If retailers do away with warehouses, the industry doesn’t have to play the buffer – everyone is talking about Industry 4.0

But what use is a production economy that is networked down to the last detail if fluctuating demand needs to be met immediately and retailers no longer operate any warehouses? A finished goods warehouse at the manufacturer that is always full cannot be a solution either. But how can value creation along the supply chain be planned in such a way that a high level of delivery readiness is achieved with the lowest possible inventories?

With solutions such as ‘Logistics 4.0’, the aim is to bring sales figures between manufacturers, retailers and end customers even closer to the point and, ideally, respond with intelligent machines in batch size 1. However, all these efforts in the sales channel from the finished product at the manufacturer to the end customer do not lead to success if manufacturers always want to meet these sometimes highly fluctuating demand reports as quickly as possible, for example within 24 hours.

To do this, they would either have to keep very high stocks in the finished goods warehouse or have manufacturing capacity in production. Both of these are superfluous outside of peak times and are therefore expensive and tie up capital. Industrial manufacturers are therefore looking for ways to implement all of this more cheaply in order to ultimately be able to offer retailers and end customers attractive prices. The potential is enormous: in addition to tying up capital, which in itself leads to dead capital, there are as much as 19 to 30 percent of running costs in inventories, which result from capital costs, insurance, administration, provision of storage capacity and so on. Ultimately, the market has to pay these costs if the logistics chain is not right. But how can you increase delivery readiness and reduce stocks at the same time?

Optimize scheduling processes

First and foremost, this is a question of better scheduling processes. For example, fast-moving items can be delivered at shorter intervals. This reduces storage capacity. In turn, products that are rarely in demand can be produced on demand and removed directly from the finished goods warehouse. In addition, the logistical decoupling point can be moved as far upstream as possible through modularization, thus reducing inventories across the entire supply chain. In practice, however, many logistical variables are planned on instinct and executed without considering the interrelationships. Filling a pallet space in a truck with slow-moving items just to save on freight costs quickly drives up stock levels. The entire supply chain must therefore always be kept in mind when implementing individual optimization measures.

One company from the metal industry that supplies DIY stores, for example, has managed to significantly reduce its stock levels while increasing its delivery readiness: GAH Alberts. The manufacturer of fittings, profiles and fencing technology, among other things, was able to reduce its stocks by 13% in the short term and a whopping 53% within nine months. More than half of the finished goods warehouse was therefore stocked with material that was not immediately required, in an effort to achieve a high level of readiness for delivery.

A manufacturer of lamps and luminaires had to overcome the challenge that some of the components for its products are manufactured in China. The delivery times here are between 60 and 150 days. However, the customers, specialist and wholesale companies, demand maximum delivery readiness at all times. Around 20,000 customer orders are received every day, most of them with a delivery time of 24 hours. A sufficiently high level of stock is therefore required on the procurement side. If the demand situation on the customer side changes, the items that were previously procured well in advance are no longer required and therefore become excess stock, which must be avoided as far as possible through clever planning.

Managing fluctuations in demand

Almost every company has to contend with fluctuations in demand, be it when launching new products or discontinuing old ones, during competitive campaigns or when demand generally changes due to seasonal fluctuations, economic cycles and other crises. In the peak phase of demand, the available production capacities are often not sufficient to produce completely in line with the market. This means that the quantities that are planned to be sold sometimes have to be produced weeks or even months before the planned sales. If sales planning is inaccurate, sales cannot be realized due to a lack of material and customers may incur contractual penalties. Other materials remain in the warehouse as slow-moving items.

Method and tool skills

Article portfolio must be structured according to ABC/XYZ criteria in order to optimize scheduling

Special methodological and tool skills are required to master such challenges. At the lamp manufacturer, for example, a comprehensive classification of the product range according to

• ABC = economic significance,
• XYZ = regularity of consumption,
• STU = number of customers per item and
• ELA = life cycle

carried out …

These classification characteristics are important parameters for deciding which planning and scheduling parameters should be set for which article. On the basis of these classification characteristics and other influencing variables, a set of rules was created that precisely defines which article classes are to be planned and scheduled and how. With such fundamental analyses and sets of rules, existing stocks can be quickly reduced and, at the same time, delivery readiness can be increased.

But all these analyses and the measures derived from them are not enough if they cannot be applied with the help of a suitable software system. At one valve manufacturer, for example, the reporting and safety stocks had to be determined without system support from the ERP. It was particularly noticeable in the case of safety stocks that they were calculated in different ways depending on the MRP controller responsible or were merely the result of empirical values. In the scheduling of purchased parts, for example, stocks were checked once a week as a general rule instead of having the ERP system indicate the need for action. Despite the ERP system, the overall process was highly manual, very time-consuming and therefore prone to errors despite the utmost care.

The example shows that in order to be able to use all of the “big data” from Supply Chain 4.0, many series and variant manufacturers must first structure the scheduling processes upstream of the finished goods warehouse sufficiently and optimize their scheduling; this is no trivial undertaking.

Good disposition is a complex matter

Just how complex the scheduling task is can be seen from the amount of master data required: Depending on the cut of the item, you have to take care of up to 130 logistical parameters, some of which are interdependent and influence each other. The right settings cannot be found by instinct or determined with simple calculations. However, major mistakes are made when individual parameters are combined for the sake of simplicity, for example by mapping safety stocks for fluctuating demand, safety stocks for fluctuating production throughput times and safety stocks for fluctuating delivery times of upstream suppliers in a common safety value. Cumulatively, this can only lead to more stock. Optimum scheduling therefore requires correspondingly differentiating tools.

ERP alone is not enough

Most companies already have a software tool for scheduling purposes: the existing ERP system or corresponding extensions. However, most ERP systems are generalist in terms of their functionalities and offer insufficient options for demand forecasting and scheduling. There are practically no automatic mechanisms for the continuous optimization of scheduling parameters. When it comes to sales forecasting, virtually all known ERP systems work exclusively with statistical methods that assume a so-called “normally distributed” demand, such as the mean value method or exponential smoothing. In practice, however, normally distributed demand is rarely encountered. As a result, calculations based on the assumption of normally distributed demand can lead to systematically incorrect demand forecasts and inventory errors of up to 40 percent.

Precise special tool for dispatchers

It should therefore be noted that although forecasting and scheduling tasks can be performed with an ERP system, the results are often far from optimal. In order to achieve better planning and scheduling results, planners need advanced planning and scheduling software, or APS software for short, which supports simulation-based planning automation. Such precision tools, such as DISKOVER SCO from SCT, are much more precisely tailored to planning and scheduling tasks than generalist ERP systems, offer much finer forecasting functionalities for improved planning and can therefore predict actual demand much more accurately. In practice, there is a great need for action, as companies with a varied portfolio can regularly save hundreds of thousands to millions of euros in unnecessarily stored material and therefore dead capital. This is important liquidity that is better invested in solutions for Supply Chain 4.0. Such APS software is generally suitable for companies with a sales volume of around EUR 10 million or more.

The impact on ongoing operations

What effect do such tools have on a company’s economic situation? The high delivery reliability of often 97-98 percent leads to high customer satisfaction. The company’s liquidity improves due to reduced inventories and increased sales, and earnings increase due to reduced warehousing costs and sales growth.

Part of the inventory reduction is often achieved by reducing order quantities and ordering more frequently. Although this does not necessarily lead to better conditions for suppliers, it can mean that suppliers can now plan their batches better because they work more closely with the customer. On the scheduling side, however, the increase in the number of ordering processes with a well-adjusted set of scheduling rules does not require more personnel in scheduling, as many processes are automated and the schedulers can therefore concentrate on the real problem cases in scheduling. Overall, users of APS software report that they have been able to make their scheduling much more efficient and thus achieve better results with less effort. This mini-max principle of reduced inventories with better delivery readiness is also reflected here.

Technical article, ZWF – Issue 12 2014, p. 973 – 975ByAndreas Capellmann (SCT) and Andreas Kemmner (Abels & Kemmner)