Demand Segmentation and Profiling

Demand Profiling : Defined

• Demand Profiling is simply the application of a Time Series Plot to the Customer demand on a process.

• Importance:– Understanding demand is so important in any

Lean Sigma project– Interpretation of the plot is the key to success

Demand Profiling : Purpose

• The purpose of creating the profile was to understand, from historical data, future volume and variation in demand

• Rudimentary forecasting tool

Demand Profile : Plot

Demand Profile :Process

• Demand data is captured over time at uniform

time intervals and plotted with an x-axis of Time and a y-axis of Demand.

• The plot highlights the likely average demand on the process and also the likely variation in demand to which the process has to respond.

Logistics of Demand Profiling

• Can be applied without the rest of the Team• Data might have to come from multiple sources,

requires team involvement to collect it• Analysis is done entirely in a spreadsheet or

statistical package • Analysis can be done in a matter of a few minutes

after the data is in the correct format• Initially, data is historical, but after the organization

understands the application of the tool, forecast data can also be used

Demand Profiling : Steps involved

• Step 1 : Identify which entity type or types are to be examined

• Step 2 : Identify the time increments• Step 3 : Collect from the downstream

Customer for the entity type for each time increment

• Step 4 : Create the graph

Roadmap : Step 1

• Identify which entity type or types are to be examined

• Demand Profiling represents demand for either one entity type or the sum across a few entity types

• If the Team needs to understand the volume and variation across many entity types then it is probably best to look at a Demand Segmentation instead of a Demand Profile

Roadmap : Step 2

• Identify the time increments• It is necessary to have at least 25 increments of

captured demand to have a useful graph• The increments themselves should be

meaningful, and it is useful to take the typical replenishment cycle as the driver

• For example, if Customers were replenished weekly, then a week would be a reasonable time increment

Roadmap : Step 3

• Data is collected from the downstream Customer for the entity type for each time increment

• Customer demand rates are typically much smoother than we care to admit, and in fact, their usage rates are even smoother

• Internally, we tend to batch entity processing into large lots, which we make on an infrequent basis; so it shows a much higher variation in demand than is actually there

• For Demand Profiling, we need to look at demand patterns, not our own planned process patterns

Roadmap : Step 4

• After the demand data for the entity type across multiple time increments is collected, create the graph similar to the one

• Take the time as the x-axis and the volume of demand as the y-axis.

Interpreting the Output

• Demand Profile is being used as a rudimentary forecasting tool, but rather than using the data to determine how much to generate on a short-term basis, it is used to create a more responsive and capable process

• Demand Profiles can exhibit one or combinations of a multitude of patterns, the most common of which are shown in Figure 7.15.2.

• Graph A represents a Zone 1 Demand Profile, whereas Graph D represents a Zone 3 Demand Profile

Advantage over Demand Segmentation

• The value that Demand Profiling brings above and beyond Demand Segmentation is that cyclical patterns become visible

• Rather than just knowing that there is variation, the Belt obtains an understanding of how the demand is varying

• After this is understood, the process can be laid out and resourced accordingly

Example

If there are significant spikes in demand at the end of each day:– More staff could be used at that point (effectively

increasing capacity)– Inventory could be built ahead of time to serve the

demand– Later staff hours could be used to spread the demand– Customers could be encouraged not to batch their

demand and perhaps spread it to earlier hours, and so on

Other options :

The interpolation techniques break down the time series data into its component parts, namely:– Current Level The mean value at the current time– Trend The rate of systematic increase (or decrease) in

the mean value– Seasonal Pattern A recurring periodic pattern– Random Component The portion of behavior that

remains unaccounted for after the Current Level, the Trend and the Seasonal Pattern have been identified

Demand Segmentation

Products in the top left corner of the graph exhibit smooth high volume demand. Products in the bottom right-hand corner exhibit variable, low volume demand. Most production planning, forecasting, and process leveling approaches mistakenly consider these in the same way, which misses opportunities to create more predictable, responsive processes and reduce inventory all at the same time.

Demand Segmentation : Plot

Considerations• Altitude – this analysis typically starts with customer demand but can be

done by market, customer, finished goods, in-process, raw materials, suppliers

• Time Bucket – depending on the ‘clock speed’ of the enterprise the demand data can be aggregated by month, week, day, or hour. When gathering data start with the smallest time period possible.

• Unit of Measure – the units of volume may be straight forward, for example ‘pieces’ or may be complicated due to different value streams or product lines having a mix of units.

• Demand – sometimes not easy to get. Shipments may not represent true customer demand, especially if on-time delivery and order fill aren’t very high.

• Geography – like altitude, this analysis can be done by production cell, value stream, plant or DC, business unit, or enterprise.

Considerations Contd..• Horizon – use forecast to determine volume, especially if product life cycles are

short• Timeline – usually want at least 25 data points to calculate a meaningful

standard deviation of the demand history• Scrub - other than filtering out abnormal orders consider weekend transactions,

huge one-time orders, and zeros.• Plot – volume vs. Cv• Interpret – Cv’s less than 1.0 lend themselves to flow and pull techniques. Cv’s

less than 0.5 can often be handled with rate-based replenishment methods. Remember a Cv of 1.0 means the demand variation is a great as the demand average. Say a part has an average daily demand of 100 with a Cv of 1.0 the demand one day could be zero and the next 200 or more – not very predictable. High Cv items are usually low volume, but not always. Take a look at the three data points in the top center of the graph above. Must be a story here – why are the highest volume parts so unpredictable?

Example 1

Example 2

Example 3

Example 4

Example 5

Example 6

Interpreting the Output

Application : ReplenishmentDemand Segmentation can be applied to materials management and specifically replenishment as replenishment to a Customer or replenishment of materials internally or from a Supplier for example, the delivery of product to a Customer, a materials delivery to a line, or a medications delivery to a Care Unit. In this case, the zones would be treated as follows:

• Zone 1Deliver direct to line. High volume, low variation materials usage is so smooth that it allows us to add service value to the Customer by managing their inventory for them. Materials would be delivered on a rate-based system directly to the point of use (POU).

• Zone 2Pull System from Customer. For the middle majority, simple pull triggers from the Customer would allow replenishment when needed

• Zone 3Make (or Deliver) to order. High variation, low volume gives such unpredictable demand that it forces the Supplier to deliver only when there is an order.

Application : Product PortfolioDemand Segmentation can be applied to the Product Portfolio to identify opportunities for rationalization and to validate the value in the portfolio. In this case the Zones would be treated as follows:

• Zone 1Dedicated Business Unit. Products in this Zone have high-volume, low variation demand and could be managed independently, either by dedicating a small internal group to manage them or to spin them off as a whole new Business Unit.

• Zone 2Majority of portfolio. Products in this Zone are probably best left as they are. There is always opportunity to rationalize in this majority, but they typically aren't a primary focus.

• Zone 3 High-value niche products. Unless they are high-value products, the validity of

keeping low volume, highly variable products in the portfolio is questionable.

Application : Production or Operations Planning

Demand Segmentation when applied to Production or Operations Planning allows processing of the entity types in the different Zones to be planned more effectively, as follows:

• Zone 1Repetitive flow rate-based scheduling. Entity types that have a large, smooth demand do not need to be scheduled individually, they can be rate-based, so that during each time-period a consistent amount is processed with the knowledge that the Customer/Market uses that amount. Slight variation in demand is catered for by using small buffers of inventory, preferably at the POU.

• Zone 2Hybrid control Pull System. For the middle majority of entity types in this Zone, Operations can be successfully governed using internal Pull Systems, to minimize work in process (WIP) inventory.

• Zone 3• Discrete job order. For entity types in this Zone, the orders are few and far between and

highly variable, so it makes little sense to process them without an order or Customer request.

References

• Forecasting: Methods and Applications (3rd Edition) by S. Makridakis, S.C. Wheelwright, and R.J. Hyndman

• Lean Sigma : A Practitioner’s Guide