Supply Chain Forecast Accuracy is usually measured with Mean Absolute Percent Error or MAPE, the average of percentage errors. But there are several other metrics to consider. Here’s an example …
Remember how you felt when you first started drawing your first Value Stream Maps? Well that’s how I felt when I first got my hands on Business Model Generation by Alexander Osterwalder, and Yves Pigneur. This is a new visual tool you can use to help connect your value propositions to your customers and suppliers. The Business Model Generation is an easy to use and very powerful diagramming method, or canvas, with nine blocks:
- Key partners
- Key activities
- Key resources
- Value proposition
- Customer relationships
- Customer segments
- Cost structure
- Revenue stream
The canvas help you think about how you plan to make money or deliver value by understanding what customer value and how that value is created.
For more on business models and business model innovation take a look at Business Model Generation, for examples of business model canvas’ of companies you maybe be familiar with. Also check out Business Model Alchemist by Alexander Osterwalder.
Over time, inventory goes up and down. As customer order shipments are made inventory transactions subtract the ship quantity from the quantity on hand. When production is made or goods received into the warehouse the quantity on hand goes up. This up/down cycle can be thought of as a ‘saw tooth’. Except it’s never as neat and tidy as this.
Cycle stock can be calculated as ‘Demand during Lead Time’, or if we make or buy product once a period I should have at least enough to last until the next order arrives. One way to do this is to first determine the replenishment time (RT) or the time between orders. For high volume products we may make the product every month, so the RT would be 30 days or 4 weeks or 1 month.
Next we need to know the consumption, sales forecast, or historical shipments and then calculate the average demand during the replenishment period.
AD = average demand, RT = replenishment time, or campaign interval, or period between receipts.
In this example we receive 400 every period. Our stock on hand goes from 400 down to zero just as the next resupply arrives. The average inventory isn’t 400 or 0, it’s 200. When there are many products being received and issued every day some are near max, some are near zero, but the average for all products is ½ (max – min).
But this is still too simple. Consumption (issue from stock, or shipments) is never exactly smooth, sometime higher or lower than planned. Sometime resupply is earlier or later than planned or in different quantity than planned. So we have two types of supply & demand variation to think about. We need safety stock and safety lead time to protect our customers from variation in supply and demand.
In this example we have 100 in safety stock. If everything were perfect inventory would cycle between 500 and 100. We keep 100 just in case production is late or customer demand is higher than expected.
Demand can come in faster or in greater quantity than we had planned (red dashed line). If we can’t get the product resupplied sooner we have a period of stock out or back order.
With safety stock we can try to prevent back orders or stock outs.
We dip in to safety stock when resupply is late.
Putting this all together we get …
Demand Safety Stock can be calculated, but first we need to look at some statistics. Here is graph of weekly shipments for CARILATUDE 350MG for the period of 1/1/2012 to 12/31/2012.
Here are some descriptive statistics:
Total shipped = 1,066,772
Weekly Average = 20,449
Max Week = 61,776
Min Week = 6,360
Weekly Standard Deviation = 8,883
Coefficient of Variation = Std Dev/Average = 0.43
How much inventory should we keep for this product? Do we want to have enough to cover the two big spikes or is it OK to go on back order for a week or two? Before we can answer these questions we need to know a few things.
- How often do we receive this product in to stock? If we make the product once a month, then we need to have enough in stock to last for a month plus any uptick in demand and some more to cover if production is late on the next run.
- What service level does management want to achieve? You would need infinite inventory to cover all possible customer demand and supply issues.
We don’t have enough money or warehouse space to hold all the stock we might want. So we have to accept some level of stock out or back order. Where we carry more stock than we need we have consumed raw materials, production capacity, machine time, labor that could have been used to make other products. When we don’t have enough stock on hand we are often forced to change the schedule and make product earlier than planned, and this means other products get pushed out. The trick is to set just the right level of target inventory with not too much safety stock.
To set Safety Stock we can consider three types of additional inventory based on the source of variation:
Demand Safety – customer demand can come in greater volume than expected and deplete inventory before the next planned replenishment (raw material or production run).
Supply Leadtime – production can be late due to machine breakdown, labor availability, scheduling changes, lost batch, or get hung up for a variety of reasons
Murphy – unpredictable circumstances that management might want to hedge.
There is also seasonality and planned stock builds, but we aren’t addressing those here.
Demand Safety is based on service level and demand standard deviation.
The safety factor is based on the service level management is trying to achieve.
|Service Level %||Safety Factor|
When we calculate, for each product, the weekly shipment historical margin contribution average, standard deviations, and Cv and then graph the average vs Cv for each product we get the following Demand Segmentation graph …
Here we see that there are many product with low volume that are very unpredictable (Cv > 1.0) unfortunately we may have to keep stock for these products. Because the history shows that shipments are erratic we may have to carry a lot of inventory to be able to cover the unpredictable demand. Fortunately most of the high volume products are predictable, but some are on the edge of being noisy and so we may have to carry a large amount of safety stock for these too.
Bruce asked “Do any of you have any standard material classification logic that you have used. I’m working with a client to classify all of the parts they purchase. In the past I’ve used some standards from NAPM but I can’t seem to find them now on the ISM site.”
I thought for a moment and went browsing and here’s what I can up with:
- Believe NAPM’s list has been obsoleted by other codes such as the Harmonized Tariff Schedule http://hts.usitc.gov/
- UN Standard Products and Services Codes http://www.unspsc.org/
- Depending on how granular you need to go you could also use SIC codes to build commodity groups. http://www.sec.gov/info/edgar/siccodes.htm
- HazMat US DOT/UN Codes might be what you need http://bhs.econ.census.gov/bhs/cfs/Hazmat%20Code%20List.pdf
- Pharma has NDC’s http://www.fda.gov/drugs/informationondrugs/ucm142438.htm
Have any better ideas, lets us know!
Got asked what would happen to inventory when the number of stocking locations change. I thought for a minute and remembered a quick rule of thumb … The Inventory Square Root Law states that …
Average inventory increases proportionally to the square root of the number of locations in which inventory is held.
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.
* The Square Root Law was mathematically proven by D.H. Maister in his 1975, International Journal of Physical Distribution article entitled “Centralization of Inventories and The Square Root Law.”
A measure of the volatility of customer orders (or any time series), aka Demand Linearity
- Calculate the standard deviation (s) of the historical demand, use appropriate time buckets (daily, weekly, monthly)
- You might need to discard abnormal demand
- Calculate the historical mean (x) (or average) or use the forecast mean
- Then calculate the coefficient of variation (Cv) Cv = s/x
- Low variability is a Cv less then 1.0, very stable demand is a Cv less than 0.5
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, that is used to match the pace of an operation with customer demand. Takt Time is calculated as Available Time/Demand, and is by definition a ratio of averages. But 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 …
- Pick a product, product family, customer, customer-item pair, or business unit of interest.
- Determine an appropriate time unit – hourly, daily, monthly.
- Gather the true 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 – range, minimum, maximum, standard deviation, etc. 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 is a common approach for prioritizing, classifying, or categorizing inventory management techniques. Classification is usually based on price or cost times usage or consumption volume. Typically ‘A Items’ are the highest dollar volume and represent 5 to 10% of the items and 50 to 70% of the total dollar volume. The ABC Analysis Principle is that A items are treated differently than C items. A Items might be counted more frequently and have different planning parameters, higher inventory turns, higher safety stock and customer service levels.
- 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 …
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.
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.
- 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
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.
Have any examples you’d like to share?
There’s the math, and then there’s the data collection effort. First the math:
- Price paid to seller (which may include some of the following)
- Payment terms
- Exchange rates over time
Transportation and Logistics:
- Foreign inland
- Line haul
- U.S. inland
Customs and Imports:
- HTSUSA (tariff) rate
- Merchandise processing
- Harbor maintenance fee
- Broker fee
- Less: Duty Drawback
- 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 …
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 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.|
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 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.|
A technique for laying out warehouse storage which seeks to minimize “pick” travel time by grouping the most used items closest to their point of use.
Dr. Shewhart was a prominent scientist with the Western Electric Engineering Department back in the 1920s. In 1924, Dr. Shewhart devised a framework for the first application of the statistical method to the problem of quality control. Shewhart wrote a note to R.L. Jones, responding to his request for some type of inspection report that “might be modified from time to time, in order to give a glance at the greatest amount of accurate information”. He attached a sample chart “designed to indicate whether or not the observed variations in the percent of defective apparatus of a given type are significant; that is, to indicate whether or not the product is satisfactory.”
Shewhart’s example was the world’s first schematic control chart. In one short letter, he had set forth the essential principles and considerations of quality control. As he pursued this work, Shewart gave birth to the modern scientific study of statistical process control.
In 1931, Shewhart’s book ‘Economic Control of Quality of Manufactured Product’ contained his findings on statistical sampling techniques. A Western Electric colleague, W. Edwards Deming, spread the word on Shewhart’s work when he joined the US War Department, and later when he taught the fundamentals of quality in Japan.
Taylor, Frederick Winslow (1856-1915), American industrial engineer, who originated scientific management in business. He was born in Germantown (now part of Philadelphia), Pennsylvania. In 1878, he began working at the Midvale Steel Company. He became foreman of the steel plant and applied himself to studies in the measurement of industrial productivity. Taylor developed detailed systems intended to gain maximum efficiency from both workers and machines in the factory. These systems relied on time and motion studies, which help determine the best methods for performing a task in the least amount of time. In 1898 he became joint discoverer of the Taylor-White process, a method of tempering steel. Taylor served as consulting engineer for several companies. His management methods were published in The Principles of Scientific Management.
A standard approach for creating a new production facility layout for either a green field or an existing facility is as follows:
- Perform PQ Analysis
- Preparing a Product Process Routing Matrix
- Develop Block Layout alternatives
- Optimize the size, shape, placement of the blocks.
PQ (Product Quantity Pareto ABC Analysis) and Product Process (find common routings in a mixed model business) are defined elsewhere. For many practitioners Block Layout seems to be either a bit of artistry or is mired in software complexities. Here are a few thoughts on how to approach this step in the facility design process.
First a few considerations:
In a green field we know the product and process and want to determine the size of the building we need and it’s layout. For a brown field we’re trying to make the best use of the use of the four walls we already have. In both scenarios there are trade offs to make. Having a decision making at the beginning of the project can save a lot of time and money. Some will take a mathematical approach, others organic consensus. Either way agree on the design process methodology up front.
Determine “best” layout type based on customer demand, product and processing characteristics, and business strategy. Common layout types include:
- process functional – group common machines or processes together
- product – line up equipment in sequence of operation
- fixed position – for large projects where you can’t move the product
- hybrid – mixed model, shared monument, group technology cells
In brown field facilities the main layout consideration is often in fact moving from one layout type to another as business conditions and strategies evolve.
Determine the activity and proximity relationships between the various blocks. A great approach for understanding relationships is the Simplified Systematic Layout Planning method by Muther and Wheeler.
The Product Process Routing Matrix noted above determines the quantity and type of equipment needed. One complication is that very expensive or large machines may need to be shared, so compromises may need to be made. I like the table top trial and error Paper Doll approach. Once we’ve gathered data on the importance of proximity and activity relationships between blocks, equipment footprints, maintenance and material handling access and clearance requirements, utilities, building codes, facility constraints, etc. and then we determine the gross footprint size. Now through trial and error we arrange the equipment in the block in a logical flow or sequence manner. We then have a beauty contest and subject each alternative to a decision selection matrix where we score and rank various design factors such as compactness, adjacency (relationship closeness), least travel distance (material handling cost and speed), etc.
First listed various formulations of calculating kanban quantities in wmarhel at Elsmar Cove writes …
The formula for calculating the number of kanban cards in a system for a particular product is:
(Daily Demand x (Run Frequency + Lead Time + Safety Time)) / Container Capacity
Daily Demand = Customer Consumption expressed as # of units
Run Frequency = Frequency which you decide to set-up and produce that item. This is expressed as a unit of time. For a five day work week, running the product every day would equal (1), every third day would equal (3), etc.
Lead Time = Manufacturing lead time (processing time + Set-up time + queue time) + lead time for kanban retrieval expressed as a unit of time.
Safety Time = Allowance for variations in demand and supply, also expressed as a unit of time. Keep as low as possible.
Container Capacity = Number of units per container (# of units in a container is always the same number).
10. World Class Manufacturing has an on-line Kanban Size Calculator that uses the following formula:
Total Required Inventory (TRI) = Weekly Part Usage * Lead-time * Number of locations for stock
# Kanban = TRI / Container Capacity
11. Oracle uses
By default, the standard calculation is:
(C – 1) * S = D * L
- C is the number of kanban cards
- S is the kanban size
- D is the average daily demand
- L is the lead time (in days) to replenish one kanban
12. SAP says …
K = ((RT * AC)/CONT) * (SF + C)
- K numbers of Kanban
- CONT contents per Kanban
- RT replenishment lead time per Kanban
- AC average consumption per time
- SF safety factor
- C constant (default 1)