42 July/August 2010 | APICS magazine

Navigating Troubled Waters

A voyage to more stable safety stock and service levels

By Steve Johanson

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APICS magazine | July/August 2010 43

many operations management professionals treat working capital and service-level targets as two separate key performance indi-

cators (KPIs); in fact, they are different sides of the same equation. There is a rigid relationship between the two and proven statistical curves that define their relationship. However, it is more common than not that safety stock

is adjusted by past experience and rules of thumb. In such an environment,

safety stock becomes quite like an ocean tide: Eventually, it

will flood.

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44 July/August 2010 | APICS magazine

When managers bump up target values in response to crisis, it creates swells of work-ing capital. Cash-flow concerns shift toward inventory reduction, and the wave begins to recede as organizations embark on initiatives to reduce and rationalize inventory. Of course, shortages soon arise, and the floods come once more. Constant changes—such as production adjustments, modifications to distribution and manufacturing assets, and process-improvement projects—keep the cycle in motion.

In order to optimize the balance between service level and safety stock, profession-als must constantly calculate valid, statistical safety stock targets. Most enterprise resources planning (ERP) systems perform a safety stock calculation. But very few include in the system all sources of variability as inputs to the safety stock formula. Furthermore, ERP tools rarely calculate accurate safety stock inputs or correct erroneous data.

Figure 1 shows 13 basic safety stock inputs. They include operational decisions, performance measures, constraints, and environmental fac-tors. All play a pivotal role, and only extensive sensitivity analysis can make it possible to dis-count any one factor.

Omission of a variable is the first major prob-lem with setting safety stocks. Miscalculation is the second. This is clear from a situation that occurred at a sausage manufacturer. Demand was smooth, forecasts were good, and safety stocks were greater than calculated targets. However, employees were fighting what appeared to be uncontrollable system shortages. On closer examination, the calculations excluded manufac-turing time from lead time. (Smoking and curing averaged 20 days.) Plus, systems discounted manufacturing time variability. (For instance, sausage cures at plus-or-minus six days.) While original safety stock estimates were based on short, invariable delivery times and good fore-casting, the correct target was significantly more than both what was calculated previously and what managers thought was a surplus.

Note that forecast error is treated as an environmental factor. Often, time shortages are blamed on bad forecasting. But rarely does a company change its forecast error—meaning, past forecast errors are very good estimates of future ones. Look at your own forecast error over time for any collection of stockkeeping units (SKUs) and distribution centers, and you will see the error is very constant.

Figure 1: Safety stock inputs

Statistical safetystock

Supply chain decisions

Planning lead time

Cycle time

Transit mode/time

Supply chain performance

Manufacturing time

Management time variability

Transit time variability

Manufacturing schedule adherence

Transit schedule adherence

Demand environmentForecast error

Demand variability

Requirements and restraints

Service level

Ship life

Capacity loading

W ith statistically calculated safety stocks, an operations manager finally can take a proactive stance.

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APICS magazine | July/August 2010 45

Current state

Reduce cycle lengths/stabilize planning

Cut manufacturing planning lead time

Achieve 95 percent schedule adherence

Improve forecast error

Reduce lot release time

Further cycle length reductions

Inventory turns

0 5 10 15 20

Capacity loading oft en is treated as a deter-ministic constraint. You have a fi xed amount of capacity, and it does not vary. In reality, it is a sto-chastic constraint and plays into the balancing act between safety stock and service level. Th is means that, the closer a work center is loaded to 100 per-cent, the more safety stock will be required. Th is stock is used to counter the risk of manufacturing possibly failing to replenish 100 percent of what was sold in any given cycle.

Th ere are several approaches to correcting the quandary of safety stock versus service level. First, operations managers need to audit their own processes by asking four questions:

Do we capture all 13 factors, constraints, and 1. variables in the system?Are they measured properly?2. Are we fi ltering erroneous data points from 3. formulas?Are we calculating factors for every node?4.

Second, a process must be established that acts upon these requirements. Most importantly, the cycle needs to occur at the same frequency as periodic planning processes. If master planning is done quarterly, set safety stocks quarterly. Th is might include redefi ning business processes and incorporating a tool that can support all the data and the periodic workfl ow.

Th e good news is that most conventional ERP solutions are correct. Th e bad news is that this only is true for the variables being considered. If forecast error is taken into account as an input, the result will be correct only if all other vari-ables are zero. However, if all 13 are included and the data are clean, then the safety stock number likely is accurate.

With statistically calculated safety stocks, an operations manager fi nally can take a proactive stance. Armed with a robust model, it’s possible to develop a plan that explicitly lays out the require-ments to reduce inventories. Figure 2 is a roadmap made by a food manufacturer, which illustrates the steps required to achieve an inventory turns target. Th e approach focuses on improvement, quantifi es that improvement in terms of cost, and identifi es responsibility for the improvement. Without a complete statistical model, this is impossible. In addition, it puts the business at risk of tipping the balance toward high turns and low service levels.

Th ere are two approaches, the results of which rarely drive up total landed costs else-where. Th ey are

using a hybrid deployment plan by mixing •one-tier and two-tier distributionachieving fl exible service levels by having •diff erent service-level targets by SKU location.

Figure 2: Inventory reduction roadmap

A facility that follows only one scheme or the other for all of its SKUs relinquishes considerable savings.

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46 July/August 2010 | APICS magazine

These solutions are independent of cost considerations that can be made in isolation. They also are straightforward if one already is calculating a good statistical safety stock.

Hybrid deployment means that some SKUs get stocked in a buffer or plant warehouse that service a distribution network (tier 2), while others are deployed directly to the distribution centers when they are made (tier 1). When look-ing at a complete product portfolio, it is unusual for the most cost-effective scenario to involve all SKUs either in tier 1 or tier 2. A facility that follows only one scheme or the other for all of its SKUs relinquishes considerable savings.

Having flexible service levels means that, within a given set of products and locations, planners won’t have to try to achieve a single service-level target for every location and every SKU. The effect is that easy-to-serve nodes (high volume and low forecast error) supplement more difficult ones (low volume and high forecast error). Like hybrid deployment, these calculations are done easily with a good statis-tical model. It is the execution that proves difficult.

Finally, to successfully balance service levels and inventory, one must adhere to the follow-

ing disciplines and philosophies:Recognize that inventory and service level are •the same KPI.Make inventory calculation a competency •and part of the planning process.Capture and properly calculate data to •support all system variables.Use these variables in a statistical model to •determine what level of inventory is required to meet constraints.Use the model to determine how inventory •might be scientifically reduced or right-sized.Proactively approach inventory targets with •an improvement roadmap grounded in fact.Creating this type of model will enable much

more proactive processes. This, in turn, makes it possible to communicate required inventory levels and understand how to reduce them. Most importantly, it helps business leaders avoid the ebb and flow so they may enjoy calmer waters.

Steve Johanson is the chief executive officer of

Supply Chain Toolworks and a founding partner

of GTM Consulting. He may be contacted at

steve.johanson@gtmconsulting.biz or (415) 533-9275.

RegisteR online at apics.oRg/extRa.

APICS extraAPICS Extra Live: Finding the Right safety stock and service levels

Attend APICS Extra Live to gain deeper insight into the July/August APICS magazine article by Steve Johanson, which illustrates how to achieve a balance between your safety stock and customer service levels.

With good tools and processes, planners can simultaneously improve turns and service levels. In this APICS Extra Live, discover how effective planning considers sources of delay on both the supply and demand sides. Attendees also will learn why thorough safety stock calculation needs to be a part of the planning process and a fundamental competency.

Presented by: steve Johanson and lorine DeHuff

Date: august 19, 2010Time: 1:00–2:00 p.m. ct

To comment on this article, send a message to feedback@apics.org.

2009 APICS Photo Contest ProfilesLast year, operations and supply chain management professionals engaged in the visual dialogue about the current state of manufacturing across the globe. They shared their photos and insights into what those photographs say about manufacturing today.

These photos represent the diverse role manufacturing plays in today’s interconnected economy.

second place

Mark McentireOil Platform, Mobile, Alabama

third place

Mike donohoeDistribution in Shenzhen, China

First place

robert GorskiEmpty High-Rise Stockroom, Wisconsin

Finalist

JiM passMore, cscp, c.p.M.Shoemaker in the Union of Myanmar

Finalist

tielMan nieuwoudtThe Ethiopian Manual Distribution Model

Finalist

lisa anGellWindustrial Park, Brownsville, Wisconsin

Finalist

alyssa o’donnellCosta Rican Palm Oil Farm

Finalist

robert rowtonOil Derrick at Night, Texas

Finalist

peter MccaFFreyAssembly Workers in Zhuhai, China

Watch your inbox for information on the 2010 APICS Photo Contest. Visit apics.org/photocontest to view these photos and learn more about them.

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