Overcoming the pitfalls of inefficient production planning

The success stories of today, in the context of the supply chain, are the ones that are backed by rigorous and coherent planning. Production planning, which is central to the manufacturing exercise, defines the utilization of resources for meeting sales targets within the limiting constraints of the supply and production environment. The plan should set achievable production targets, specify resource allocation for the complete product portfolio for the planning period, and direct the flow of energy, labour, and costs to meet the same. If planning is optimal and efficient, it is intrinsic to ensuring greater profit and service levels in production.

Yet, despite the emphasis given to production planning, most companies fail to produce the desired results. The reason is inefficiency.

The Inefficiencies of Planning

The inefficiencies of production planning are observed in terms of an inefficient allocation of resources, leading to

a. Capacity Waste. Capacity waste occurs due to errors in capacity planning and allocation. Such wastes impede demand fulfilment and profit actualization goals. Most scenarios of capacity waste carry the risk of capital waste due to spending being directed towards inefficient processes, such as higher or lower production of a certain product or model, non-fulfilment of production or packaging criteria.

b. Underutilization of Capacity. Underutilized capacity or idle capacity implies suboptimal production that leads to poor equipment and resource utilization and therefore, high administrative costs. Further, idle capacity does not allow production to meet demand in full and on time leading to poor order fulfilment.

c. Overutilization of Capacity. Overutilization of capacity leads to production errors, employee burnout, greater machine downtime, and product quality issues. These outcomes are detrimental to the health of the organization.

The pitfalls of such inefficiencies are observed in terms of poor fulfilment and high costs.

  • Poor Fulfilment. Poor production planning leads to a failure in meeting production targets, completing tasks on time, and driving innovation. For instance, planning inefficiencies and errors do not allow production to respond uniformly to demand. When production fails to fulfil demand, either due to capacity underutilization or despite running at capacity, production delays may occur. Such delays may lead to a cancelled contract or a lost market opportunity. Further, the business becomes unequipped to handle rush orders and sudden demand spikes.
  • High Costs. When inefficiency causes misalignment of production with the demand, two high-cost scenarios may unfold.
  • There may be more of a certain product leading to high inventory spending and, therefore, greater marketing costs to move supply.
  • Businesses may need to invest in expensive last-minute capacity building, such as overtime, new hiring, etc. to capitalize on the demand.

Although capacity allocation can be designed to meet demand with a focused approach, an important requirement also of businesses is the reduction of costs and the maximization of profit. As per a survey, 78% of companies reported a marked enhancement of responsiveness of production to market changes but at the cost of burgeoning production costs. This is a classic outcome of inefficient planning. This is because, without planning optimization, businesses incur greater overtime expenses, additional labour expenses, machine downtime, scenarios of maxed-out resources, and high material costs on expediting shortages.

The Drivers of Inefficiency in Planning

Given the nature and outcomes of planning inefficiencies, it is important to understand the drivers of inefficiency in some depth. The inefficiencies of production planning stem from -

  • Both a granular and an aggregate view of products, resources, and production-specific criteria are required to specify the capacity allocation for a period. For instance, the production capacity for each production line and production plant will be unique. Further, special allocation rules may need to be specified depending on the production setup and the nature of orders.
  • Long-term production planning can stretch anywhere from between 3 and 6 years, while short term planning requires capacity allocation for about a year. Therefore, specification at all levels of granularity — days, months, and years can be a requirement.
  • Depending on the supply chain network for which planning is undertaken, capacity allocation may be done for one or more plants in a region or all plants in a country or continent.
  • Other processes can be part of the planning paradigm such as packaging. Therefore, the unique packaging criteria and constraints based on order destinations or order types need to be incorporated.
  • Various limitations of planning need to be factored, such as holidays, hours per day, number of work shifts, maintenance downtime or setup time, etc.

Besides such extensivity of data, the on-ground reality of production creates uncertainty. For instance, unplanned events, equipment breakdown, employee absenteeism, and other inefficiencies change and modify the actual capacity making a reliable plan difficult to achieve.

  • Data Collection Challenges. As mentioned above, production planning involves a great depth and width of data. This data needs to be procured from multiple sources and processes — plants, suppliers, distributors, etc. Such data collection from multiple data silos, to obtain a single version of capacity, requires time and effort which could be better spent in analysis and decisioning.
  • Data Accuracy Challenges. To ensure reliability and accuracy, planning needs to be driven by updated and relevant data. The numerousness of data sources for planning makes data validation a difficult undertaking. Further, with each change in the production environment, such as new product launch, production setup modification, or new orders, the data must be immediately integrated for planning.

Further, much of supply chain planning is still being undertaken with Excel. Data-entry errors and conflicting information are fairly common with spreadsheets. As per an estimate, nearly 88% of all business spreadsheets contain errors. Even one error can dramatically alter calculations and, therefore, the planning outcome.
Ensuring data accuracy is, therefore, a task requiring much vigilance and constant upgrades and revisions.

The Solution to Efficient Planning

The need for handling the complexity of production planning and the challenges of data collection, validation, and accuracy is one that can be fulfilled with innovation and technology. Recent strides in information technology create limitless possibilities of data processing, collection, and storage. Today, with cutting-edge research in AI and ML technologies, it is possible to fully model the manufacturing ecosystem of an organization with its network of suppliers, customers, production facilities, assembly lines, and warehouses and the host of environmental variables and complexities and constraints. Cross-functional processes can also be aligned with production planning to ensure a seamless flow of efficiency. For instance, the Verdis solution to production planning provides production, packaging, and transportation control all within a single interface.

Automation and optimization algorithms, in particular, have much to offer towards addressing the challenges of production planning.

Through automation,

  • The data residing in Excel sheets and ERP systems can be seamlessly integrated into a single data layer. Any data change is easier to manage as the change is notified and translated into the data layer, therefore, removing the encumbrance of manually monitoring and updating data.
  • Through machine intelligence, the validation of data for reliability and accuracy is executed. Changes in operational data are cross-validated with the Master data for accuracy, therefore, removing conflicting information.
  • By reducing the number of human touch-points, many instances of errors, whether data entry errors or calculation errors can be reduced. Further, since planning is undertaken in an automated platform, the effort given to the maintenance and management of Excel sheets is no longer required.
  • Considering that planning is undertaken in one platform, the same can be made available for viewing and analysis so that planners can identify and understand the impact of the proposed output. Through discussion and collaborative input, trade-offs and measures for maximal fulfilment of goals can be decided.

Through optimization, the complete data space is mapped, and different aspects of production such as costs, order fulfilment, and capacity allocation are weighed for optimal productivity of systems. In Verdis PPC, optimization occurs for production cost, packaging cost, freight cost, and order fulfilment. This guarantees the optimal utilization of production systems for high-profit actualization.

The benefits to such an approach are many.

  • Planning can be undertaken as repeatedly and as frequently as needed. As automated planning is fast, new information or change of criteria/rules can be input for new simulations.
  • With high fidelity to the constraints and objectives of planning, errors and inefficiencies are rare.
  • Capacity allocation can be driven by ecosystem rules such as priority of capacity allocation can be directed towards specific models or orders. For instance, in PPC, prioritization can be ensured for specific models, markets (export or import), as well as dispatch routes (sea, rail, or road). Similarly, packaging and lot sizing specifications and rules are actively incorporated.

The approach to production planning determines the business response to incoming demand besides defining production levels, product availability, lead times, and operating costs. Although the challenges to efficient planning are many, it should not be a deterrent as the solution exists in terms of technology utilization. Through smart technology leverage, a smarter approach to planning can be accomplished.

Originally published at https://holisollogistics.com on November 17, 2020.

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