Without quality you don’t have a product. Successful direct mail service providers define, measure, audit and analyze quality. Though admittedly a daunting task, designing effective quality assurance systems and procedures is well worth the effort.
Before outsourcing your next direct mail job, ask existing and potential suppliers about their approach to quality assurance. You will make better outsourcing decisions.
Quality involves everyone within an organization, from production workers to company presidents. Quality is not what customers expect; it’s what manufacturers inspect. Accountability is the backbone of a good quality assurance program. If employees think their mistakes either don’t matter or will be caught by others, complacency and inattention to detail will result. Quality assurance is a balancing act. Unrealistic quality fanaticism kills output and profitability.
One challenge for the direct mail production industry is that every piece of mail is different. Therefore, we must question everything. If operators don’t see a barcode, they need to look at the job jacket instructions to see whether it is missing. If they see a barcode, they need to look at the job jacket to verify image positioning. Get the point?
Direct mail production occurs in a variable job shop manufacturing environment. This differs from our cousins in the printing and binding industries, where conformity is the goal and success depends on every piece being the same. In direct mail production, we deal with constantly changing parameters.
Quality assurance needs great information systems. A company’s choice of job definition/production work flow software is crucial. Consistent product quality depends on accurate job definitions and unambiguous communication among departments. High-volume mail service providers need integrated software systems that:
• Disseminate the right information to the right departments.
• Determine the shortest manufacturing critical paths.
• Integrate production schedules with other work inhouse.
• Track material flow.
• Monitor production on a real-time basis.
No two customers are alike. People give information in different ways, and account managers must get varying information into a standard, familiar format easily understood by everyone. Good software standardizes and disperses customer instructions to the right people throughout a manufacturing facility.
Operating procedures are also necessary, and one just prevented a major headache for us. We require employees to fan through boxes of inserts before loading pockets. One of our financial services clients offers different credit card interest rates to groups of people with different credit ratings. Because insert examination is an item on our standard production checklist, our diligent employee noticed that many boxes contained inserts with different interest rate offers. He stopped the job from being run and prevented a mailing that could have had dire consequences for our client.
Data audit. Your mailing services company should sample converted data before beginning production. These data audits usually involve random sampling by extracting records from the outgoing data file and examining them for conformity to customer expectations. Everything is checked, including data field content (ZIP code, customer identification, ordering history, etc.), uppercase and lowercase use, barcoding, data truncation and so forth.
If a problem is found, it’s important to determine where it occurred. Most mail houses start at the end of the data conversion process and work backward by examining the printing program, output file (post-data conversion) and input file (pre-data conversion) – in that order. For example, if an audit reveals a truncating problem, we may discover that a computer operator picked up data from byte positions 15 to 20 instead of from 15 to 30. Though fixing such a problem consumes additional resources, at least the mailing will not have been produced wrong.
Interval sampling. It’s hard to imagine quality assurance without good sampling and inspection procedures. Standards must be defined in all production departments and may differ, even within the same job. Some operations are susceptible to manufacturing problems and high spoilage rates while others run great for days, weeks and months. Understandably, problem-prone manufacturing areas require more frequent sample inspection. Customer requirements and job quantities also are important in determining appropriate sampling intervals.
Quality assurance needs machine operators armed with clearly written job jacket instructions regarding sampling frequency and product expectations. These operators are the front lines that pull samples, inspect the work and record their findings. The next quality assurance layer involves production monitors who constantly inspect recent sample pulls. The third layer involves gathering, examining, recording and storing samples. The last layer is the account manager who shoulders ultimate responsibility for customer expectation compliance.
Analysis: Seeing the big picture. Deconstructing problems and analyzing results is vital to manufacturing performance and customer satisfaction. Good companies analyze production data and determine whether overall quality is going in the right direction. Not only do they examine historical data, they consider information such as out-of-pocket rework costs, wasted production hours and percentage of on-time deliveries.
Analysis lets mailing service providers recognize when error patterns emerge. If errors are sporadically dispersed throughout a plant, they’re probably OK. However, detectable patterns of problems may indicate a systemic breakdown somewhere.
Plotting sample frequency graphs is an underused management tool. For example, if a mailing services company produces a million-piece job with imprinted unique barcodes, workers could scan each sample, determine when it was produced and plot the results in a graph. Large gaps between plotted points would indicate that someone didn’t do his job properly or possibly is even hiding something. If a reasonable shop floor data capture system exists, isolating and researching data anomalies is easy.
A culture of thinking. I agree with sales education speaker Zig Zigler when he says, “The only thing worse than training people and having them leave is not training them and having them stay.” My company is building a 1,500-square-foot training facility. Just as we do our best to define customer expectations and clearly communicate them throughout our company, we are using the same approach with employee training. New and existing employees will receive an appropriate amount of training for their job and experience levels. We expect this to improve employee retention and reduce production errors.
The two most dangerous types of employees are those who can’t follow instructions and those who can only follow instructions. It’s unrealistic to think that you or anyone else can write procedures that will cover all or even most manufacturing contingencies. The bottom line is that job shop manufacturers need people who can think.
We have a saying: “If you find your mistake, it’s not a mistake.” The logical corollary is: “If someone else finds your mistake, then it’s a mistake.” Quality assurance begins with each individual and radiates throughout an organization. It requires well-considered management procedures and information technologies. Quality assurance helps companies keep their promises. Isn’t this the point of being in business?