How Analytics Can Help Optimise Production Planning?

How Analytics Can Help Optimise Production Planning?

Introduction

"Every day, the process repeats itself. The salesperson calls, asks for production dates for an order with 10 different products, produced in five different lines and in varying quantities. While I am trying to answer this first request, two others have already been received. How do they expect me to ensure that I manage this process and at the same time come up with a plan that can guarantee the best efficiency of all the lines? But I don't even spend two minutes on this task - a line supervisor has already walked into my office warning me that one of the machines is stopped for the rest of the month. I'll spend the next two days replanning."

This testimony describes, in general terms, the daily life of most planning teams, with no visibility of the global impact of different order allocation combinations and with a large part of their time generating new production plans due to unforeseen events or strategy changes. How can we deal with this uncertainty?

Over the last decades, technological evolution has enabled the technical and organisational development of the vast majority of companies. Production flows include more automation and restrictions which culminate in increased complexity in the management and control of the entire chain. In this context, the best planning teams use decision support tools to establish plans that maximise the profitability of production activity, reducing set-ups and optimising delivery times.

So, how can these data analytics-based tools materialise the constraints and complexity of production systems?

Planning layers

In the Pull planning models included in the KAIZEN™ methodology, there are three areas of action, with different degrees of detail, with the aim of guiding the chain in accordance with demand:

  • Strategic Planning (High Level): Decision at the commercial, logistics and production levels regarding which references should be kept in stock (MTS - make to stock), as opposed to those that should only be produced when orders are placed (MTO - make to order).
  • Capacity Planning (Medium Level): Management of production capacity, determining which equipment and shifts are necessary to meet the proposed deadlines and objectives according to the demand variability.
  • Execution Planning (Low Level): Sequencing of production orders, allocating them to a machine and a start time, respecting the sequence of operations and maximising efficiency.

Note that these three layers are not independent and, in fact, are closely related. The strategic layer, which defines the level of service for each of the references, makes it possible to compromise, not only with external customers, but also with internal customers.

Similarly, the execution layer cannot start its sequencing work if the necessary and adequate capacity to meet the demand is not present at the mid-level. Against this background, it becomes vital to coordinate information from the three planning levels in a consistent and coherent manner.

Analytics as a pillar of decision support tools

For each of the layers, working and decision-making support tools are needed. Traditional spreadsheets allow you to quickly analyse simple data and information to determine the first steps of a Pull strategy, categorising MTS and MTO references, and making weekly or monthly allocations according to installed capacity. The same spreadsheets can also transform this weekly planning into a production sequence, despite their limitations.

However, the increased complexity and constraints of the production process, together with the massive data collection enabled by Industry 4.0 models enhance and trigger the need for new planning tools. Spreadsheets can no longer meet this challenge and the solution is to rely on optimisation algorithms. These new alternatives make it possible to model the production process in a holistic way. The final objectives to be optimised are clear, but they hide the complexity that truly limits manual planning - the integrated vision and guarantee that the plan fully meets the production constraints.

Thus, one of the approaches is to resort to robust optimisation engines that allow us to obtain the optimal production sequence in the face of an established objective (for example, minimising the number of set-ups or work in progress - WIP). Alternatively, and whenever faced with highly complex problems, these optimisation engines can be replaced by a set of heuristics, that is, a set of rules that allow the construction of a solution applied to the reality of each company, and to the particular constraints of each process.

With these tools, planning and supervision teams have access to a set of functionalities that allow them to monitor, predict and act in advance. This truly contributes to the change from a ‘reaction-based’ paradigm to a reality where the organisation can plan in a more solid way and clearly assess the impact of the reaction effects. In this regard, it is important to highlight some of the functionalities that allow the user to incorporate this new visibility concept:

  • Online recalculation - the ability, should any change occur, such as the entry of a new order or an equipment breakdown, for the system to automatically recalculate a new solution to incorporate the new reality.
  • Blocking of certain equipment - the possibility (for certain priority machines) for the system to allow the allocated production to be predetermined and to plan with this specific allocation.
  • Integration with existing ERP and MES systems - a complete integration with existing databases which avoids the need for manual importation and exportation of information (e.g. without using spreadsheets).
  • Business intelligence tools - the creation of dashboards that support the decision-making, allowing to easily verify the impact and control the production process.
  • Monitoring and interface solutions - allow visual control (such as Gantt charts) of the production of each piece of equipment as well as the tracking of the main process indicators.

The online recalculation and integration with the various ERP and MES systems allow teams to be freed up for higher value-added activities, such as focusing on the discussion of various alternatives for prioritising orders, in order to provide a better level of customer service.

The online recalculation and integration with the various ERP and MES systems allow teams to be freed up for higher value-added activities, such as focusing on the discussion of various alternatives for prioritising orders, in order to provide a better level of customer service.

These algorithms, after creating and validating the model, establishing its variables and main constraints, have proven to be important allies of the planning teams when it comes to necessary replanning and determining the new production scenario caused by the change of a certain factor.

Such solutions, combined with the monitoring of dashboards updated in real time, allow planners, supervisors, and sales staff to follow the production of the most critical orders and to base decisions and actions on data, namely regarding urgent requests. Often, the anticipation of the production of a certain reference implies a reduction in the efficiency of other lines, generating more set-ups and stocks. BI tools can help you understand and monitor the key indicators of each decision, allowing you to manage priorities seamlessly.

Finally, the use of sensors and the high connectivity between all systems in a factory, enhanced by Industry 4.0, allows not only to detect problems faster, but also to react almost instantaneously. More specifically, the breakdown detected by a sensor can automatically trigger the recalculation of a production plan and notify the sales personnel responsible for the orders affected by this breakdown.

Another example would be a quality fault detected in a piece of equipment that requires the launch of a reaction - an additional quantity of parts to be produced that will naturally occupy that equipment for longer and will have an impact on the delivery of the final product.

In short, the paradigm of production planning teams is changing. The manual management of production plans, without visibility of the impact of each decision, making the coordination process of the various equipment and constraints time-consuming, has been replaced by decision support solutions. The implementation of advanced planning systems allows, not only the ability to analyse in detail each production option available, but also to generate those same options and production sequences.

We live in a new reality, in which analytical solutions go beyond the purest concept of automation. The aim is to give these intelligence solutions the ability to integrate the constant challenges that reflect the demand volatility. This new reality is the essential methodology to approach production planning in a more holistic and efficiency-generating way throughout the entire chain.

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