Effects of the material flow
It is frightening how little the planners are free to decide without causing repercussions on other costs and decision areas, and in other sections – effects they most often do not see or think of at the moment of the decision.
The material flow is sending its effects widely around in the laundry.
Put into boxes, some of the variables the planners have to take into consideration when optimizing the production are:
(IED is the workstation change-overs that demand the machine brought to a complete halt. OED can be carried out while the machine is still running. WIP is short for Work In Progress).
Coherence of decisions
And it is really just a part of the picture, because behind the boxes even more decisions hide, e.g. about synchronization of incoming and outgoing goods, maintenance, investment strategies, change-over techniques, and product mix norms. And from these decisions the material flow relates to the optimization of the distribution, the stocks and the workforce.
There is of course the obvious: The line from the batch flow optimization, via the actual product mix to the collection frequencies, which is the connecting link to the distribution optimization.
But there are more links. From collection frequencies we are able to deduct the longest interval between two collections with a customer (the LIBTC-value), and the maximum collected quantity, which is the basis for calculating stock norms per article for each customer. Which brings us into stock optimization.
From the difference between stock norm and the actual stock per article per customer we are able to calculate the gross demand, from which we are able to derive the net demand, and then we are back in the production again.
In other words there is an unbroken line through production, distribution, stocks, and back to production again, when it comes to optimization. The circuit is closed, like in the figure below.
(LIBTC is the Longest Interval Between Two Collections with a customer).
And with that we have actually uncovered some of the most fundamental decisions in the laundry, that is, the ones with the greatest practical and financial consequences in the daily operation.
And more than that. Now we know for sure, that the decisions our planners make, with regards to any one of these optimization types, affects the other two. And strike back at the first again.
Even though it may seem a little academic, it has very concrete effects. We know from bitter experience from other industries, that when we try to increase the utilization of a factory’s capacities to 100% we loose control of stocks and leadtime, and the stress level converges towards disaster.
We can try to loosen these tight connections with buffers and spare capacity, but it is at the cost of our laundry’s lifeblood – the working capital.
Instead we can try to emulate what the very best laundries do.
Key figures and measuring points
Batch sequences, process route choices and allocation of operators are what (at the very short term) govern the costs and capacity utilization. It is at these decisions the most fundamental key figures must be aimed, those we rely on to tell us if we are carrying out an optimal plan.
And that is what the very best laundries do. They plan and execute with targeted methods (like operation strategies and product mix norms) and continuously monitor their progress with key figures.
The less experienced laundries apply methods proven useful yesterday, last week, last month or last year, also when things have changed in the meantime. And they monitor tomorrow, next week or next month whether expectations are met – when it no longer is possible to correct actions, and maybe only when consequences reach the accounts.
And that is what it is about, when we are talking expectations: Financial results. The bottom line. The cost of the decisions made every day in the production, on the plant floor. And which the laundry, applying the right key figures and measuring frequencies, is able to predict the outcome of while the decisions are still being carried out on the shop floor.
The best laundries have a firm grasp of the links between the accounts and the actions taking place in the laundry production, the links found in hierarchical structures like
the one below.
The hierarchy
As an example the working hour consumption in the production is made up of check-in hours, washing hours, ironing hours and so on. Given the building, the machines sitting in the laundry, the employees and the market, ironing hours are determined by a large number of variables, among others:
Efficiency on the ironer, which is the result of:
A. Ironer speed, which is in part determined by:
B. Moisture retention, as a result of:
C. Press / spin cycle time,
C. Drying time, which is influenced by:
C. Steam pressure,
C. Employee productivity, which is influenced by:
D. Working speed, as a result of:
D. Number of allocated operators,
D. Operator skill levels,
D. Micro pause occurrences, which is kept down by:
E. A steady, low buffer content, which is determined by:
F. Category sequences,
F. Process route choices,
etc.
The point being…
We cannot keep track of all these parameters. We have to focus our effort on the most important ones, that is, identify the cost drivers which carry the greatest financial weight, and devise key figures and find measuring points aimed at monitoring these.
Very few entries in the accounts cover several cost types in the production, which involve even more actions. But these actions stem from fewer decisions, which actually only concerns a small number of important cost drivers in the laundry production.
If we work out these cost drivers’ weight we are able to identify the decisions carrying the greatest financial weight. Traditional focus points are:
- fresh water and energy consumption,
- employee productivity,
- fluidity,
- rewash percentage,
- steam production
and the like, but most often there is a layer of governing parameters behind these.
Cost drivers can be grouped around a small number of determining decisions. Working out a link hierarchy for all cost drivers in the laundry, you are likely to experience that:
- category sequences,
- buffer contents,
- process route choices and
- moisture retentions
appear frequently, on the lowest level.
When our planners decide on these parameters they control, in other words, a major part of the laundry’s cost complex. The respect for, and management of, these decisions should reflect this fact.
Decisions with impact
Breaking down the laundry’s cost complex into this kind of hierarchy also shows with what decisions the laundry needs to be particularly careful when formulating the operation strategies, because category sequences, buffer contents and process route choices do have wide consequences.
The connections also give hints as to how the laundry should allocate work and size capacities. For instance it is often cheaper to extract water in a process step that doesn’t require operators, rather than in a step that does (depending on bottlenecks). In this case the planners should prefer to extract as much water as possible in extractors or dryers, rather than in the ironers – if the object is to reduce costs. It is indeed worth considering.
The point is: Quantifying your laundry’s cost parameters, in hierarchies like the one above, lends confidence to your decisions and priorities.
Just remember that wages are not the only cost. When reducing costs, we have to consider all costs. On the other hand, if our objective entails moving processing from expensive workstations to cheaper ones, we should do it, also if it is at the “expense” of leadtime. And if we prefer to reduce costs in general, this should be reflected in capacities, e.g. in a high drying / ironing capacity ratio.
The hierarchy also tells us that the resultant ironing speed not only depends on the ironer itself and moisture retention, but also on buffer contents and category sequences, parameters we – to some degree – control by means of planning.
Stockpiles
With extensive use of buffers the laundry relieves the pressure on the production and its planning. But large quantities of linen and textiles with the customers reduce pressure on the production even more. With large customer stocks and frequent collections the customers feel secure and leave us alone. So in most cases the laundry keeps stocks and delivery frequencies high of its own accord. Otherwise the customers see to it.
Requisition system
And then we have come to one of the most serious causes of financial drainage in the laundry:
The stocks.
Stock sizes are determined by a basis supply of linen and textiles deposited with a customer, and the steady flow of supplies to and from the customer, which is often managed by the customer herself, by means of requisitions.
But linen and textiles are almost never the most important part of the customer’s business. In the hotels it is the well-being of the guests, in the hospital the constitution of the patients, in the slaughterhouse the carvings, in the metallic industries the cuttings, weldings and solderings, and in the prisons it is the rehabilitation programmes.
In neither of these places does the linen or the matron room have any prominent place in the business, or its handling held in any greater respect. The linen simply has to be available, abundant and flawless – preferably at no effort and no cost. Linen depot management usually bears the impress of this fact.
The majority of customer stocks are based on a requisition system of some kind, and the examples of misuse of these systems and mismanagement of the stocks are many, surprising and sometimes colourful.
To be in control
Requisition systems must be something the textile producers, or perhaps some lazy laundry manager, has invented. No other single factor has had greater influence on the exorbitant losses in the laundries’ linen stocks.
But the production is actually able to remedy this problem, by counting what is collected each day, from each customer.
Of course a requisition system is harmless in itself, provided the laundry keeps track of each item in circulation, i.e. when the laundry take on the responsibility for stock utilization – what in other industries is called the stock turnover ratio.
But this requires:
• sizing and distributing stocks according to each customer’s maximum need (in the long term),
• continuous control of the actual stock turnover ratio with each customer (in the medium term), and
• continuous transaction control (in the short term).
Think of it as if the customer once and for all is provided with a supply of linen equivalent to her maximum need in the season. The stock is hers. What she sends to the laundry is returned to her, as if it was her own linen. Most likely not the exact same pieces, but in the same qualities and quantities. Her stock circulates.
And the requisitions are, all things considered, not necessary in this laundry.
To be in control requires four things:
• a realization: Her stock consists of the linen on the beds (if it is a hotel), a safety buffer in her linen depot, and the maximum quantity returned by her to the laundry. The number of beds is constant, as is the safety buffer. Only the quantity sent to the laundry varies, depending on her activities and the longest interval between two collections (abbreviated LIBTC),
• frequent calculations of the stock turnover ratio. To avoid bringing the customer in short supply, and to avoid “dead” linen, the laundry has to keep track of the stock utilization and nip surplus and shortcomings in the bud,
• continuous transaction monitoring by the check-in. To be able to deliver, what the customer has returned dirty, each collection has to be accounted for, in quantities and qualities, and finally
• a system to record the data and carry out the necessary calculations. And it doesn’t have to be complicated or particularly fancy.
This is what it takes to gain control of the stocks. Only now the laundry is able to keep track of stock turnover, stock utilization, redistribute stocks, control the heavy losses of linen, and reduce the need for new linen supplies.
Stock sizing
Only now the laundry is able to size stocks according – not to some theoretical factor to multiply by the number of beds – but to the customer’s actual requirements.
The use of requisition systems is based on the assumption that the stocks need to be adjusted daily or weekly. But is that really necessary?
No, it isn’t. If stocks need to be adjusted, it is only in and out of seasons. During and between seasons stocks might as well be regarded as being constant, unless, of course, the laundry customers have alternating seasons. But who has?
Only very few. Maybe the requisition systems are convenient to the laundries, because they excuse them from managing the stocks, but uhm… what other industries leave it to the customers to administer their assets?
The calculations
Are simple. The customer’s necessary stock of a given article type is the sum of these four measures:
• the quantity currently in use by the customer (e.g. sheets on beds),
• collected quantity,
• if synchronous: delivered quantity, and
• a safety margin (of e.g. 20%),
– on the day when the sum of the first three measures are at maximum (= max. consumption).
LIBTC and transaction control
This teaches us two important lessons.
Collected quantities depend on the customers’ maximum consumption (of course), but also on the interval between collections. Longest interval between two collections (LIBTC) then becomes an important parameter to stock sizing. LIBTC is determined by the geography (mileage costs), the customer’s fear of shortcomings, or hygienic considerations, but in any case it ties distribution and stocks closely together.
To size stocks correctly we need to keep track of daily deliveries and collections. It is in the track records we find the maximum consumption by means of the formula:
[maximum consumption + number of beds] x safety margin,
– which could read:
[collected 17 + delivered 78 + 162 beds] x a safety margin of 20% = 308 duvet covers.
An example
Organized in a table the calculations could look like this (for duvet covers with a single customer):
Taken literally the calculations imply that the laundry would be able to recall 364 duvet covers from a single customer. Applied on all customers the calculations get rather interesting.
For the same article, duvet covers, the calculations for a number of the laundry’s customers might look like this:
Without changing distribution patterns, the production set up, responsiveness, customer service or anything else of importance, this laundry would be able to reduce the risk of shortcomings by redistributing 40 duvet covers, and reduce the need for reinvestments by recalling 652 duvet covers.
It is an established fact that laundries, by means of these simple calculations, have been able to reduce textile procurement by up to 90% in 2 consecutive years and generate hundreds of thousands of pounds sterling in working capital.
By means of transaction tracking the laundry regains control of flow and stocks, and is able to utilize their full potential. The customer is not burdened by cost demanding requisition systems, invoicing can be moved from the administration into the check-in, be carried out automatically by means of chip systems, according to incoming volumes.
Each article is accounted for, and in the long run the laundry accumulates an unequalled statistical data basis, from which it is able to control customer stocks and the laundry’s total textile volumes.
The laundry does not need to produce in portion to maintain this kind of transaction control. It works just as well in pool laundries. But it does require piece control and counting in the check-in.
And here is the problem.
Only very few laundries are willing to shoulder this cost, in spite of the advantages of controlling assets in both the short and the long run. So we need to make the laundry suppliers attack this problem when designing check-in and registration systems, in order to make multiple counting, reading and registration as simple, fast and cheap as possible.
Hans arbejde er baseret på års erfaring med virksomhedsdrift og ‑planlægning på mange niveauer, både i produktions-, service- og udviklingsvirksomheder (bl.a. 8 års erfaring som vaskeridirektør).