Added value from end to end

Dr. Dominik Eberlein

To stay competitive, companies need to constantly adapt their supply chains to market needs. New markets, for instance, demand an extension of the network while mergers call for the integration of additional sites and the re-organisation and optimisation of the existing network structure. Every modification has an effect on the entire supply chain. Thus, local improvements can sometimes lead to a deterioration in overall performance. Transport costs, for example, can only be optimised at the expense of prod-uction costs. It is this complexity and these interactions that make strategic planning all the more challenging. Decisions need to be made holistically, taking into account the supply chain as a whole. In order to derive the global optimum from end-to-end-optimisation, it therefore makes sense to begin by evaluating action alternatives. In what-if scenarios, possible strategies are simulated with a view to deducing and assessing their impact on business. Companies are then in a position to identify crucial success factors at an early stage, take proactive measures and reduce potential risks.

One of the most urgent strategic issues which causes companies to resort to what-if scenarios is the adaptation of an existing network to current market developments. The opening up of new markets, for example, often means planning additional production and distribution sites, whereas mergers usually necessitate the re-design of the entire supply chain. Simulations allow an integrated evaluation of diverse planning scenarios, revealing the impact of strategic decisions as well as the significance of single cost factors such as transport costs or delivery strategies. Processes and their interdependencies become transparent, helping to arrive at the right decision.

Normally, the reduction of inventories automatically generates major saving potential. In the context of integrated, end-to-end supply chain optimisation, however, inventories cannot be reduced at the expense of delivery service. In fact, a fully integrated approach must take account of the simultaneous optimisation of both factors. One way to do this is to find the best pos-sible compromise which, of course, must result in the highest cost reduction for the supply chain as a whole. What-if scenarios thus deduce optimal stock quantities and allocations as well as the best supply strategy. This is the only way for companies to guarantee the availability of goods while simultaneously raising delivery service and quality. In addition, storage costs and transportation times and routes are minimised while capital lockup is reduced. The effects on the network of alternative distribution strategies are likewise evaluated. One strategic question here might be whether it is more efficient to supply customers from regional warehouses or to reduce costs by centralising the distribution structure.

Globally operating companies in particular need to be in a position to answer strategic questions such as this. In order to evaluate potential business scenarios and draw on them for decision making, however, a sophisticated planning system must not only enable a detailed representation of highly complex supply chain structures. Most of all, it needs to be ca- pable of handling whatever volume of data arises. Conventional planning systems based on linear programming (LP) are soon stretched to the limit when it comes to modelling and calculating scenarios on this kind of scale.

With its further development of the cost scaling (CS) algorithms explored in depth at Stanford University in the eighties, Axxom has introduced a special technological approach and a trend-setting innovation for efficient optimisation of highly complex business networks. By applying CS algorithms, it is possible to evaluate and optimise strategic questions in extensively networked business scenarios, in other words networks with several hundred production sites and distribution centres as well as thousands of products and customers. Although scenarios of this magnitude are, and have always been, common in globally operating companies, there has so far been no way of calculating an overall, optimal business plan from end-to-end. Conventional solutions have therefore tended to reduce the complexity of a given scenario by clustering, that is by pooling customers and products. Yet this simplification of the problem at hand not only results in increased modelling effort, it above all distorts the representation of the actual planning situation. As a result, part of the optimisation potential is lost in the mere modelling process. CS algorithms, by contrast, allow supply chain structures to be represented down to the last detail. Moreover, their high performance enables a fast and realistic calculation of pos-sible strategic scenarios. The strength of the technology is immediately obvious when compared to conventional approaches that rely on linear programming: simply by calculating a scenario of average complexity – which could also be worked out by an LP solver – the CS solver taps 98 % of the optimisation potential. At the same time, the approach only requires a fraction of the computing time and much less storage capacity than the LP solver. Introducing CS algorithms therefore equips companies to efficiently simulate, and realistically represent and evaluate, actual supply chain scenarios from end to end. Risks can be identified at an early stage by systematically expanding the scope.

Strategic supply chain optimisation is increasingly gaining in importance in industry, because the practical savings and advantages it generates are today indispensable for business efficiency and competitiveness. However, since a company’s supply chain structures change very quickly with the market, it no longer suffices to initiate strategic, project-oriented planning. The next stage must be to enforce and operationalise strategic planning, in other words to respond continuously to market needs by implementing repetitive optimisation. Only operationalised, end-to-end optimisation based on CS algorithms can enable companies to weigh up action alternatives quickly and in a well-considered way. The high performance of these algorithms, combined with their extensive modelling options, lets managers realistically design and re-design their supply chains as and when necessary. It is this kind of end-to-end optimisation – from supplier to production and distribution, all the way to the customer – that will bring companies crucial added value today and in the future.

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