Enterprises intent on success realise Optimisation is fundamental to staying efficient and competitive. Creating accurate, timely and repeatable schedules that deliver target efficiency performance is in the interests of every stakeholder of the business.

Shorter and shorter ‘SKU make’ cycles, lower inventory holdings and improved customer response times create demands on the scheduling team to define the most efficient path to production. The optimisation of a production schedule has two primary focus points: (i) to service the customer demand for on time delivery (ii) to ensure the optimal cost performance is achieved

For most manufacturing and conversion operations it is a cognitively difficult and extremely challenging to schedule operations. Work centre constraints, product range / variety due to complex BOM’s and sequencing combinations with their own constraints all heavily impact throughput and cost. Ultimately, P&L results relate to the quality of information used by the scheduling team to establish operations parameters and to identify the best available result from a set of alternatives, which is about the smartest choices and trade-offs that are being made.

Many of the existing scheduling support operations cannot be seen as viable options going forward. The hybrid approach (shadow systems, Excel and the like) in which an expert scheduling practitioner must harvest data from many interfaces, no longer supports the expansive array of information sources and interpretation of supply chain factors, that are inherent to create a schedule of high efficiency in which stakeholders have confidence. Contrast the contemporary approach of relentless focus on optimisation, with the schedule derived from hybrid database interfaces, planning system outputs and various manual small systems tools – a schedule that is un-challengeable, constrained as it is by a template approach built from heuristic knowledge over time, is costing you more.
EVRIS empowers the scheduler to leverage every knowledge dimension so that the path yielding greatest efficiency is known, selected and confidently executed. The risk is avoided of relying on unsubstantiated assumptions and unchecked master data, that inevitably follows reliance on fragmented knowledge and data capture through hybrid shadow systems.