose faith in a firms ability to deliver products. This is because firms are having trouble meeting demand. Likewise, firms often must lengthen lead time for finished goods, which also may discourage customers, which in turn leads to lost sales. In a worst case, incorrect forecasts may entice a company to adjust capacity which could be detrimental to the overall success of the company. To reduce stocked product, retailers may offer sales promotions to customers. If retailers fail to notify firms upstream in the supply chain, these firms may forecast increased sales as legitimate demand. Thereby producing product that was not wanted by the customer in the first place. Furthermore, salesforce incentives may entice selling products to firms to meet targets. This may cause large inventories for the firm, or the firm may cancel the orders, which causes demand fluctuations in the supply chain. Firms upstream in the supply chain may feel that the increased demand may be legitimate and increase production and inventory levels to produce more. However, in reality, the product hardly moved and required a drop in price to be moved off of retailer’s shelves. Each firm upstream in the supply chain will feel the whip effect.

Here’s the classic illustration from The Bullwhip Effect in Supply Chains by Hau L. Lee • V. Padmanabhan • Seungjin Whang, SLOAN MANAGEMENT REVIEW/SPRING 1997.

Time Value Chart

Lawrence Loucka — Fri, 25 Feb 2011 23:32:32 +0000

Determine Total Cycle Time
Determine Queue Times between steps
Create Step segments proportional to the task times
Place steps, queue’s along the line segment in the order that they happen
> Place Value Adding steps above the line
> Place Non-value Adding steps below the line
Draw in feedback loops & label Yield percentages
Sum Activity / Non-activity times
Sum Value / Non-value Times

Overall Plant Effectiveness

Lawrence Loucka — Sun, 27 Dec 2009 22:44:48 +0000

The Eight Major Plant Losses

Shutdown
Production adjustment
Equipment failure
Process failure
Normal production loss
Abnormal production loss
Quality defects
Reprocessing

Waterspiders as continuous improvement innovators

Lawrence Loucka — Tue, 08 Sep 2009 11:34:14 +0000

The term Waterspider or water beetle (mizusumashi in Japanese) comes from the behavior of the insect known in the States as a whirligig, an aquatic animal that skitters around on the top of a pond quickly changing direction as it goes. For a lean enterprise the role of material handlers, expediters, and support staff changes. In the Toyota Production System this is the common name for a person assigned to support a production operation, so that others may focus exclusively on value-added work. The waterspider delivers parts to the other associates in the cell or on the line so that they don’t need to stop to replenish their work stations.

Unlike a ‘floater’, a waterspider is assigned specific tasks, such as replenishing raw material inventories (via milk run), common area clean-up, communicate status, maintain visual metrics, etc… Waterspider duties usually don’t include tasks which take them away from the production area, or detract from their specific, assigned duties (the waterspider is not the ’5S’ person or a ‘fill in’). Think of the waterspider as the ‘race car pit crew’ for the production team, without which it would be impossible to win or even run the race.

Waterspiders quickly become experts in the withdrawal and production kanban system. They can ‘see’ more of the up and down stream flow in real time than most others, and because of this often making it possible to identify and eliminate errors. From recent experience the waterspiders often have a better grip on reality than their managers, planners, and engineers.

Non manufacturing examples abound in restaurants, hospitals, insurance claims processing; serving the folks that add the value isn’t just for manufacturing. In product and software development the role of the program manager is sometimes something like that of the waterspider, except bringing knowledge to the various development team members instead of parts.

Here are a few references:
Single piece flow at ConMed Linvatec
Improving Workflow With Water Spiders at University of Michigan Health System
Inventory management in electronics manufacturing: The Move To Lean
Lean in the Oil Fields

Have any examples you’d like to share?

How to calculate Total Landed Cost?

Lawrence Loucka — Mon, 17 Aug 2009 10:28:49 +0000

There’s the math, and then there’s the data collection effort. First the math:

Purchase Price:

Price paid to seller (which may include some of the following)
INCOTERMS
Payment terms
Exchange rates over time

Transportation and Logistics:

Foreign inland
Line haul
U.S. inland
Accessorials
Insurance
Packaging

Customs and Imports:

HTSUSA (tariff) rate
Merchandise processing
Harbor maintenance fee
Broker fee
Less: Duty Drawback

Inventory Costs:*

Cycle stock
Safety stock
Inventory in-transit

*Inventory costs can vary depending on the INCOTERMS in category 1 (when does ownership of the inventory change) and the way a company values its inventory.

Overhead and Administration:

Sourcing and Supplier Quality staff
Due diligence
Relationship building/travel
Learning curve

Risk and Compliance:

Compliance costs (technology, staff, other)
C-TPAT program costs
Channel Master carbon footprint mandate compliance costs
Insurance costs
Cost of potential risk of supply disruption
Cost of potential risk of damage to reputation Health, Safety, Environment

Did I miss any?

To this add trends and forecasts for the drivers of these factors. Such things as labor rates, social costs, fuel, cap and trade, currency exchange rates. After all it’s tomorrow’s total landed cost that we’re after.

Next, the data collection plan …

Lean Sigma Tools for Supply Chain, part 2

Lawrence Loucka — Sun, 09 Aug 2009 15:28:51 +0000

Here are a few more lean and six sigma tools that can be applied in supply chain. Have any additions, comments, or examples to share?

**Lean Sigma Tools for Supply Chain**
Lean Sigma Tool	Definition	Supply Chain Application
Location Checksheet	A common visual quality data display in manufacturing is to take a product drawing and make a mark or place a sticky dot on the location of a defect or touch up. After a period of time you’ll often see clusters. Then we use good old Pareto and focus our team based problem solving skills on the areas of interest.	Plotting the physical location of inventory accuracy errors can often be a clue for getting to the bottom of and eliminating a significant source of wasted time. Similarly marking the location of packaging damage can help identify problems with overhang, pallet specification, strapping, and handling.
Sampling	Manufacturing process and quality engineers have been taking product and process samples for over 50 year as a routine part of statistical process control or designed experiments. 100% inspection is actually less accurate in quality control than is a well designed sampling plan and the use of descriptive statistics. As an aside the US Census could stand to use more sampling and less door to door canvasing.	A full physical inventory count or ‘stock take’ is also less accurate that a well designed cycle counting program. But even a cycle counting program is a waste of time if the errors discovered aren’t studied for root cause and permanent corrective action taken. Whether its an annual full inventory or a daily cycle count if all we do is adjust ‘the book’ then we aren’t doing anything to improve our future.
Statistical Distributions	The widely known ‘bell shaped curve’ of the normal distribution is often a good approximation of the spread we find in machining operations. Paint thickness, electrical resistance, tensile strength can vary plus or minus around a mean or average. Descriptive statistics such as mean and standard deviation help us understand and describe the behavior of the systems we are studying.	Caution Will Robinson. Playing with statistics without the proper training can be dangerous. Real example: when calculating safety stock and expected inventory we often need to consider the supplier lead time. Like any variable measurement there is always some spread, the expected 10 days could be 9 days or 15. Lead time is almost never bell shaped. Suppliers are rarely early. So which distribution to use? Find a good black belt and give’m a job.
Control Charts	Control Charts are how we display the behavior of a process and help process operators decide when to make an adjustment, stop the process, or start an investigation. We plot data taken from periodic samples and then follow SPC rules to determine if there has been a change in the process since the last sample.	Kanban are containers or cards used to control the replenishment, supply, production of product. The number of Kanban in circulation can be calculated based on the average consumption, replenishment time, and container size. A single card or container then has an expected lifecycle from empty to empty. By periodically sampling the time the container last passed through a ‘tollgate’ we can get an early warning on shifts in demand or replenishment time, hopefully in time to avoid a stock out.
5 Whys	First impressions are sometimes wrong, so when we are brainstorming or investigating a situation we’ll ask about the cause of the cause of the cause. A method for pushing our thinking beyond superficial solutions that don’t really solve the problem.	Took 20 minutes to get started picking this morning. Why? Because the printer was jammed? Why was the printer jammed? I guess the rollers were dirty. Why where the rollers dirty? … You get the idea? We keep asking Why until we get to something we can do something about like adding a weekly printer maintenance task to our TPM schedule and assigning responsibility for doing it.
Pull Systems	Trying to predict (forecast) what to make and when is tough to do in many industries. Toyota found great advantage in only making what was needed when needed, that is to replenish only what was consumed. Ideally a supplying operation would hand off one piece at a time to the down stream consuming operation. But when supplier and customer can’t be in close physical proximity we need some way to communicate what is needed and when. 2 Bin, kanban, FIFO flow lanes are just a few types of pull systems common in manufacturing.	Some have tried using pull thinking in distribution inventory management, only replacing stock at customer facing warehouses when product is shipped out (Toyota accessories for example). The traditional approach is to forecast the demand and then make or buy a batch large enough to cover the future demand, and hope you didn’t plan too much or too little. Pull works well in some industries and not at all in others. Most warehouses regardless of industry can use pull techniques for resupply of packaging, fresh pallets, wave picking period.

Lean Sigma Tools for Supply Chain, part 1

Lawrence Loucka — Sat, 08 Aug 2009 00:24:15 +0000

Not all of the lessons from Toyota and Motorola translate well into health care, project management, product development, services but many easily do. Here’s a partial list for supply chain. Have any additions, comments, or examples to share?

**Lean Sigma Tools for Supply Chain**
Lean Sigma Tool	Definition	Supply Chain Application
Brainstorming	Generate a wide range of ideas around any topic.	Why not get the warehouse pickers together from time to time to engage them in a discussion on safety, accuracy, or productivity improvements?
Affinity diagrams	Sort the post-its in to logical groups, and give each cluster a name.	Start or end of shift crew meetings can have a team problem or improvement board. Sorting suggestions, brainstorm ideas, or process defects in a public forum is a great way to engage the warehouse or office.
Multivote	One way to prioritize or narrow down a list of alternatives.	Instead of the squeaky wheel, or the boss’ mandate, allowing the folks to set their own priorities for continuous improvement is one way to foster engagement and buy-in.
Process mapping	Come in a variety of styles: flow charts, swimming lanes, spaghetti, etc. A visual model of the process.	Helpful training aid. For many a flow chart is easier to comprehend than a standard operation procedure text.
Process observation	Gain a deep understanding of a process in action by planning what you want to capture and how you plan on doing it. Most processes have too much going on all at one to be able to ‘see’ what’s really happening, so we focus on one ‘actor’ at a time and usually start by watching what happened to the product or service, then is a separate session observe the machines or technology, and then only after really understanding product and process to we observe the people and what they are doing. Reason? People are almost always victims of the processes and products others designed.	In supply chain there are a number of challenges. First hurdle is recognizing that there is a process. What is the product or service supply chain provides? Is it movement of goods or processing of information or both?
SIPOC	Supplier, Inputs, Process, Outputs, Customer – a visual table or chart to help define process boundaries and stakeholders.	Every new WMS or TMS project should start with a charter, project plan and a SIPOC to get all the players calibrated on who is who and why. Surprising the confusion often found around understanding who the customer is and what happens up and down stream.
Spaghetti map	Stable yourself to a order or component and follow it through the process, always enlightening, often embarrassing when plotted on a facility layout	Pick path maps often show problems: location inaccuracies, split lots, poor slotting. Your WMS may direct traffic, even if it does it can be worthwhile to follow a picker around and watch for dead ends, reversals, treasure hunts.
Swim lanes	Flow chart arranged with rows or columns to show functional handoffs.	From customer order through sourcing, planning, scheduling, receiving, putaway, pick, pack, ship the number of times the order and product are touched, adjusted, queued, handed off, and acted on is the start at recognizing waste and variation in supply chain management.
VA Analysis	Breaking down a process into activities and then deciding if the customer would think each task was valuable.	Most of Supply Chain is non value added. Just moving product from here to there doesn’t change the product. Some will argue that the end customer is willing to pay to move product, so any activity that doesn’t move the product closer to the customer is waste. Does the customer care if you have to inspect the paperwork, or put the pallet in and out of a rack?
7 Wastes	A way to categorize non value-added activities and help us see waste: overproduction, defects, transportation, waiting, inventory, motion, processing. Also known as ‘muda’.	Overproduction – unnecessary packaging Defects – inventory record errors, shipping damage, mislabeled Transportation – shipping from the wrong DC Waiting – queuing up orders Inventory – excess, slow moving, obsolete Motion – rearranging a split pallet, reaching for supplies Processing – unnecessary tasks
Check sheets	Simply a list of tasks, hopefully unambiguous and logically sequenced. A memory aid.	Wouldn’t want an airplane pilot to take off with out running through the preflight checklist, why conduct a physical inventory without one?
Frequency plot	Also known as a histogram. Helps to see the distribution of a set of data. A statistical tool.	More picking errors on small orders or large, or early in the shift or at the end? Collect some data and plot it to find out.
Measurement System Analysis	Statistical study to determine if the accuracy of an measure is adequate.	Many warehouses have labor productivity goals or standards. How accurate and reliable is the record keeping? If the case pick to powered pallet jack is standard 52 lines an hour should a picker be concerned about achieving only 50, or feel great about hitting 54?
Total Productive Maintenance	An approach to maximizing the effectiveness of facilities used within a business. Total productive maintenance, or TPM, aims to improve the condition and performance of particular facilities through simple, repetitive maintenance activities. Based on a culture of teamwork and consensus, TPM teams are encouraged to take a proactive approach to maintenance. A team is made up of operators and those involved in the setting up and maintenance of the facilities.	Got to keep the lifts running, batteries charged, printers printing … Does equipment downtime ever become an excuse? Don’t let the equipment decide when to take a break, schedule the maintenance on your own terms. Factories have figured this out why not the warehouses?
DMAIC	Project planning mnemonic – define, measure, analyze, improve, and control	Why not use this outline on any change initiative?
FMEA	Failure Modes and Effects Analysis – often used in postmortem, best used to prevent.	Better to anticipate what could go wrong with the new WMS installation than to have to deal with the clean up after the meltdown.
Gemba	Go see. Don’t theorize from the front office, instead to to where the issue, problem, value lives and look at it.	Looking in the racks, using the white glove test (how thick is the dust on the slow moving stock?), observing the housekeeping is all part of the visual management and servant leadership culture of lean sigma in supply chain.