Simulating the cause and effect relationship of print production work flow considering feedback mechanisms presents a comprehensive representation of the system’s complexity whereby interplay of associated variables and parameters are examined. Specifically, through developing alternative mental models, opportunities at both strategic and operational level can be exploited based on impact assessment of policies and decisions from simulation runs of different scenarios Keywords: System Dynamics, Print Production, Order Fulfillment
Worldwide manufacturing industries are in constant search for means of producing faster, cheaper, and better products or services(Uribe, 2008). In line with this, production takes a dominating and strategic logistic function in the process of order fulfillment (Helbing & Reichel, 1998). However, despite increasing technology in the printing industry added to widely available management tools, print productivity still has fallen further behind other manufacturing industries (Grant, 2003; Uribe, 2008; Zeng, Lin, Hoarau, & Dispoto, 2009).
In response to this, a system dynamics approach, modeling print production system, is provided to better understand the interrelationships between operation parameters and to uncover feedback loops influencing overall organization productivity. Analysis of the cause and effect relationships of print production variables exposes underlying reasons for lagging behind the average productivity levels. Specifically, simulating print production mental models provides an overview of system behavior whereby opportunities at both strategic and operational level are identified and exploited.
Commercial printing embodies a diverse, complex, and dynamic system(Uribe, 2008; Romano, Fawcett, & Soom, 2003). Its diversity is rooted to wide customized product offering possibilities. Particularly, commercial printers are involved in the manufacture of various custom printed products including flyers, posters, brochures, books, newsletters, invitations, packaging materials, and many more(Romano, Fawcett, & Soom, 2003). This make-to-order production system makes productivity improvements quite a challenge(Uribe, 2008).
Industry complexity, on the other end, transpires as diversity is complemented with mass production. More to this high customization high volume not-so-good match structure, complexity is intensified as it is classified as both a capital- and labor- intensive industry (Uribe, 2008; Zeng, Lin, Hoarau, & Dispoto, 2009). The highly diverse and dynamic job mix hence result to different print system configuration involving several work flows and varying combinations of equipment, resources, and business models (Kipphan, 2001; Uribe, 2008).
In view of that, this highly complex and dynamic nature of print production system makes it viable for a system dynamic approach investigation. In so applying systems thinking, further knowledge and comprehension enables management to not only create better strategies and decisions but more so to determine the effectiveness of the proposed solution immediately, without the need of waiting for results and actual application to the system which may be too costly and risky.
Generally, there is much to explore in the world of printing. Today, just like several other industries, it has been highly populated with printers that can produce good quality printing, and what differentiates a printer from another is basically a combination of the added value and services it is able to provide (Dana). Thousands of different mix and match of strategies have been designed and employed by commercial printers yet no single one is deemed the best and only solution (Uribe, 2008).
Assessment of various commercial printers’ current situation and problems point to disconnected or misaligned process requirements in producing print materials, whereby disintegrated equipment or process efficiencies are accounted distinctly. Adopting a system dynamics approach of exploration, focusing on the relationships interconnecting the complex system of printing rather than on individual areas of production, provides a holistic view whereby opportunities for improvement and better decision-making can be built.
Problem Statement and Research Objectives Productivity has been widely acknowledged as among any organization’s critical success factors. However, evaluating and understanding this measure entails a rather complex process(Kipphan, 2001). Beginning with the question of how to measure, Millet and Rosenberg, as cited in Uribe (2008), characterize productivity as the relation of throughput with a given set of resources, thus translating the ability to produce higher output with a fixed set of resources to be classified as more productive.
In this study, throughput through time is the accounted measure of productivity as per production backlog and turn-time estimates. Research indicate that the behavior of in-process production follows a cyclical pattern, increasing and decreasing accordingly with demand and turn time. Figure 1 shows the reference mode for the production backlog based on available literature. Figure 1: WIP in an Uncontrolled Process (Pritschow & Wiendahl, 1995) The high complexity of print production systems outlays difficulties in realizing high productivity which in turn contribute to unsatisfactory order fulfillment performance.
Being influenced by various interacting factors that form feedback mechanisms, straightforward linear solutions may not automatically result to observable higher productivity. Apparently, most decisions in printing companies are subject to miscalculations taking its roots from managers’ personal assessment on the basis of individual expertise. Basically, decision foundation builds on trial-and-error solution implementation originating from manager’ experiences(Zeng, Lin, Hoarau, & Dispoto, 2009).
Accordingly, the higher the complex¬ity of the system, the more likely decisions are to be errant. Incidentally, printing managers’ idea of a “perfect print shop,” can be highly diverse and conflict¬ing(Uribe, 2008). Hence, in order to address the rather low productivity evident in the printing industry, a holistic perspective analyzing the design and management of print production as an integrated system is simulated using a system dynamics approach to model the causation among interrelated variables and identify leverage points for designing organizational policies and decisions.
Generally, the modeling of print productivity in terms of system dynamics allow for a comprehensible depiction of complex relationships, thus subjecting underlying assumptions and relationships expressed in terms of functions and differential equations to be evaluated over time and scrutinized(Richmond, 2001). With this in mind, specific objectives of the study are identified as follows:
- create a computer simulation model that conceptualizes the dynamics of a generic commercial printing production system
- to provide an understanding of the current state of commercial printers so as to indicate a path for higher productivity
- identify policies and decisions towards opportunities for improvement through simulation runs of different strategy scenarios Study Scoping
In printing job orders, process is quite fascinating, having to involve high-speed machines, reams of paper, metal plates, and many other supplies. In the order acquisition phase, commercial printers receive an order from a customer and coordinate to determine job order specifications and project concept.
The following phase, consisting of the design stage, may be a joint venture for print buyers and providers whereby the design team prepare basic design for the print job based on the discussed concept. In the electronic production phase, all scanning, editing and electronic image processing is performed ensuring compatibility with printing mechanism. The customer shall then signal the printer to proceed with the printing of the project whereby film production is initiated if CTP technology is absent to the provider.
In this phase, films produced are put in the proper layout, and then blueprints and digital proofs are sent to customers for approval. Upon approval from the customer, the printing phase is commenced whereby negatives are converted to plates to be used in the offset printing machine. The machine is set up with the appropriate colors and paper and printing impression is performed. From printing, printed sheets undergo a finishing phase whereby all cutting and binding activities are performed.
Finally, the delivery phase includes all activities related to final inspection, packaging and delivery (Glykas, 2004; Zeng, Lin, Hoarau, & Dispoto, 2009). However, the scope of the study shall only include the film production, printing, and finishing phase of the production system.
Dynamic Hypotheses and Model Presentation
A mental model of a generic print production system is developed with causation hypotheses adopted from available researches and knowledgeable people from the industry. The mental model incorporated the interaction between sales variables, production backlog, customer satisfaction, and production capacity whereby sales orders are triggered by relative price, sales aggressiveness and customer satisfaction which in turn adds up to in-process production stock delivered as per capacity.
This time delay of fulfilling an order constrained by capacity influences customer satisfaction level specifically modeled by the effect of too much production backlog towards turn time and quality. Production Stock Flow The production stock is modeled as per the three basic production phases in offset printing, namely, the
- finishing or bindery (Mine).
Pre-press covers the stage wherein digital files are converted into film negatives to be used in creating metal plates for press run. Press includes the actual impressions of images or texts to papers.
Lastly, Postpress or Finishing comprises the binding of printed material or any finishing services that may be required of the job specification until it is packaged and made ready for delivery (Introduction to the Offset Printing Process). Print production operates under the assumption of a make-to-order policy. Hence, stock and flow struc¬ture are represented by orders coming into the production system, processed for a given time (production delay or turn time), then shipped to customers(Uribe, 2008). The production backlog represents the in-process orders waiting to be completed — Prepress + Press + Postpress.
It increases as sales order exceeds the output capability of the system and vice versa. It flows from the Prepress phase as plates are processed constrained by associated capacity and influenced by pressure or processing factor based on perceived WIP level. These processed plates then serve as an input to the Press stage. In the Press stage, there is an added outflow and inflow based on print rejects and returns that is unacceptable to standard quality and require reruns respectively. The Postpress stage likewise follows the same underlying mechanism with an option to outsource finishing requirements.
Figure 5: Production Stock Flow The rest of the model concentrates on the feedback structures that control the system. Order Inflow The overall production stock is affected by the rate of the order inflow coming in to the system at the prepress phase generated by sales orders from customers (Uribe, 2008). The assumption is that all orders undergoes the three print production stages as is often the case in most commercial printing services. In modeling production systems, it is very important to understand what affects the production stock including the inflow and how this rate can be managed.
Generally, order inflow can be affected by multiple variables. Refer to Figure 6 for significant factors considered by consumers in print service provider selection. However, inputs to the model focus on identified variables from literature as relevant to the problem and to the policies to be designed in the simulation. The first variable accounted in the model having direct impact on the sales order rate is the relative price, defined as the ratio of the price of the company to the market price.
Demand curve indicates that demand increases as price decreases. On the basis of this basic demand theory, if relative price is less than one, meaning price offered is less than market average, then the order rate increases, and vice versa. The second variable considered is the average order. To replace the complexity of the market demand, this is modeled as a constant indicating that a printing company that has been established in a market will have an average inflow of orders based on historical order patterns. The third variable is described as a switch variable on sales aggressiveness.
Lastly, customer satisfaction level is also considered to account for the top three ranking factors in printer selection:
- Turn Time.
Many more variables could be included in the model, yet current variables selected are assumed adequate for the model purpose and boundaries. Customer Satisfaction Level Customer satisfaction level generally creates the feedback mechanisms within the production system as per the effect of high level of satisfaction towards increasing sales order that likewise increases production backlog.
In particular, increasing backlog may be exceeding production capacity thereby increasing turn time as well as probability of rejects. This in turn decreases customer satisfaction which then creates the balancing loop. Fundamentally, the main reasons why print buyers abandon printers are due to quality issues and late deliveries (Merit, 1992; Uribe, 2008). However, the behavior of customer satisfaction is observed to follow the trend of turn time considerably more to quality as per simulation study.
Apparently, in-process level is cyclical in nature, depicting the principle of limits to growth. However, it is likewise observed that a limit to the growth of sales orders that serves as input to production backlog exist given that a printer can only accommodate orders pertinent to their capacity.
As capacity represent the controlling parameter of production outflow, increasing backlog is accumulated when demand exceeds capacity thereby increasing turn time and reducing customer satisfaction. This balances the upsurge in backlog as sales order is limited, thus creating the cyclical pattern.