## Measuring Forecast Accuracy

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 . 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
• Channels
• 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.

## Safety Stock

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 …

Example:

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 60 0.26 70 0.52 75 0.77 80 0.84 85 1.04 90 1.28 92.5 1.44 95 1.65 97.5 1.96 98 2.05 99 2.33 99.5 2.58 99.9 3.08

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.

## Material Classification Logic

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:

Have any better ideas, lets us know!

## Square Root Law – inventory in multiple locations

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.”

## Coefficient of Variation

A measure of the volatility of customer orders (or any time series), aka Demand Linearity

1. Calculate the standard deviation (s) of the historical demand, use appropriate time buckets (daily, weekly, monthly)
2. You might need to discard abnormal demand
3. Calculate the historical mean (x) (or average) or use the forecast mean
4. Then calculate the coefficient of variation (Cv)  Cv = s/x
5. Low variability is a Cv less then 1.0, very stable demand is a Cv less than 0.5

## Demand Profile

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 …

To build a demand profile take the following steps:

1. Pick a product, product family, customer, customer-item pair, or business unit of interest.
2. Determine an appropriate time unit – hourly, daily, monthly.
3. 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.
4. Create the graph or time series plot as above.
5. 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: how to

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.

1. Make a list of part numbers
2. Determine total quantity used over some period of time
3. Obtaining the cost for each part
4. Calculate usage \$ value for each part by multiplying the quantity and the cost
5. Sort the list from high to low \$
6. Calculate the total usage \$ value for all items
7. Calculate each item’s percent of total usage \$ value
8. 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

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

1. Determine Total Cycle Time
2. Determine Queue Times between steps
3. Create Step segments proportional to the task times
4. 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
5. Draw in feedback loops & label Yield percentages
6. Sum Activity / Non-activity times
7. Sum Value / Non-value Times

## Overall Plant Effectiveness

The Eight Major Plant Losses

1. Shutdown
3. Equipment failure
4. Process failure
5. Normal production loss
6. Abnormal production loss
7. Quality defects
8. Reprocessing

## Waterspiders as continuous improvement innovators

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
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?

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:*

*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.

• 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

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, part 1

## Lean Sigma Tools for Supply Chain, part 1

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?

## Warehouse Zoning

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.

## Walter Shewhart

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.

## Frederick Taylor

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.

## Facility Block Layout

A standard approach for creating a new production facility layout for either a green field or an existing facility is as follows:

1. Perform PQ Analysis
2. Preparing a Product Process Routing Matrix
3. Develop Block Layout alternatives
4. Optimize the size, shape, placement of the blocks.
5. Nimawashi

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:

1. process functional – group common machines or processes together
2. product – line up equipment in sequence of operation
3. fixed position – for large projects where you can’t move the product
4. 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.

The number of permutations can be huge, so either use group intuition for the block details or investing in one of the current software packages such as Pro Planner, PlanOpt.

## More Kanban Calculations

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

Where:

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

where:

• 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)

where

• 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)