Optimizing manufacturing processes for efficiency can have a significant impact on your bottom line. Production optimization is usually never a one-off effort towards a momentary target but instead a continuous arrangement of activities planned for delivering business objectives. These long-term drawn-out goals make an extensive advantage by decreasing the expense of assembling, delivering better productivity, and expanding the number of products created per unit.
What is your ideal Point of Production?
The ideal production level is the ideal output level where the marginal revenue derived from a unit sold roughly equals the marginal cost to produce it. Profits could be maximized at the production level as long as the marginal costs are lower than the marginal revenue.
However, just like in any industry, there are numerous things that can keep manufacturers away from advanced transformational activities, regardless of whether it’s offering the plan to higher management or battling about backlogs encompassing business and staff assets.
Let us assume: You are a soft drink manufacturing company and you have four machines that make soft drinks in various types: zero sugar, regular, vanilla, and cherry along with five types of packaging: small and big bottles, small and big cans.
Scenario 1: Assume you need to produce 1000 small cans in cherry and vanilla flavour and 2000 regular in big bottles as it is presumed from the historical pattern of demand. Once the production begins, an unforeseen event like plant disruption occurs. This would result in waste, overproduction, increased lead-time, and many more.
Scenario 2: Assume a standard order of 500 cans and bottles of each type are needed to be produced based on the requirements. With a sudden increase in market demand, the supply needs to be altered. That would mean backlog in production, more raw material, more production machines, etc. resulting in more asset accumulation. The business can run profitable when the marginal costs are lower than marginal revenue and in this scenario, it is increasing.
For years of experience, it is seen that It generally becomes difficult to monitor, optimize, and apply decision making through manual processes. What does this mean for a manufacturing company?
Apply Automated Predictive Automation Approach
Taking scenario 1 into consideration:
In the case of an unforeseen event like plant disruption with pattern recognition and predictive analysis, predicting and benchmarking can be adjusted to avoid overproduction, waste, asset depreciation, etc. The model would help predict and influence the speed of the order of 1000 small cans in cherry and vanilla flavour and 2000 regular in big bottles.
In order to improve operational effectiveness by reducing time and costs on non-value adding activities like increased asset utilization to fulfill customer orders, a combination of machine learning and classic optimization techniques can be used. In the manufacturing sector, ML allows manufacturers to uncover hidden insights and enable predictive analytics. The key essential for a predictive maintenance application is to have enough information. The dependable guideline is you need multiple variables/factors to foresee.
Apply Automated Production Planning Approach
Taking Scenario 2 into consideration:
Companies with data science experts or with external support can create product mapping and grouping activity to identify combinations with similar properties & product entailments. This makes the production planning effective as it would save time and resources on manual calculations and the inability to address market demand and supply like increases to 200 regular soft drink bottles (unexpectedly).
A method could be that data optimization is done on all the machines separately that takes these desired variables into account, it then becomes easy to compute these dimensions by an orchestrator which is a large optimization model that overlooks to give the optimization process depending on the objective chosen.
Production Optimization & Plant Utilization in manufacturing is vital to guaranteeing proficient, practical, desirable results and sustained competitive advantage. AI and Machine learning combined together can deliver unseen benefits. With the right approach that edges all the three, your manufacturing line can turn out to be entirely beneficial.