Warehousing vs.
Lean Production

Warehousing vs. Lean Production

When volatile demand meets production planning

What do magazines and cars have in common? The answer is that the raw materials required for their production are running out. This is because of the extreme fluctuations in demand during the Corona pandemic – the automobile industry needs semi-conductors, publishers need paper.

The global demand for electronic goods during the pandemic has meant that computer chips are no longer available in sufficient quantities for the automobile industry. According to a BBC article, semi-conductor manufacturer Intel believes that the bottlenecks will even run into the year 2023. Many German car manufacturers have been forced to shut down whole shifts. Opel recently announced that it would be closing an entire factory until the end of the year, furloughing all of its employees – although the demand for cars is currently picking up.

Printing plants, and therefore also publishing houses, are experiencing something similar. Paper is becoming scarce. While business insiders had surely seen this development coming, I, personally, was taken by surprise. The fact that the publishing sector, which has already been struggling with falling print runs for years now, is under pressure because of raw materials, is something I had not reckoned with. One of the causes is the lack of recycled paper. How has this happened? After a surplus of paper at the onset of the pandemic, many manufacturers switched their production to packaging material for the booming online and mail order trade.

What makes matters worse in both cases is that manufacturers have reduced their storage capacity over the past few years, making the strong fluctuations in demand harder to manage. Let us now take a closer look at two general strategies, “Make to Stock” and “Make to Order”, and consider their pros and cons with regard to demand fluctuations.

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Advanced Production Planning

Make to Stock: Security first and foremost

The Make to Stock strategy provides security in two respects. Here, goods are manufactured and then put into interim storage until they can be sold. This makes a production planner’s work a whole lot easier. Both personnel and material resources can be planned and deployed at maximum capacity. The number of set-up times is reduced. This not only saves costs initially but also removes a potential source of error which could lead to more rejects. In such a setting, the throughput in production would tend to be high whereas rejects would be at a fairly low level.

A further advantage of Make to Stock are the short delivery periods. This not only increases customer satisfaction but ideally it also binds them to the company on a long-term basis. And of course high inventories also help to cushion demand fluctuations during peaks.

Of course, this all comes at a price. After all, storage space is expensive. Not only does it have to be procured, it also needs to be maintained, insured as well as managed. These are only the direct storage costs, though. Stored materials also tie up capital since manufacturing costs have arisen for them which cannot be capitalized upon immediately. While high inventory during peaks in demand can guarantee security, conversely, they also pose a major risk. If demand changes abruptly in a negative direction and the inventory cannot be sold, it loses its value and has to be depreciated. If these are perishable goods, the problem becomes even greater. With regard to customer demand, Make to Stock has a further disadvantage: customers want more and more individualized products. However, the more versions of a product there are, the less convenient it is to keep large quantities in stock.



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Make to order: Profit first

With the Make to Order strategy the focus is clearly on profit and returns. Production only begins when an order has been placed. With this system, no costs can arise which are not covered by an order, giving manufacturers maximum security. However, there are other advantages. Since there is an order for every product, distribution warehouse space is kept to a minimum. This saves direct and indirect storage costs. In addition, less capital is tied up in goods in storage and there is a lower risk of having to depreciate products because they are unsaleable. One advantage for the customer which should not be under-estimated is that a Make to Order strategy usually permits a higher degree of individualization as the product has not been manufactured at the time of order. This meets the current customer trend of having customized products.

However, the Make to Order strategy does have its disadvantages. If the manufacture of a product only begins after the order has been placed, then this poses a major challenge for production planners. They have to ensure that personnel and resources can be deployed appropriately, that set-up times are reduced and that throughput is increased. When orders are constantly entered, then plans have to be changed frequently and the planning quality sinks as a result. The costs that have been saved in the distribution warehouse are offset by the costs of the warehouse. Here, there must be enough reserves of all products to execute customers’ orders as early as possible. And while we are on the subject of delivery times: with this strategy, these are naturally higher than when selling goods already in stock. Furthermore, the strategy is prone to peaks in demand as production capacity can only be increased to a limited extent.



Next Level Planning: The best of both worlds

As is always the case when we have two diametrically opposed alternatives, one has an advantage over the other. The ideal scenario would be to combine the advantages of both worlds and reduce the disadvantages. With mathematical optimization, both are possible. Let us show you how…

Demand forecasts with Predictive Analytics

What is evident in both of the above strategies is that they can only work if they are based on good demand forecasting. In the Make to Stock strategy, demand forecasts ensure that you only manufacture and store products for which demand exists. In the Make to Order strategy, these are crucial to decide which raw materials and semi-finished products have to be stored in order to safeguard against demand fluctuations.

When deploying Predictive Analytics, the system calculates how likely it is that a specific product can be ordered in a specific quantity at a specific time. At OPTANO, additional Machine Learning methods are also deployed. Its appeal is that you only need to “train” the system with real data and it automatically detects correlations and cross-dependencies. It is also possible to integrate external data into the calculation such as meteorological data and the quality of the forecasting will noticeably increase.

Production plans with Prescriptive Analytics

While production planning with the Make to Stock strategy probably produces better results than the Make to Order strategy due to its long lead times, it can only offer customers a limited variety of products. However, the trend is moving towards customized products. Using Prescriptive Analytics, as OPTANO does, optimal production plans can be created at the touch of a button. From millions and millions of possibilities, the system calculates, for example, the combination with the lowest set-up times, the optimum use of materials, the highest throughput and the lowest personnel costs (by avoiding overtime or shift bonuses as far as possible).

Making strategic decisions using what-if scenarios

Let us go back to the paper manufacturers who switched to manufacturing packaging materials. This demonstrates how such a decision can be supported by using what-if scenarios. Whether this method was actually involved in this example or not, the effect of such a decision could have been evaluated by using scenarios in advance. Based on various demand scenarios, different versions would have been calculated with the real costs and company KPIs to determine the impact of a change in strategy. What’s more, the system would have proposed the solution and procedure which would optimally contribute to a previously defined objective (for example, the objective to optimize profit).
This also applies to the question of when, how and to what extent a company should return to its original business model after the crisis. An invaluable advantage in strategic planning.

To sum up...

We are living in turbulent times. For this reason, production strategies should not be rigid, as in the examples described above. Fortunately, they are not like this in practice. Still, comparatively few companies are making use of the possibilities that mathematical optimization can offer in order to improve their processes. All too often, the good old spreadsheet is still used in manual planning which solely relies on the planner’s own wealth of experience. Yet, manual planning assumes that there are only a few changes in a defined setting. These conditions no longer exist, which became evident during the Corona pandemic if not before. So now it’s time for change –  and this is where OPTANO can help. Why not contact us?

Have you already got your factsheet on this topic?

In our factsheet Advanced Production Planning we present some of the use cases of Predictive and Prescriptive Analytics in modern production planning which you can download here.

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Michael Osterkamp
Michael Osterkamp

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Dr. Sven Flake
Analytics Senior Consultant