Science matters when selecting software
All executives and managers typically want predictive control over their business. Many of them also assume that installing new IT systems will provide that control. In far too many cases, however, that is an incorrect assumption.
As Dr. Mark Spearman, a leading innovator in applying advanced science to manufacturing, likes to say, "Implementing more and bigger IT systems will not make a bad supply chain good." The reason lies in the difference between science and technology.
Science is defined as knowledge. Technology is the application of knowledge. Without a working knowledge of the practical science governing operations performance, executives and managers tend to implement solutions that are not productive-or even worse-are counterproductive.
Science is necessary for the successful design and planning of business processes. Technology can be a key factor in the successful implementation of those processes.
Business executives are responsible for operations strategy-selecting, implementing, and controlling an operations logistics design that best meets a company’s marketing and financial goals. Operations Logistics refers to a framework of capacity (people and machines), response time, inventory and variability associated with the entire manufacturing supply chain, not solely to a description of distribution and transportation.
Executives responsible for operations strategy should understand the science behind their operations so they can evaluate and select the best operations logistics design for their business.
The Toyota Production System (TPS) is just one operations logistics design, albeit a very successful one. Executives and managers would do well to understand where the TPS design works well for their company and where it does not if their company is implementing or considering implementing a TPS-based approach-with or without software.
Having a Manufacturing Execution System (MES) that performs "real-time monitoring" can become a method for using technology to do little more than provide overwhelming floods of data.
Implementing an Advanced Planning and Scheduling (APS) or Advanced Planning and Optimization (APO) system that optimizes a schedule out of an MRP system is often a case of optimizing a bad schedule.
This is no Luddite argument to do away with information technology (IT). On the contrary, IT provides a critical productivity advantage when applied properly-and most companies have the IT systems they need to proactively control performance and profitability.
Too often, however, companies buy and install IT systems that don’t reflect their business models. It also often happens that companies are operating without basic business controls- for instance, unique product numbers-that are necessary for the successful use of most any IT system. In such cases, the company should change its business practices to get predictive business control and improve performance.
In far too many cases, however, this conflict between the IT business model and the company business model never gets resolved because there is no fundamental, scientific model for understanding and controlling the natural behavior of the business.
All processes or supply chains or value streams are composed of two fundamental elements: demand and transformation. Transformation can be broken down into two primitive elements, stocks and flows. So any supply chain can be understood as a structure of demands, stocks, and flows. This might seem like an over simplification like, "Eliminate Waste" or "Elevate the Constraint" or "Ship to Demand" but there is one critical difference: Demands, stocks, and flows can be described in a practical and comprehensive scientific framework which enables completely predictive control of the supply chain in question.
That doesn’t mean that one can predict the future. But given a level of variability-and it’s the business manager’s job to determine the level of variability (or risk) that is prudent to tolerate-logistical and financial performance can be predicted quite accurately.
Yet, all too often, companies employ their valuable IT resources to propagate unproductive or counterproductive management practices. It’s one thing to say that there will be enough inventory to respond to demand but quite another to determine exactly what that inventory level should be given constantly changing conditions.
Everyone would love 100 percent fill rates out of inventory but how much does that cost? Few know. So, many set their inventory levels to a nearly always suboptimal fixed period of supply, e.g. maintain inventory to cover two months of demand.
What if a company could reduce its fill rate to 80 percent and thereby greatly reduce its inventory investment requirements because it knows that, for the 20 percent fill rate misses, shipments occur no more than one day late 99 percent of the time?
A smart company would use its predictive knowledge of the behavior of its supply chain to competitive advantage by promising that anything ordered from stock will ship within two days, thereby having very satisfied customers and minimal cash tied up in inventory.
In the same vein, how many companies strive to achieve one piece flow without knowing how much the reduced cycle time will cost them in reduced throughput or poor customer service?
One company installed an automated assembly line achieving one piece flow and very low cycle times for 15 years. The line controls enabled detailed data tracking and manipulation of the various stations and line speeds. Unfortunately, they could never get the line to run close to full capacity. A practical understanding of the natural behavior of the system showed that the line design was starving the line of WIP. In two days time, a quantitative diagnosis was provided leading to an eventual 30 percent increase in throughput. Imagine if that line technology had been put to use for 15 years controlling 30 percent more throughput?
Companies spend billions of dollars on manufacturing and supply chain software solutions and the results are quite often dismal. Executives in operations, marketing, finance or IT should understand the practical science that describes the natural behavior of their operations systems before purchasing and implementing software "solutions."
Implementing an IT system is not a strategy; it’s a tactic. Use a practical, scientific, and comprehensive approach to design your business strategy and performance to ensure that IT implementations and upgrades actually improve performance and profitability. Executives should stop betting their business’ performance-and their careers-on the blind assumption that technology is an effective substitute for science.
Edward S. Pound is COO of Factory Physics Inc ., a management consulting company that provides a scientific framework, software, and training to optimize performance of manufacturing supply chains.