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Unlocking the scheduler’s dilemmain manufacturing

Yogi Berra, the great American baseball coach, when pressed on how his team would

perform in the coming season, replied, “It’s tough to make predictions, especially about

the future.” Berra’s experiences in the challenging world of baseball may have led him to

err on the cautious side, but since time immemorial, there have been many pundits and

oracles all too willing to take the great leap and offer predictions on our futures.

The evidence can be seen all around us with a constant sprinkling of sporting, political,

economic and weather forecasts, to name a few. Depending on your point of view, some

of these fields of work have in fact delivered greater levels of predictive accuracy as

technology continues to advance to better manage and control data.

We know all too well the complete and catastrophic failure of our political and financial

institutions to predict the recent economic downturn. On the other hand, progress in the

accuracy of weather forecasts has been steady as computer power has improved

exponentially over recent decades. Generally, however, it remains a minefield. As the

management guru Peter Drucker explains, to predict the future is “like trying to drive

down a country road at night with no lights while looking out the back window”.

That may be so, but performances of business teams in many organisations depends

precisely on making accurate predictions and this sometimes governs their very

existence. Visualising the future and then providing prescriptions for the public domain

can take on many connotations, but the making of forecasts or predictions are actually

institutionalised into the way businesses operate and reach decisions. In particular, many

organisations rely on the ritual of the annual sales forecast for the coming year to drive

plans and budgets on the supply side such as procurement, engineering and human

resources.

FORECASTING AND SCHEDULING

How the sales team derives the figures, the formulas it uses and how it measures their

accuracy are topics for regular discussion in board meetings. Forecasts from the

customer, economic conditions, product lifecycles and historical trends are just some of

the variables thrown into the mix. But, as complexity continues to grow in the global

marketplace, the best-laid-schemes oft go awry. Depending on the distance from target

can cause major problems in the planning of resources for the coming year.

Production schedulers who operate wholly-computer based MRP (manufacturing

resource planning) systems to meet sales forecasts know well the problems if they are

not converted accurately into solid work orders. The MRP ‘push system’, which is based

Many engineeringand manufacturingorganisationsunder-perform as aresult of poorscheduling. DavidHarkin addressesthe key issues andoffers solutionsfrom Leanprinciples andconstraintmanagement

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Figure 1: the scheduler’s dilemma

(click to enlarge)

Figure 2 (click to enlarge)

Figure 3: Adapted from the Lean tool

box, Prof J Bicheno, Lean Enterprise

on the preparation of a multi-period schedule of future demands for the company’s

products known as the ‘master production schedule’, uses the computer system to break

it down into detailed manufacturing and purchasing schedules.

The phrase-push system refers to the mode of planning that these detailed schedules

push work onto manufacturing in line with appropriate finish dates and process loading.

In this way, it requires manufacturing to produce the required parts and push them onto

the next process until they reach final assembly.

The dilemma (Fig 1) faced by the

operational planners who execute the plan

is how much of the plan can actually be

taken at face value to provide optimum

service levels without tying up too much

working capital in the process. Their mode

of operation is usually based on

unsatisfactory and constantly changing

compromises, which in particular can be

highlighted by how many times the

forecasts are updated as the year evolves.

The over exuberance of business development teams at the prospect of increased sales

can send the wrong signals – the resulting forecasts mean that schedulers, all too aware

of the risks, put a greater reliance on increased stock holdings. This results in restricted

cash flows and increased operating costs.

THREE STEPS TO UNLOCK THE DILEMMA

Step 1: Product categorisation – ‘runners’, ‘repeaters’, ‘strangers’ (RRS) and planning

mode

What, instead, if most of the noise from the external environment could be replaced in

part with signals along the supply chain, to instruct each stage with regard to what needs

to be produced? The level of risk in solely relying on the forecast as the driver would be

lessened considerably, resulting in work orders being derived from consumption rates.

The improved accuracy of the system would deliver significant customer service and

lower operating cost benefits to the business.

The stalwart tools required to achieve this

improved supply-chain performance comes

in the form of a combination from Lean and

from the Avraham Y. Goldratt Institute’s best

practice models. Firstly, the mode of

planning can be determined by categorising

the products based on existing run rates

(Fig 2). An Irish manufacturing example is

below.

Thought to have originated in Lucas

Industries during the late 1980s, the product

categorisation into ‘runners’, ‘repeaters’ and ‘strangers’ forms part of an excellent strategy

for production scheduling and supply-chain management. When applying the ‘Venetians

rule’ (Pareto analysis) to the RRS v current throughput rates, the ‘runners’, ‘products’ or

‘product-family’ – having sufficient volume to justify dedicated facilities or

manufacturing cells – make up to ~60% sold, as the example above demonstrates.

A ‘repeater’ is a product or product-family with intermediate volume, where dedicated

facilities are not justifiable showing a further ~15%. The ‘strangers’ are a product or family

with low intermittent volumes making up to ~25% volume, but making up over 70% of

the products. Of course, there will be variations across industries, but the principles

remain the same – that being classified will determine the optimum scheduling mode of

operation (Fig 3).

Step 2: Pull for ‘runners’ – push for ‘strangers’

The range of modes to support flow

through the supply chain can be seen in Fig

3 and what it demonstrates is that pull

systems are more likely with ‘runners’,

whilst MRP fits better with ‘strangers’. Pull

systems/kanbans control the flow of

resources in a production process by

replacing only what has been consumed.

They are customer order-driven production

schedules based on actual demand and

consumption, rather than forecasting.

Implementing pull systems can help

eliminate waste in handling, storing and

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Research Centre, Cardiff Business

School (click to enlarge)

Figure 4: Adapted from TOC Goldratt

Institute

Figure 5 (click to enlarge)

getting product to the customer on time,

therefore improving costs and service

levels. This is a critical component of the

value stream and the end-to-end supply chain.

Step 3: Combine RRS and constraint-management scheduling

Constraint management (CM) is a

philosophy and set of techniques used to

manage the throughput of an organisation.

Most widely implemented in manufacturing

operations, it teaches management how to

identify and direct their focus on the few

critical drivers.CM begins with one underlying

assumption: the performance of the system’s

constraint will determine the throughput of the

entire system. Put simply, the strength of the

supply chain is determined at the weakest point, or

the pace of the entire supply chain is controlled by

the slowest processes. In scheduling terms (Fig 4),

it means:

1. Developing a detailed schedule for the constraint resource;

2. Add buffers to protect the throughput of that resource;

3. Synchronise all other resources to the constraint schedule.

BUFFER AND SYNCHRONISING TO THE CONSTRAINT

The buffer is a period of time to protect the constraint resource from problems that

occur upstream. Its effect is to provide a resynchronisation of the work as it flows

through the plant. The buffer compensates for process variation, and makes schedules

very stable and immune to most problems. It has the additional effect of eliminating the

need for 100% accurate data for scheduling. It allows the user to produce a ‘good enough’

schedule that will generate superior results over almost every other scheduling method.

After the constraint has been scheduled, material release and shipping are connected to

it, using the buffer offset. Material and parts are released at the same rate as the

constraint can consume it. Orders are shipped at the rate of constraint production.

Of course, implementing the three-step

programme will face different

organisational, managerial and cultural

contexts and the large stock mentality can

be hard to break down in some companies.

The scheduler’s role changes completely

under the new system from the classic

expeditor to strategic planning choices for

each product classification.

Investing in a production, planning and scheduling system that makes to order or stock

(depending on the product classification) with a modus operandi along the value stream

combined with Goldratt’s very effective ‘theory of constraint’ principles will provide the

platform for a winning planning and stock-holding strategy. Furthermore, the inherent

risks of being wholly reliant on a sales-forecasting system to determine resources will be

greatly reduced.

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