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A Structure for Manufacturing Planning and Control

A Structure for Manufacturing Planning and Control

We first start with the definition of planning, which is making decisions about future activities and events.

There are 5 levels of planning,

The first is strategic network planning, which is the high level you plan, like supply chain links and nodes, this is a sort of plan you did hardly (like yearly).

The questions in here are a big questions, like how many warehouses, manufacturing sites, what type of markets.

the next is sales and operations planning, where the plan is done product group level. This is more of a "how big our business gonna be?" like how much resource do we need to meet the demand.

after comes master production scheduling, where we actually check out the product on each product group. the official abbreviation of this is MPS. In here we actually figure out how much products need to be produced.

then there's this thing called order planning, where we actually "buy" stuff. Here we figure out the timing of the item coming, when should we "trigger orders" so we can actually do stuff.

The lowest level is production activity control, where we actually do the operation order.

Production Planning and Capacity Management

Production planning involves making decisions about future activities, ranging from short-term decisions about starting a new order in the next few hours to long-term decisions about hiring staff or investing in new machinery six months or more out.

This process can be viewed through a planning hierarchy, divided into material planning and capacity planning. This explanation focuses on capacity planning.

Capacity Planning Process:

  1. Sales & Operations Planning (S&OP): Provides the initial production plan, usually focusing on product groups or families.

  2. Resource Planning: Checks if existing resources can meet the production plan. This involves:

    • Using a resource bill to determine the resources needed for one average unit of each product group.
    • Comparing required resources against available resources (e.g., available labor hours).
    • Addressing any discrepancies by either finding extra capacity (overtime, subcontracting) or adjusting the production plan.

  3. Master Production Scheduling (MPS): Defines the required quantity of each finished product per period (typically weekly) over a horizon of 3-6 months.

  4. Rough-Cut Capacity Planning (RCCP): Verifies that critical resources (bottlenecks, labor, key materials) are available to support the MPS.

  5. Order Planning: Includes:

    • Material Requirements Planning (MRP): Uses the MPS and a bill of materials to determine the components needed and when they must be available.
    • Capacity Requirements Planning (CRP): Verifies sufficient capacity to meet the MRP plan, identifying any short-term discrepancies.

  6. Production Activity Control & Purchasing: Authorizes shop orders and purchase orders based on the finalized plans.

Capacity Calculations:

Capacity Requirements: Quantities in the production plan and MPS are translated into capacity requirements using operation times from the ERP system.

Capacity vs. Load:

Workshops manage the load by controlling input (new orders) and output (completed orders). Keeping input and output balanced maintains a stable load.

Addressing Capacity Discrepancies:

Four ways to reconcile available capacity and capacity requirements:

  1. Adjust Capacity Availability: Short-term (overtime, subcontracting) or long-term (hiring/layoffs, buying/selling machines, adding shifts).

  2. Reallocate Capacity: Shifting resources between departments or production groups.

  3. Adjust Capacity Requirements (Load): Changing order quantities or production schedules.

  4. Reallocate Load: Moving work between time periods.

Task Allocation Approaches:

  1. Infinite Loading: Assumes unlimited capacity, leading to potential overloads and underloads. Provides a realistic picture of the planned load, allowing planners to adjust it using the four principles above.

  2. Finite Loading: Considers capacity limits at each workstation. If insufficient capacity is available, orders are scheduled for different time periods. Ensures no overload conditions.

Most ERP systems and companies utilize infinite loading and manually adjust capacity and load using priority rules and the four principles outlined above.

  1. Demand Management:

    • Companies manage demand through two processes: forecasting and customer orders.
    • Customer orders represent known demand, while forecasts estimate future demand.

  2. Customer Order Process:

    • This process varies depending on the industry and product type.
    • It can involve simple retail purchases, custom-made products, or regular bulk orders.

  3. General Steps in the Customer Order Process: a. Inquiry/Request:

    • For standard products: Simple inquiries about price and delivery time.
    • For custom products: More complex discussions, possibly including quotations. b. Order Placement: Customer commits to purchasing. c. Order Confirmation: Supplier confirms details of the order. d. Order Fulfillment:
    • For stocked items: Picking from inventory.
    • For manufactured items: Creating work orders for production. e. Packing and Dispatch f. Delivery Notice: Informing the customer that the order is on its way.

  4. Determining Delivery Time:

    • Crucial for customer satisfaction and demand planning.
    • For standard products: Based on Available to Promise (ATP) calculations.

  5. Available to Promise (ATP):

    • A method to calculate how much of a product can be promised for delivery at a given time.
    • Takes into account current stock, incoming deliveries, and existing reservations.
    • Helps prevent over-promising and ensures accurate delivery dates.

  6. ATP Calculation Example:

    • Uses a table showing weekly data for: a. Customer order reservations b. Expected inbound deliveries c. Projected available balance (stock) d. Cumulative available to promise quantity
    • Demonstrates how to determine the earliest possible delivery date for a new order.

Forecast processes

In this section, we'll explore demand management with a specific focus on the forecasting process. Forecasting is a critical component used at various levels within the planning hierarchy and across different time horizons. For example, in the sales and operations plan, forecasts help determine the necessary capacity, decide what and where to produce, and manage current operational activities.

The primary goal here is to estimate future demand for the company's products, making forecasting a demand-focused activity. The magnitude of demand is crucial as it directly influences both the flow of materials and the production process. Accurate demand forecasts inform tactical and operational decisions, such as:

It's important to acknowledge that forecasts are inherently uncertain—a forecast is always wrong to some degree. However, understanding how forecasts are constructed can help manage this uncertainty effectively.

To illustrate this point, consider the game of golf. When a golfer hits the ball in an unintended direction that might pose a danger to others, they shout "fore," which serves as a warning to "watch out." Separately, the word "cast" means to throw out. Combining these two words gives us "forecast," suggesting that we should be cautious because something (in this case, information about the future) is being projected outward.

Accepting that forecasts may not be perfect, it's useful to recognize that they can vary in accuracy. Knowing the potential variability allows us to use forecasts more effectively. Generally:

For instance, meteorologists can predict tomorrow's temperature in London with reasonable accuracy. However, predicting the temperature on the same date next year is significantly more challenging. Similarly, if we ask for the average temperature over a day, week, month, or year, the forecast tends to be more accurate as the time unit increases. The same principles apply to product demand: aggregating demand into product groups or over longer time frames typically results in better forecasts.

To facilitate effective forecasting, access to accurate information and historical demand data is essential. However, compiling true historical demand isn't always straightforward. The data available in an Enterprise Resource Planning (ERP) system often includes delivery records and invoicing statistics, which may not fully represent actual demand. Discrepancies can occur due to:

Despite these limitations, companies often rely on delivery or invoicing statistics because they are the most readily available data. For practical reasons, historical demand is reported for forecasting purposes using time series—compiled data showing demand volume over successive periods.

When working with time series data for forecasting, it's important to identify underlying demand patterns within the sequence of data. Common demand patterns include:

  1. Stable Demand: Consistent demand over time with random variations around a constant level.
  2. Trend Changes: Demand that consistently increases or decreases over periods.
  3. Seasonal Variations: Regular fluctuations in demand at specific times of the year.
  4. Cyclical Changes: Long-term fluctuations often related to economic cycles, where revenues are higher during periods of economic growth and lower during downturns.

By recognizing and accounting for these patterns, businesses can develop more accurate forecasting models. This, in turn, leads to better decision-making in areas such as production planning, inventory management, and capacity utilization, ultimately improving operational efficiency and meeting customer demand more effectively.

Methods for Forecasting Demand

Forecasting demand is a critical component in business planning and inventory management. The methods used for forecasting demand can be broadly categorized into qualitative (judgmental) methods and quantitative (calculation) methods. Each approach has its unique applications, advantages, and limitations.

Qualitative Methods

Qualitative forecasting methods rely primarily on individual experiences, intuition, and well-considered personal assessments of future demand. These methods typically involve minimal or no formal calculations based on historical demand statistics or other data. Instead, they focus on subjective judgment and expert opinion.

Characteristics of Qualitative Methods:

Common Qualitative Techniques:

  1. Intuitive Estimates: Simple, instinct-based forecasts made by experienced personnel.
  2. Delphi Method: A structured communication technique that involves a panel of experts who respond to questionnaires in multiple rounds. After each round, a facilitator provides an anonymous summary of the experts' forecasts and the reasons behind their judgments. This process encourages experts to revise their opinions, leading to a convergence towards a more accurate forecast.
  3. Consumer Surveys: Systematic data gathering from external customers to assess their future interest and demand for products.

When to Use Qualitative Methods:

Quantitative Methods

Quantitative forecasting methods rely heavily on mathematical calculations and historical data to predict future demand. These methods use time series analysis of sales, consumption, or other demand-related data to generate forecasts.

Characteristics of Quantitative Methods:

Common Quantitative Techniques:

  1. Moving Average:

    • Description: Calculates the average demand over a specified number of past periods to predict future demand.
    • Formula:
      $$ F_{t+1} = \frac{D_t + D_{t-1} + \dots + D_{t-n+1}}{n}
      $$ Where Ft+1 is the forecast for the next period, Dt is the demand in the current period, and n is the number of periods considered.
    • Advantages: Smoothens out random variations in demand, providing more stable forecasts.
    • Considerations: The number of periods ($ n )shouldbechosenbasedonthedata′svariabilityandtheforecastinghorizon.Alargern$ increases stability but reduces sensitivity to trends.

  2. Exponential Smoothing:

    • Description: Applies decreasing weights to past demand data, giving more importance to the most recent observations.
    • Formula: $$ F_{t+1} = \alpha D_t + (1 - \alpha) F_t $$ Where $ \alpha $ is the smoothing constant (0 < α ≤ 1), Dt is the demand in the current period, and Ft is the forecast for the current period.
    • Advantages: More responsive to recent changes in demand compared to moving averages. Only the latest actual demand and the previous forecast need to be retained.
    • Considerations: The smoothing constant $ \alpha $ determines the weight given to recent demand. Common values for α range between 0.15 and 0.3. A higher α makes the forecast more sensitive to recent changes, while a lower $ \alpha $ results in a smoother forecast.

Example Applications:

Choosing the Right Method

The choice between qualitative and quantitative forecasting methods depends on several factors:

In practice, businesses often use a combination of both qualitative and quantitative methods to enhance the accuracy and reliability of their demand forecasts. Additionally, it's essential to continuously monitor and evaluate forecast performance to adjust methods and parameters as needed.