Viewpoint -- Achieve Superior On-Time Delivery Performance

This viewpoint is based on results of the Fourth Annual IndustryWeek Census of Manufacturers, a massive editorial research project that was designed to collect information about U.S. manufacturing trends, best practices, and specific manufacturing performance metrics. To that end, two questionnaires were developed: a mail survey that targeted plant-level manufacturing executives and a telephone survey aimed at corporate-level manufacturing executives. The research was conducted in association with PricewaterhouseCoopers. More than 3,300 survey responses were collected during the spring of 2000. The ability to deliver goods on time is becoming an increasingly critical success factor among manufacturing plants. For one, businesses are outsourcing more of their manufacturing activities to partners that can perform those activities at much lower costs. In order to achieve leverage or economies of scale in procurement, businesses are pruning their supplier bases. In both cases, businesses are choosing the most reliable suppliers -- those that can provide the best on-time delivery performance. Also, the thrust by supply-chain participants to achieve efficiency in the use of working capital dictates that inventories maintained to protect against untimely deliveries be drastically reduced, if not completely eliminated. This commentary discusses what it takes to achieve superior on-time delivery performance, both from a theoretical perspective and from a "best practices" perspective. The objective is to provide a systematic approach that will enable plant managers to identify which levers to push and which practices to implement as they pursue improvements to on-time delivery. On-Time Delivery Defined On-time delivery is strictly defined as the percentage of time that goods arrive on customers' receiving docks by the promised date. This definition implies that both manufacturing and logistics must be managed well. Because Fourth Annual IW Census data are focused on manufacturing-related metrics and practices, this paper's scope is limited to factors that are within the control of plant managers. This should not be taken to imply that logistics-related factors are not as important. In fact, it is advisable to take a holistic approach when dealing with a challenge such as improving on-time delivery. Additionally, this discussion is focused on the build-to-order environment. We defined delivery leaders as those plants that have an on-time delivery performance of at least 98%. Although the percentage of plants with an on-time delivery rate of at least 98% has risen since the IW Census of Manufacturers survey began four years ago, still only one-third of plant-survey participants in the Fourth Annual IW Census were able to achieve this level of performance. The increasing importance of on-time delivery can be deduced from the following findings from the Fourth Annual IW Census survey of corporate executives:

61% of respondents report that at least some of their manufacturing is being outsourced;

82% report that supplier rationalization is "somewhat critical" or "extremely critical" to achieving world-class manufacturing status;

92% report that JIT delivery by key suppliers is "somewhat critical" or "extremely critical" to achieving world-class manufacturing status. Key Success Factors From a manufacturing perspective, key success factors to achieving superior on-time-delivery service levels include having the following:

Short customer lead times, measured as the time from order entry to shipment;

A due-date quoting system that is based on actual plant status and capability;

A planning and scheduling system that can generate feasible production schedules;

Appropriate capacity flexibility; and

Tight control of all aspects of the manufacturing process. Short customer lead times are necessary because the longer the lead time, the further out into the future one must plan for. Such plans are subject to more uncertainties and consequently have greater chances of being in error. This means that on-time delivery tends to be higher for plants with shorter customer lead times. For plants that manufacture to order, the largest component of customer lead time is manufacturing cycle time. Reducing manufacturing cycle time requires a systematic effort that includes addressing sources of variability that contribute to long queue times (long and infrequent machine failures, long set-up times, frequent rework, operator errors), determining optimum move batch sizes, and possibly increasing capacity of highly utilized machines. Where possible, a shift to a pull system that does not allow work-in-process (WIP) inventory to increase to enormous levels should be considered. The second key success factor of superior on-time delivery performance is an integrated manufacturing information system that can provide a good estimate of the lead time for a customer order. The estimate should be based on actual and foreseen plant status during the lead-time period, as well as the plant's capability. The latter requires not just knowledge of the average plant output, but also the standard deviation of plant output. In general, the more variable the plant output the longer the quoted lead time for a given target service level. The third key success factor -- finite-capacity planning and scheduling systems -- ensures that committed due dates are feasible from a capacity-availability and a WIP-availability perspective. A main flaw of MRP systems is the assumption of infinite capacity. Finite-capacity scheduling systems address this flaw by letting schedulers know when extra capacity (e.g., overtime) will be needed or when work releases must be pushed back due to WIP or capacity unavailability. From a pure accounting perspective, we would like all resources to be fully utilized. However, from a superior customer-service perspective, we would like to have ample capacity to accommodate rush orders or to make up for lost time when unforeseen problems occur. Simulation packages are available that assist in deciding optimum capacities. Before embarking on costly capital investments, manufacturers first should reduce "capacity wastage" -- as indicated by measures such as low first-pass quality yield, high scrap and rework costs, and extended machine downtimes. The final key success factor of superior on-time delivery requires the close monitoring of the metric, as well as the identification, measurement, and control of all variables that could significantly affect it. Statistical process control (SPC) techniques that determine process capability and provide warning flags when processes are out of control are valuable tools in this regard. The table accompanying this article (see below) outlines the best practices (from among those on the IW Census survey) that correlate well with better on-time delivery. It should provide a useful guide for deciding which practices to implement to achieve the goal of superior on-time delivery performance. Conclusion Success in the networked economy requires superior on-time delivery performance. This white paper highlights the key success factors and the best practices that support them. It shows that the use of new technologies like advanced planning and scheduling are as important as more traditional tools, such as statistical process control. Finally the paper shows just how extensive is the scope of the effort to achieve superior on-time delivery performance and the need to involve the entire manufacturing organization. ***** Achieving Superior On-time Delivery Performance Best practices to achieve short customer lead times:

Cycle-time reduction

Error-proofing (poka-yoke)

Quick changeover techniques

Predictive or preventive maintenance

Bottleneck/constraint removal Best practices to achieve realistic due-date quoting system:

Advanced planning and scheduling

Statistical process control

Forecast demand-management software Best practice to achieve finite-capacity planning and scheduling system:

Advanced planning and scheduling Best practices to achieve capacity flexibility/capacity waste reduction:

Formal continuous-improvement program

Employee problem-solving teams Best practices to achieve manufacturing process control:

Statistical process control