Classic value stream design

Ask your logistician or production planner about side effects

You can also use value stream design. to make your value chain more productive? Probably yes, because value stream design has developed into a standard tool for optimizing the value chain in companies and beyond company boundaries over the last ten years. Wherever you talk about logistical or production organization issues. The pen is immediately to hand and a first rough value stream is thrown onto paper.

There is probably no more transparent way to visualize the architecture of a value chain than through value stream design. A tool that gives you such a quick overview must also be well suited to optimizing the value chain. At least that’s what it looks like at first glance. This is why value stream design is carried out – often at great expense in terms of time and effort. Representative products are worked out. the processing and storage stages are identified. Numerical values are compiled and throughput times. Storage ranges, batch sizes, inventories, cycle times. etc. are determined.

All of these key figures help to gain a more precise understanding of the mechanisms of a company’s value chain. Unfortunately, the potential for improvement derived from such a classic value stream design often turns out to be wrong.

The concept of value stream design started its career in the automotive industry. Compared to other sectors, the automotive industry is benefiting from a stabilization of demand. by expecting customers to queue up with their orders. The more uniform the demand, the smaller the production batches and the shorter the throughput times. the closer the characteristic values from a classic value stream analysis and optimization are to the later reality. But even in the automotive industry, the uniformity of demand decreases the further upstream a supplier company is in the supply chain. Nevertheless, fluctuations at many suppliers remain low compared to most other sectors. In many cases within and outside the automotive industry, however, batch sizes and throughput times cannot be made arbitrarily small without incurring very high (investment) costs.

The greater the fluctuations in demand, the larger the economic production and procurement lots and the longer the throughput times, the less helpful static considerations are when designing a system.

An example from practice:
An average of 3,153 units are produced per day in one manufacturing process. The production capacity is 4,000 units per day. A quick calculation shows us that our average capacity utilization is just under 79. That sounds like a good value. to produce as synchronously as possible with demand. Under the given conditions, we can manage with an average finished goods inventory of 7,000 units.

However, a closer look at incoming orders shows that they fluctuate significantly. On many days, the company receives an order volume of significantly more than 4,000 units. If our production hopper can let through 4,000 units a day, but 10,000 units come in one day, for example, then the order queue grows and the throughput time increases by one and a half days. Increases the throughput time. This increases the work in progress and the stock required downstream of the production process.

A dynamic simulation of the value stream under consideration shows that the classic average values convey a completely false picture. Instead of two days lead time, four days have to be scheduled and either the finished goods stocks have to be increased from 7,000 to almost 8,700 or the flexibility costs of production are 4 times higher. Which alternative strategy we should pursue is now quite easy to determine from a cost comparison. In any case, the optimal value chain is very different from the static view of value stream indicators.

For classic value stream design, ask your logistics or production planners about the expected side effects; hopefully they know them.

Picture of Prof. Dr. Andreas Kemmner

Prof. Dr. Andreas Kemmner

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