Production Scheduler in Indonesia

In the mass production assembly plants commonly found in Indonesia’s two-wheeler and four-wheeler industries, production planning and workload planning are inherently interconnected. Since production plans are formulated based on quantity, there is a strong demand for workload plans to also be viewed on a quantity-per-machine basis. Typically, production planning and workload planning are required on a daily or shift basis.

Indonesian planning personnel mentally approach the process by considering questions such as: "How many units should this machine produce today (or in this shift)? How many units exceed capacity, and when should those excess units be produced? If capacity is insufficient, how many hours of overtime are required?" Consequently, there is a need for a PSI (Production, Sales/Demand, Inventory) table to compare production quantities, consumption quantities (Sales or Demand), and inventory quantities within the system.

Since production planning must be compared against actual results to monitor progress, when creating a one-week plan, all displayed figures are based on inventory forecasts at the time of planning. As production results are input, inventory levels fluctuate, and when the next week’s plan is created, the forecast-based figures are reset according to the current inventory levels at that time.

When systematizing production planning operations in Indonesia, instead of focusing on detailed minute-by-minute scheduling, it is simpler and more readily accepted by on-site planning personnel to allow production results to be entered and referenced on a daily or shift basis.

This blog is intended to assist Indonesian manufacturers who recognize the need for systematizing production planning (scheduling) but are unsure where to start. It aims to provide insight into what kind of manufacturing system is suitable for Indonesia.

Evolution from MRP (Material Requirements Planning / Manufacturing Resource Planning) to APS (Advanced Planning & Scheduling)

MPS (Master Production Schedule) is created by subtracting product inventory from received orders and forecasts while considering minimum stock levels. Ideally, the MPS is developed through agreement between the sales department, which prioritizes shipping schedules and forecasts from a customer service perspective, and the production planning and inventory control (PPIC) department, which wants to consider on-site conditions.

However, in Indonesia, the MPS is often created mechanically by the production planning department based on sales forecasts and shipping schedules received from the sales department. The sales department rarely intervenes in the MPS creation stage and only becomes involved when shipping delays occur, rushing to the production site for follow-ups.

The purpose of MRP is to create a gross production plan to meet net requirements during normal production. However, production plans created through MRP lead-time adjustments rely heavily on human efforts, such as on-site adjustments and production preparation (capacity planning and sales planning). When this approach reaches its limits, an imbalance between production and capacity planning arises, resulting in inefficient manufacturing instructions, unnecessary overtime, weekend work, and excess intermediate inventory.

To address this issue, the introduction of APS (Advanced Planning and Scheduling), which considers finite capacity without time buckets, is explored.

Scheduling Logic of the Production Scheduler Asprova

In Asprova’s rescheduling logic, order collection commands generate order lists from order tables, while work collection commands generate work lists from work tables during the list generation phase.

During the command execution phase, in the order explosion (Explode Orders) step, the system references the manufacturing BOM and generates replenishment orders to compensate for missing work input instructions in the order list. Work input and output instructions are then created for registered and replenishment orders.

In the order assignment/pegging (Assign/Peg Orders) step, dispatching rules and resource evaluation properties are used to generate work usage instructions and allocate work from the work list. First, all candidate resources are tentatively assigned in a provisional allocation. Then, during final allocation, tasks are reassigned to the highest-rated resources based on resource evaluation.

Orders are linked twice: once during order explosion and again during order assignment/pegging. This is necessary because, in some cases, reassignment based on FIFO is required after determining allocation results, such as minimum inventory constraints.

Differences Between Infinite Capacity Loading and Finite Capacity Leveling in Production Scheduling

In Indonesian manufacturing sites, production planning is quantity-based, focusing on how many units can be stacked per shift or per day relative to machine or line capacity. Given the long-standing practice of using Excel, where each cell represents a shift or day, this mindset is natural.

On the other hand, production schedulers operate based on time allocation, assigning work within available operating hours (e.g., 8:00–17:00). The key difference lies in whether objects are generated per shift/day or per manufacturing order.

MRP in production management systems generates daily objects based on BOM-referenced requirements calculations. Load calculations assume infinite capacity, with manual load leveling expected before execution. In contrast, production schedulers perform finite capacity leveling, reducing excess workload by rescheduling tasks earlier to meet deadlines.

Challenges of Operating MRP Systems in Indonesia

MRP determines order timing through backward allocation based on lead time. However, due to the effects of production lot sizes and lead-time adjustments, the linkage between manufacturing orders and their original requirements (MPS) becomes unclear.

Since production lot sizes and lead times vary by item, the complexity of linking MRP-generated production orders to customer orders leads to ad hoc instruction issuance by the production management department. As a result, production activities deviate from official work orders, causing inefficiencies.

Additionally, MRP’s core principle of ensuring accurate net requirements calculations prevents excess order generation. However, this sometimes makes order linkages less transparent to system operators. As forecast information overlaps with confirmed orders, careful system design is needed to avoid duplicate registrations.

Production Management Systems Supporting Manufacturing to Win in Indonesia's Global Competition

One of the key challenges facing Indonesia’s manufacturing industry is the increasing complexity of demand forecasting due to rising income levels, evolving consumer preferences through social media, and shorter product lifecycles. This results in smaller order quantities, frequent order changes, and a multiplier effect throughout the supply chain, complicating inventory control.

If inventory levels are too low, the risk of unfulfilled orders increases. Conversely, excessive inventory leads to higher holding costs, creating a trade-off problem. The solution to this issue lies in establishing a production planning system that considers the company's production capacity.

By integrating supply capacity into production planning, process flows become more efficient, reducing manufacturing lead times and in-process inventory. This, in turn, lowers inventory holding costs, increases cash flow availability, and improves overall business profitability.

Steps to Systematize Production Management and Turn Shop Floor Efforts into Corporate Competitiveness

Manufacturers have transitioned from focusing solely on stable deliveries of a few high-volume products to handling a greater variety of low-volume production runs. To manage this transition within limited production facilities, companies often subdivide production lines and introduce shared lines to maximize overall utilization. However, smaller lot sizes mean shorter lead times per process, increasing the frequency of mold changes, cleaning, and setup adjustments.

This variation in total manufacturing lead time from initial processing to final assembly makes accurate delivery date commitments challenging. Additionally, shop floor teams struggle to determine prioritization due to the lack of clear order tracking, leading to deteriorating on-time delivery performance. To avoid production line stoppages caused by material shortages, excessive material and work-in-progress inventory often accumulates.

A major challenge for factories in Indonesia is rising labor costs. One way to counter this is by improving efficiency (reducing labor hours), which enhances the value of time and internal resources throughout the supply chain—from procurement to production and shipping.

However, merely shortening production times is not a viable solution. If only upstream processes improve efficiency while downstream processes fail to keep up, work-in-progress inventory will pile up, resulting in local optimization rather than overall efficiency gains.

Challenges in Issuing Work Orders and Recording Production Data in Indonesian Manufacturing

Work orders play a crucial role in breaking down production plans into executable tasks on the shop floor. However, recording actual production data remains one of the biggest operational challenges and a key determinant of a production management system’s success.

To maintain a clear linkage between order intake and production planning, it is essential to implement the MRP (Material Requirements Planning) function within a production management system. Issuing work orders through Excel often leads to misalignment with shop floor operations and weakens the connection between orders and production activities.

Expanding Asprova’s Capabilities Through COM Interface

Asprova provides hooks to execute plugins at specific event timings. These plugin keys serve as access points for events, similar to WordPress’s do_action or apply_filters hooks.

The middleware and components involved in database access through ADO can be likened to the logistics process of shipping goods. An application program (Shipper) accesses RDBs, Excel, or text files (Consignee) via an intermediary OLE DB provider (Forwarder), while ADO (Cargo Carrier) handles the cumbersome data exchange process.

Impact of Production Scheduling on Work and Order Status

In Asprova, all tasks have fixed date and fixed quantity levels. When a task is manually moved, its fixed date level becomes 10, causing preceding and subsequent tasks to adjust accordingly during rescheduling. If actual production quantities are recorded, the fixed date level becomes 40, updating downstream task quantities accordingly.

Reflecting Constraints Such as Molds and Workers in Production Schedules

While capital investment in new equipment is not always feasible, adjusting worker assignments and hiring can be cost-effective and flexible ways to enhance resource utilization.

By setting mold change time as a primary resource’s setup time, internal setup (requiring machine stoppage) occurs. Conversely, setting mold change time as a secondary resource’s setup time allows external setup (without stopping machines).

Differences Between Production Scheduling for Assembly and Process-Based Manufacturing

In process-based manufacturing, continuous lines operate without idle time, and tasks in successive stages follow sequentially. If a tank is used for processing, it must not be assigned to other orders until the current batch has fully transferred to the next stage.

Optimizing Production Scheduling with Raw Material Inventory Constraints

Asprova prioritizes orders linked to purchased or pending procurement orders over those dependent on existing inventory. This ensures production schedules favor orders with available raw materials first.

Standard Cost Calculation in Asprova

Asprova’s item table allows cost element-based settings for raw material unit costs, labor rates, and allocation rates. However, as Asprova does not automatically calculate rates from fixed cost budgets, external calculations (e.g., Excel) must be imported into the system.

Leveraging AI for Advanced Production Scheduling in Indonesia

The Asprova Solver option incorporates genetic algorithms for machine learning-driven scheduling optimization. This advancement has significantly simplified the deployment of production schedulers, enabling non-IT consulting and sales firms to implement them effectively, marking a paradigm shift in the industry.