In this era of ever increasing globalization and digitalization, companies have to take on the major challenge of global transformation, not only at a strategic level, but also at organisational and operational levels. In this context, the Supply Chain changes from being a fixed solution to an ever changing pathway which, depending on the way it is dealt with, can either become a burden with the potential to pull a company down, or an ever-present opportunity to drive value creation. The supply chain becomes a true lever allowing organisations to carry out transformation projects of that efficiently support the company’s direction, from high level strategy down to the most operational steps. The performance, reactivity and flexibility of the management and planning processes of the supply chain are key success factors for all companies, regardless of size or business sector. A recent study by Cabinet Deloitte illustrates this. It reveals that 79% of organizations with high performing supply chains have turnovers that are clearly higher than the average for their sectors.

Very complex decision-taking issues

From the supply of raw materials down to the sales and the forecast, through the management of stocks and production and routing decisions there is a multitude of decisions to take at each step of the supply chain: how many items we should produce, which resources we need to use, when and what is the right quantity to supply to where etc. The consequences of these decisions are extremely important and the stakes could not be higher: increased turnover, decreased cost of production, purchasing and call-off strategies, transportation, reduction of working capital requirement etc. Thus, today companies have to be able to take these decisions in a more efficient and reliable way within a more and more complex context: ever-increasing numbers of products with variations that disrupt established production norms, a more and more spread out supply chain (with options and differing risks), multiplication of distribution channels… The difficulty of taking these decisions does not end there!

Indeed, as numerous publications of specialized literature on the subject of operational research illustrate, Supply Chain planning issues are known to be among the most difficult to solve mathematical problems of optimization and are intensively studied. But this is more than just an intellectual pursuit; the solutions that are driven by this continuous research are becoming the levers used in the planning strategies of companies with high performing supply chains

The possibilities offered by the digital intelligence

Thus, to address these issues, it becomes essential to use tools that allow decision-makers to understand and evaluate all the stakes together in order to make the most efficient choices. Decision-making tools such as advanced planning systems (APS) enable decision makers to represent and formalise the flows, constraints, decisions and strategies to enable global visibility of the Supply Chain. These solutions can bring value at different levels, depending on the approaches adopted and the maturity of the organisation.

First of all, they allow the automatization and the modernisation of the processes. They free up the demand management and scheduling teams from low value-added tasks by automating the data gathering and calculations. They also offer management by exception as well as an adapted ergonomics supporting the decision-taking process (flexible navigation and power, adapted to different levels, KPIs…). Demand and supply planners can then dedicate their time and energy to problematic cases that require analysis, adjustment or arbitration.

Faced with complex planning issues, the decision-support tools enable us to move towards operational excellence and to guarantee an efficient implementation of company strategy. The efficiency of new techniques in operational research coupled with current computing power makes it possible, today, to solve more and more complex and vast problematics. Not only it is possible to define consistent and realistic plans for the whole supply chain, but also to get the guarantee that the plan provided fits with a global optimization of defined priorities. As a result, we combine efficiency and performance, and we make sure that the decisions taken at every level (S&OP, tactical planning and operational planning) and on every level of the Supply Chain are consistent.

Finally, with the help of these intelligent tools, the Supply Chain itself can become an engine of optimization and transformation for the company. The knowledge of the global optimization approaches (end-to-end Supply Chain) and the possibilities of analysis and simulation offered by some APS solutions not only allows us to identify the bottlenecks, the gain factors and levers of optimization, but also to simulate and suggest new strategies (stock politics, sourcing strategies, priorities evolution, transformation of management techniques, activity mutation, etc.). It then becomes possible to anticipate the impacts of the strategic decisions as well as measuring the performance in an objective way (indicators, KPIs, value-creation, etc.). The supply chain plays a major role because it does not only apply the strategies but also actively participates in the decision-making process.

In order to make good use of these tools and benefit from these possibilities of optimization, it is also necessary to understand the limits.

The necessity of a synergy with the human intelligence

The success of a Supply Chain planning project involves three essential elements:

• High quality and timely master and transactional data
• Reactivity and quality of the plan adapted to the business needs and objectives
• Well controlled process by teams (analysis, management…).

It is essential to choose the planning granularity wisely (time bucket, granularity, geographical level, etc.). This choice will have substantial impacts on the planning process (data availability, management complexity, calculation time) and must be adapted to the available resources (teams and material) as well as decisional needs. Organisations need to find the right balance between the level of detail and the optimality necessary to achieve reliable and exploitable results on the one hand, and the complexity management on the other hand.

Obviously, the plans that are determined are only right within the limits of the accuracy of input data. The guarantee of the existence, exactness, unicity and longevity of the source data are usually the consequence of substantial effort, often requiring a review of the operational and information processes. Such reviews often yield unexpected benefits elsewhere in the business. The further you would like to go in search of optimality, the more sensitive the solution will be to the accuracy of your data.

We also need to remain pragmatic and not imagine that an advanced planning solution, no matter how clever and high performing it might be, can do all the planning and forecast work on its own. Indeed, decision support solutions cannot take into account the exhaustiveness of the context nor the subjective perception, which remain an important element in any decision taking (exogenous knowledge, experience, intuition, risk balancing etc.). It is unrealistic to imagine that a system can depict the exhaustiveness of the rules, constraints, specific cases and exceptional events inherent in any supply chain. Although the editors are developing more and more methods to process and analyse all the digital data available, whether they are endogenous or exogenous, these techniques (Big Data, Machine Learning, etc.) remain today inapplicable in the context of advanced solutions for optimization.

For an optimal management with a minimum of risks, it is thus necessary to rely simultaneously on human expertise and on automatic calculation tools. The decision support solutions must be able to provide results that are reliable and in most cases, directly exploitable. Additionally, the solution should supply analysis and management by exception tools to allow the planner to process the remaining cases, by managing the plan levers or by imposing, if needed, a decision in the plan. In this case, planners are freed from the tasks of gathering and processing data in order to spend their time in a more productive manner; to manage exceptions and make higher quality decisions that deliver real value based on ever more accurate models.